9 Reasons Why You Should Keep Learning Machine Learning

Machine Learning is an application of Artificial Intelligence. It allows software applications to become accurate in predicting outcomes. Machine Learning focuses on the development of computer programs, and the primary aim is to allow computers to learn automatically without human intervention.

Google says “Machine Learning is the future,” and the future of Machine Learning is going to be very bright. As humans become more addicted to machines, we’re witnesses to a new revolution that’s taking over the world, and that is going to be the future of Machine Learning.

More online businesses are integrating machine learning into their operations, with the bigger and established ones trailblazing the revolution.

Machine learning has brought myriad opportunities and improved strategies to help business owners foster customer relationships and get more profit and conversions.

If you haven’t fully leveraged the power of machine learning in your business, let me give you five reasons why you should do so now.

1. Machine learning helps increase your efficiency.

Can you imagine buying from the grocery store without having to wait in line to pay for your goods?

If you can’t, then you’d better prepared because that is now a reality.

Amazon, for one, has applied machine learning to make grocery shopping ultra-efficient for your customers through computer vision, sensor fusion, and deep learning algorithms.

Using the Amazon Go app, customers only need to open it, scan the QR code when they enter, pick their items, and confidently walk out of the store.

Amazon Go detects the items they take out from the shelves, automatically adds them into their virtual carts, and charges the bill into their Amazon accounts when they leave.

Such is a classic example of how machine learning can increase the efficiency of your business operations and processes, and help your customers, too.

While you can use pre-built machine learning technologies, you can also master how to develop them yourself.

The concept may sound complicated, but through the right machine learning course, you can invest in building machine learning technologies that suit your specific business needs.

2. You can understand your customers better.

Since the launch of automation, businesses have embraced customer-centeredness.

If you want to maintain a competitive edge over other businesses, you need to know what your customers need and give it to them.

If you fail to do so, you can lose your potential customers to your competitors.

Here is where machine learning plays a critical role.

Machine learning can analyze and organize patterns, trends, and data about your customers’ demographic profiles, choices and preferences, behaviors, and others.

Machine learning can get these data from online tools and mechanisms that you use, such as emails collected from sign-ups.

Such ability by machine learning enables you to know and understand your customers more quickly.

For instance, for your advertising campaigns on Google Adsense or other channels to be effective, they should be deeply targeted according to the mentioned data.

The more accurately you can understand your customers and their needs and wants, the more sharply you can target your ads.

3. You can personalize your marketing campaigns.

When customers feel that your offers are accurately aligned with their preferences — or personalized — they are more likely to patronize your business.

The question now becomes, how do you personalize your campaigns and customer shopping experience?

The answer is by using machine learning to build them based on the data gathered and analyzed, just like Flybits does.

Flybits is a context-as-a-service product that helps businesses provide hyper-personalized digital content and experiences for your customers.

With its easy user interface, your digital marketers can easily and instantly access internal and external data through cloud synchronization.

Its real-time mobile analytics allow you to customize your content and campaigns for your customers according to their location, weather, and others.

What’s more, Flybits ensures your customers’ data are safe and kept confidential. Your customers retain full ownership over their data as well.

Personalized campaigns are influential in increasing your conversions and sales, and machine learning help you create them.

4. Machine learning recommends products to your customers.

In line with personalizing your campaigns, machine learning can recommend products similar to what you previously viewed, purchased, or added to your cart.

Amazon is one company that uses machine learning to recommend similar products.

Machine learning picks up on the features of the items you previously searched, viewed, or bought, and creates algorithms from those data.

Amazon then personalizes its recommendations to you by stating your name and showing similar items.

It can also recommend to you similar items that other customers viewed or bought.

Let’s say you clicked on some Omine grey loafer sneakers.

Machine learning notes the features of the shoes, such as color, size, and style, and then shows you what other customers also bought.

In this way, they leverage social proof and fear of missing out (FOMO) to entice you to consider buying what other customers also liked (besides widening your range of options).

That said, machine learning helps you improve your sales and conversions significantly.

5. Machine learning helps to detect fraud.

The convenience that online payment systems offer, especially through mobile applications, has attracted both customers and businesses to transact and purchase online.

However, transmitting money online has also attracted cybercriminals and given them opportunities to execute fraudulent attacks.

Some businesses have implemented different cybersecurity measures but find that they need more to stop fraud.

If you’re experiencing the same problem, there’s fortunate news for you. Machine learning can now help strengthen businesses’ fraud detection system.

For instance, PayPal uses machine-learning mechanisms to catch suspicious and shady transactions and separate them from legitimate ones.

Machine learning further assists you by inspecting specific attributes among your data and develop standards as the basis for examining each transaction.

Machine learning, therefore, helps prevent malicious transactions from taking place even before you can complete them.

6) Learning machine learning brings in better career opportunities

According to a Tractica Report, AI driven services were worth $1.9 billion in 2016 and are anticipated to rise to $2.7 billion by end of 2017 of which 23% of the revenue comes through machine learning technology.

A report from TMR mentions that MLaaS (Machine learning as a Service) is expected to grow from $1.07 billion in 2016 to $19.9 billion by end of 2025.

Machine learning is the shining star of the moment. With every industry looking to apply AI in their domain, studying machine learning opens world of opportunities to develop cutting edge machine learning applications in various verticals – such as cyber security, image recognition, medicine, or face recognition. With several machine learning companies on the verge of hiring skilled ML engineers, it is becoming the brain behind business intelligence. Netflix announced prize worth $1 million to the first individual who could enhance the accuracy of its recommendation ML algorithm by 10%. This is a clear evidence on how significant even a slight enhancement is in the accuracy of recommendation machine learning algorithms to improve the profitability of Netflix. Every customer- centric organization is looking to adopt machine learning technology and is the next big thing paving opportunities for IT professionals. Machine learning algorithms have become the darlings of business and consumers so if you want to put yourselves somewhere in the upper echelon of software engineers then this is the best time to learn ML.

7) Machine Learning Engineers earn a pretty penny

The cost of a top, world-class machine learning expert can be related to that of a top NFL quarterback prospect. According to SimplyHired.com, the average machine learning engineer salary is $142,000.An experienced machine learning engineer can earn up to $195, 752.

8)  Machine Learning Jobs on the rise

You need a special kind of person to build a hammer, but once you build it, you can give it to many people who will use it to build a house.”

The major hiring is happening in all top tech companies in search of those special kind of people (machine learning engineers) who can build a hammer (machine learning algorithms). The job market for machine learning engineers is not just hot but it’s sizzling.

According to the popular job portal Indeed, the number of open machine learning jobs have been steadily rising from 2014 to  the onset of 2016, from 60 job postings per million to more than 100. The number of job postings jumped up to 150 postings per million by end of 2016. Indeed job trends report also reveals that the number of machine learning engineer job postings outstrip the number of searches for machine learning jobs – 100 million searches vs. 150 job postings.

A recent survey on the Indian job market found that there is a requirements of 4000 machine learning engineers in Bengaluru alone.

Here is a snapshot of the total number of machine learning jobs in US for IT professionals as of November 13, 2017 –

Machine Learning Engineer Jobs Positions on Glassdoor.com – 12000+

9) Machine learning is linked directly to Data Science

Machine learning appears as a shadow of data science. Machine learning career endows you with two hats, one is for a machine learning engineer job and the other is for a data scientist job. Becoming competent in both the fields makes an individual a hot commodity to most of the employers. It means that you can analyse tons of data, extract value and glean insight from it, and later make use of that information to train a machine learning model to predict results. In many organizations, a machine learning engineer often partners with a data scientist for better synchronization of work products. Furthermore, data scientist has been voted the Sexiest Job of 21st Century so one can get started as a data scientist specializing in Machine Learning and become more desirable to employers.

If these reasons ring a bell then you might be interested to get your start in machine learning career right now.

Are you ready to learn machine learning and land your dream job at one of the top tech companies? Share your personal approach, knowledge, and strategy in the comments below. Everyone has a different take on machine learning, and we want to know your thoughts.

Future of Machine Learning

Machine Learning can be a competitive advantage to any company be it a top MNC or a startup as things that are currently being done manually will be done tomorrow by machines. Machine Learning revolution will stay with us for long and so will be the future of Machine Learning.

Resources to learn Machine Learning

Learn Artificial Intelligence for Beginners

Machine Learning In The Cloud With Azure Machine Learning

Machine Learning Python: Regression Modeling

MACHINE LEARNING with Microsoft AZURE

Machine Learning with AWS

Machine Learning from scratch through Python

Machine Learning and Training Neural Network in MATLAB

Practical Deep Learning: Image Search Engine

DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS

Machine Learning and Data Science using Python for Beginners

Machine Learning using R and Python

Machine Learning Adv: Support Vector Machines (SVM) Python

Conclusion

We have studied the future and the algorithms of Machine Learning. Along with that, we have studied its application, which will help you deal with real life. Furthermore, if you have any queries, feel free to ask in the comments section.

 

 

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Top 10 Technology Trends of 2018

2017 became the Year of Intelligence: the advance of technological achievements has triggered exciting and unexpected trends with wider impact horizons and very promising business prospects. This year we expect drastic exponential changes in every technological direction. Machine learning and artificial intelligence will transform the entire industries, making way for virtual helpers and a myriad of cases for automatization. The Internet of Things (IoT) will become more intelligent, uncovering a huge potential for smart homes and smart cities. A more efficient human-machine interaction will become established with the natural language replacing specific commands.

In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.

1. Artificial intelligence will reshape the business strategies

AI brings enormous changes to business operations, reshaping entire industries with the power of advanced technologies and software. Some companies now acknowledge the value of implementing the AI strategies for their business, and a major leap towards AI is on the way. Large companies with over 100,000 employees are more likely to implement the AI strategies, but for them, this process can be especially challenging. 2018 will be the year when the leading firms will incorporate AI applications into their strategic and organizational development. Additionally, there is a potential for algorithms marketplaces, where the best solutions created by engineers or companies can be shared, bought, and deployed for organizations’ individual use.

Brave ideas that used to be hard to believe, are becoming real. The constant development of machine learning and AI technologies will make every business become data-driven, and every industry smarter. After years of background work on prototypes and ideas, the new solutions will be breathtaking. Virtual assistance for patients, computational drug discovery, and genetics research give a glimpse of the amazing use cases in medicine. Many more applications for automation, robotization, and data management in different industries will bring significant changes. Healthcare, construction, banking, finance, manufacturing — every existing industry will be reshaped.

Top 10 Technology Trends of 2018a

2. Blockchain will reveal new opportunities in different industries

Everyone is now talking about blockchain, a revolutionary decentralized technology that stores and exchanges data for cryptocurrencies. It forms a distributed database with a digital register of the transactions and contracts. Blockchain stores an ever-growing list of ordered records called blocks, each containing a timestamp and a link to the previous block. Blockchain has impressive prospects in the field of digital transactions which will open new business opportunities in 2018.

This technology also uncovers many new possibilities with various applications in various other fields. Due to the growing role of social responsibility and security on the internet, the blockchain technologies are becoming increasingly relevant. In a system using blockchain, it is nearly impossible to forge any digital transactions, so the credibility of such systems will surely strengthen. This approach can become fundamental for disruptive digital business in enterprises and startups. Companies, previously operating offline, will be able to translate the processes into the digital environment completely.

Business needs to account for the blockchain risks and opportunities and analyze how this technology can influence the customer behavior. As the initial hype around blockchain in the financial services’ industry will slow down, we will see many more potential use cases for the government, healthcare, manufacturing, and other industries. For example, blockchain strongly influences the intellectual property management and opens new insights in protection from copyright infringement. Some websites like Blockai, Pixsy, Mediachain, and Proof of Existence intend to apply the blockchain technology for this purpose.

3. New approaches to privacy and security are coming

The technological development boosts the importance of data, so hacking techniques become ever more progressive. The increase in numbers of devices connected to the internet creates more data but also makes it more vulnerable and less protected. IoT gadgets are getting more popular and widely used, yet they remain extremely insecure in terms of the data privacy. Any large enterprises are constantly under threat of hack attacks, as it happened with Uber and Verizon in 2017.

Luckily, the solutions are achievable, and this year we will see great improvements in the data protection services. Machine learning will be the most significant security trend establishing a probabilistic, predictive approach to ensuring data security. Implementing techniques like behavioral analysis enables detecting and stopping an attack capable of bypassing the static protective systems. Blockchain brought our attention to a new technology called Zero Knowledge Proof which will further develop in 2018 enabling transactions that secure users’ privacy using mathematics. Another new approach to safety is known as CARTA (Continuous adaptive risk and trust assessment). It is based on a continuous evaluation of the potential risks and the degree of trust, adapting to every situation. This applies to all business participants: from the company’s developers to partners. Although our security is still vulnerable, there are promising solutions that can bring better privacy into our lives.

4. The Internet of Things will become intelligent

The intelligent things are everyday devices capable of smarter interactions with people and the environment. These things operate either semi-autonomously or autonomously in uncontrolled real-world conditions without the need for human intervention.

Intelligent things have been in a spotlight for several years, and with a continuous expansion and enhancement in 2018 they will influence another global trend — the Internet of Things.

A network of collaborative intelligent things will be created where multiple devices will work together developing IoT to its full potential. Connected to the global web and combined via wired and wireless communication channels, things will turn into a one big integrated system driving a major shift in the human-machine interaction. The fusion of artificial intelligence with the Internet of things brings about new amazing technologies to create smart homes and cities.

5. Deep learning will be faster and data collection better

Nowadays, deep learning faces certain challenges associated with the data collection and the complexity of the computations. Innovations in hardware are now being developed to speed up the deep learning experiments, e.g. the new GPUs with a greater number of cores and a different form of architecture. According to Marc Edgar, a Senior Information Scientist at the GE Research, deep training will shorten the development time of software solutions from several months to several days within the next 3-5 years. This will improve the functional characteristics, increase productivity, and reduce product costs.

Currently, most large firms realize the importance of data collection and its influence on the business effectiveness. In the coming year, companies will start using even more data, and the success will depend on the ability to combine the disparate data. In 2018, companies will collect customer data via CRM, ticket systems, BMP and DMP, as well as the omnichannel platforms. The popularity of collecting data on specialized sensors like LIDAR is also on the rise. Integrating the existing systems with all types of client data into a single information pool will definitely be on trend. Startups will continue to create new methods for gathering and using data, further reducing the costs.

6. AI will refine auto constructing and tuning of models

Top 10 Technology Trends of 2018b

Since Google’s launch of AutoML last year, use of the AI tools to accelerate the process of constructing and tuning models is rapidly gaining popularity. This new approach to AI development allows automating the design of machine learning models and enables the construction of models without human input with one AI becoming the architect of another.

This year, experts expect growth in popularity of the commercial AutoML packages and integration of AutoML into large machine learning platforms.

After AutoML, a computer vision algorithm called NASNet was built to recognize objects in video streams in real time. The “reinforcement learning” on NASNet implemented with AutoML can train the model without humans showing better results when compared to the algorithms that require human input.

These developments significantly broaden the horizons for machine learning and will completely reshape the approach to model construction in the next years.

7. The CDO role will grow extensively

The Chief Data Officers (CDOs) and other senior data professionals are getting more involved in the top management of large organizations changing their approach to data management. CDOs are the driving force behind the innovation and differentiation: they revolutionize the existing business models, improve the corporate communication with the target audience, and explore new opportunities to improve the business performance. Although this position is quite new, it is getting mainstream. According to Gartner, by 2019 CDO positions will be present in 90% of large organizations, but only half of them will actually succeed. Strong personal qualities, understanding of the responsibilities and potential obstacles are considered crucial to achieving success, yet there is another important step to unlock the full CDO’s potential. Firms should consider branching the IT department into the “I” and “T” separately, and CDOs should take the lead in the new group responsible for the information management.

8. The debates on ethics will flare up

As the AI industry makes significant progress in performing various tasks and actions in the everyday life, questions are raised regarding ethics, responsibilities, and human engagement. Who will be to blame if an artificial intelligence unit performs an illegal act? Do AI bots need any regulations? Will they be able to take over all the human jobs?

The first two questions assume that one day a bot will be legally recognized as a person and could take responsibility or be punished for their actions. Although this perspective is still years away, the debates around ethics are heating up already. Considering different possibilities, scientists are trying to find a compromise regarding the bots’ rights and responsibilities.

However, the possibility that robots will take all the workplaces is actually close to zero. Of course, the AI industry is developing extremely fast, but it is still pretty much in its infancy. 2018 promises to take the hype around this question down. Once we dive deeper into this subject, understand how to interact with the AI, and get used to it, the myth about robots taking over will surely be dispelled.

9. No more specific commands: growing of NLP

Use of chatbots in customer service became one of the leading trends of the outgoing year. In 2018 applications will require the ability to recognize the little nuances of our speech. The users want to get a response from their software by asking questions and giving commands in their natural language without thinking about the “right” way to ask. The development of NLP and its integration into computer programs will be one of the most exciting challenges of the 2018 year. We have high expectations about this.

What seems as a simple task for a human — to understand the tone of speech, the emotional coloring, and the double meaning — can be a difficult task for a computer accustomed to understanding the language of specific commands. These complex algorithms require many steps of predictions and computations, all occurring in the cloud within a split-second. With the help of NLP, people will be able to ask more questions, receive apposite answers and obtain better insights on their problems.

Top 10 Technology Trends of 2018c

10. Self-teaching AI will be more confident without the human data

Since the invention of the first artificial intelligence, the future in this field approaches faster than we expect. Experts were predicting that the AI would beat humans in the Go game by 2027. But it happened 10 years earlier — in 2017. It took only 40 days for the algorithm AlphaGo Zero to become the best Go player in the history of mankind. It was teaching itself without the input of any human data and developed strategies impossible for human players.

Next year the race for the creation of a developed, self-taught artificial intelligence will only continue. We look forward to the AI breakthrough in solving many human routines: decision-making, developing businesses and scientific models, recognition of objects, emotions, and speeches, and reinventing the customer experience. Also, we expect that AI will be able to cope with these tasks better, faster, and cheaper than people. The capability of algorithms for self-learning brings us closer to implementing the AI into many areas of human life.

Conclusions

To conclude, the year 2018 will bring great progress in technological innovations. We will witness faster and more accurate Machine Learning and AI applications and some new exciting developments. The exponential improvement of technologies like the Internet of Things, NLP, and self-teaching AI will change every business industry and our everyday lives. Although this can create a certain threat to the data security, the new approaches and solutions are continuously evolving. The changes will be streamlined and the outcomes are sure to be amazing.

The above list of trends is not definitive, please share your ideas about the main technology tendencies for this year in the comment section below. More

 

Best Machine Learning, Deep Learning, AI & IOS Courses Online

Statistics and Data Science in R

Best Machine Learning, Deep Learning, AI & IOS Courses Online2

Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples.

Let’s parse that.

  • Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings.
  • Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.
  • Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context.
    What you will learn
    • Harness R and R packages to read, process and visualize data
    • Understand linear regression and use it confidently to build models
    • Understand the intricacies of all the different data structures in R
    • Use Linear regression in R to overcome the difficulties of LINEST() in Excel
    • Draw inferences from data and support them using tests of significance
    • Use descriptive statistics to perform a quick study of some data and present results

    Click here To join us for more information, get in touch keep enhancing

     

     

Complete iOS 11 Machine Learning Masterclass

iOS 11 Machine Learningdf

If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging.

In this course, you will:

  • Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition
  • Develop an intuitive sense for using Machine Learning in your iOS apps
  • Create 7 projects from scratch in practical code-along tutorials
  • Find pre-trained ML models and make them ready to use in your iOS apps
  • Create your own custom models
  • Add Image Recognition capability to your apps
  • Integrate Live Video Camera Stream Object Recognition to your apps
  • Add Siri Voice speaking feature to your apps
  • Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit.
  • Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience
  • Get FREE unlimited hosting for one year
  • And more!

 

What you will learn
  • Build smart iOS 11 & Swift 4 apps using Machine Learning
  • Use trained ML models in your apps
  • Convert ML models to iOS ready models
  • Create your own ML models
  • Apply Object Prediction on pictures, videos, speech and text
  • Discover when and how to apply a smart sense to your apps

Click here To join us for more information, get in touch keep enhancing

 

Introduction to Data Science with Python

Data Science with Pythond.png

This course introduces Python programming as a way to have hands-on experience with Data Science. It starts with a few basic examples in Python before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

What you will learn
  • Writing simple Python scripts to do basic mathematical and logical operations
  • Loading structured data in a Python environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

Click here To join us for more information, get in touch keep enhancing

 

Introduction to Data Science with R

 

Data Science with Rgw.png

This course introduces R programming environment as a way to have hands-on experience with Data Science. It starts with a few basic examples in R before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

What you will learn
  • Writing simple R programs to do basic mathematical and logical operations
  • Loading structured data in a R environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

Click here To join us for more information, get in touch keep enhancing

 

Machine Learning In The Cloud With Azure Machine Learning

Best Machine Learning, Deep Learning, AI & IOS Courses Online1

The history of data science, machine learning, and artificial Intelligence is long, but it’s only recently that technology companies – both start-ups and tech giants across the globe have begun to get excited about it… Why? Because now it works. With the arrival of cloud computing and multi-core machines – we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data.

This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis.

You may have experienced various examples of Machine Learning in your daily life (in some cases without even realizing it). Take for example

Credit scoring, which helps the banks to decide whether to grant the loans to a particular customer or not – based on their credit history, historical loan applications, customers’ data and so on

Or the latest technological revolution from right from science fiction movies – the self-driving cars, which use Computer vision, image processing, and machine learning algorithms to learn from actual drivers’ behavior.

 

What you will learn
  • Learn about Azure Machine Learning
  • Learn about various machine learning algorithms supported by Azure Machine Learning
  • Learn how to build and run a machine learning experiment with real world datasets
  • Learn how to use classification machine learning algorithms
  • Learn how to use regression machine learning algorithms
  • Learn how to expose the Azure ML machine learning experiment as a web service or API
  • Learn how to integrate the Azure ML machine learning experiment API with a web application

Click here To join us for more information, get in touch keep enhancing

 

Best Machine Learning and Data Science Courses for 2018

Statistics and Data Science in R

Data Science in Rg

Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples.

Let’s parse that.

  • Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings.
  • Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.
  • Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context.

What’s Covered:

  • Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames
  • Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots
  • Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2
  • Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots
  • Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance

Click here continue to improve your Knowledge

 

Complete iOS 11 Machine Learning Masterclass

iOS 11 Machine Learningadd.jpg

If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging.

In this course, you will:

  • Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition
  • Develop an intuitive sense for using Machine Learning in your iOS apps
  • Create 7 projects from scratch in practical code-along tutorials
  • Find pre-trained ML models and make them ready to use in your iOS apps
  • Create your own custom models
  • Add Image Recognition capability to your apps
  • Integrate Live Video Camera Stream Object Recognition to your apps
  • Add Siri Voice speaking feature to your apps
  • Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit.
  • Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience
  • Get FREE unlimited hosting for one year
  • And more!

Click here continue to improve your Knowledge

 

Introduction to Data Science with Python

Data Science with Pythongs.png

This course introduces Python programming as a way to have hands-on experience with Data Science. It starts with a few basic examples in Python before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

Basic knowledge
  • This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs
What you will learn
  • Writing simple Python scripts to do basic mathematical and logical operations
  • Loading structured data in a Python environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

Click here continue to improve your Knowledge

 

Introduction to Data Science with R

Data Science with Rgfaw.jpg

This course introduces R programming environment as a way to have hands-on experience with Data Science. It starts with a few basic examples in R before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

Basic knowledge
  • This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs
What you will learn
  • Writing simple R programs to do basic mathematical and logical operations
  • Loading structured data in a R environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

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Machine Learning In The Cloud With Azure Machine Learning

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The history of data science, machine learning, and artificial Intelligence is long, but it’s only recently that technology companies – both start-ups and tech giants across the globe have begun to get excited about it… Why? Because now it works. With the arrival of cloud computing and multi-core machines – we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data.

This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis.

You may have experienced various examples of Machine Learning in your daily life (in some cases without even realizing it). Take for example

Credit scoring, which helps the banks to decide whether to grant the loans to a particular customer or not – based on their credit history, historical loan applications, customers’ data and so on

Or the latest technological revolution from right from science fiction movies – the self-driving cars, which use Computer vision, image processing, and machine learning algorithms to learn from actual drivers’ behavior.

Or Amazon’s recommendation engine which recommends products based on buying patterns of millions of consumers.

In all these examples, machine learning is used to build models from historical data, to forecast the future events with an acceptable level of reliability. This concept is known as Predictive analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling.

This progress in the field of machine learning is great news for the tech industry and humanity in general.

But the downside is that there aren’t enough data scientists or machine learning engineers who understand these complex topics.

Well, what if there was an easy to use a web service in the cloud – which could do most of the heavy lifting for us? What if scaled dynamically based on our data volume and velocity?

The answer – is new cloud service from Microsoft called Azure Machine Learning. Azure Machine Learning is a cloud-based data science and machine learning service which is easy to use and is robust and scalable like other Azure cloud services. It provides visual and collaborative tools to create a predictive model which will be ready-to-consume on web services without worrying about the hardware or the VMs which perform the calculations.

The advantage of Azure ML is that it provides a UI-based interface and pre-defined algorithms that can be used to create a training model. And it also supports various programming and scripting languages like R and Python.

In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset.

 

 

Machine Learning Classification Algorithms using MATLAB

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This course is for you If you are being fascinated by the field of Machine Learning?

Basic Course Description

This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Ouput Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the esesential ideas. The following are the course outlines.

  • Segment 1: Instructor and Course Introduction
  • Segment 2: MATLAB Crash Course
  • Segment 3: Grabbing and Importing Dataset
  • Segment 4: K-Nearest Neighbor
  • Segment 5: Naive Bayes
  • Segment 6: Decision Trees
  • Segment 7: Discriminant Analysis
  • Segment 8: Support Vector Machines
  • Segment 9: Error Correcting Ouput Codes
  • Segment 10: Classification with Ensembles
  • Segment 11: Validation Methods
  • Segment 12: Evaluating Performance

At the end of this course,

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  • You can confidently implement machine learning algorithms using MATLAB
  • You can perform meaningful analysis on the data

Student Testimonials!

This is the second Simpliv class on Matlab I’ve taken. Already, a couple important concepts have been discussed that weren’t discussed in the previous course. I’m glad the instructor is comparing Matlab to Excel, which is the tool I’ve been using and have been frustrated with. This course is a little more advanced than the previous course I took. As an engineer, I’m delighted it covers complex numbers, derivatives, and integrals. I’m also glad it covers the GUI creation. None of those topics were covered in the more basic introduction I first took.

Jeff Philips

This course is really good for a beginner. It will help you to start from ground up and move on to more complicated areas. Though it does not cover Matlab toolboxes etc, it is still a great basic introduction for the platform. I do recommend getting yourself enrolled for this course.Excellent course and instructor. You learn all the fundamentals of using MATLAB.

Lakmal Weerasinghe

Great information and not talking too much, basically he is very concise and so you cover a good amount of content quickly and without getting fed up!

Oamar Kanji

The course is amazing and covers so much. I love the updates. Course delivers more then advertised. Thank you!

Josh Nicassio

Student Testimonials! who are also instructors in the MATLAB category

“Concepts are explained very well, Keep it up Sir…!!!”

Engr Muhammad Absar Ul Haq instructor of course “Matlab keystone skills for Mathematics (Matrices & Arrays)”

Your Benefits and Advantages:

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  • You receive knowledge from a PhD. in Computer science (machine learning) with over 10 years of teaching and research experience, In addition to 15 years of programming experience and another decade of experience in using MATLAB
  • The instructor has 6 courses on Simpliv on MATLAB including a best seller course.
  • The overall rating in these courses are (4.5/5)
  • If you do not find the course useful, you are covered with 30 day money back guarantee, full refund, no questions asked!
  • You have lifetime access to the course
  • You have instant and free access to any updates i add to the course
  • You have access to all Questions and discussions initiated by other students
  • You will receive my support regarding any issues related to the course

Check out the curriculum and Freely available lectures for a quick insight.

It’s time to take Action!

Click the “Take This Course” button at the top right now!

Time is limited and Every second of every day is valuable.

I am excited to see you in the course!

Best Regrads,

Dr. Nouman Azam

Who is the target audience?

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  • Researchers, Entrepreneurs, Instructors and Teachers, College Students, Engineers, Programmers and Simulators
Basic knowledge
What you will learn
  • Use machines learning algorithms confidently in MALTAB
  • Build classification learning models and customize them based on the datasets
  • Compare the performance of diffferent classification algorithms
  • Learn the intuition behind classification algorithms
  • Create automatically generated reports for sharing your analysis results with friends and colleague

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The Oozie Orchestration Framework

The Oozie Orchestration Framework

Prerequisites: Working with Oozie requires some basic knowledge of the Hadoop eco-system and running MapReduce jobs

Taught by a team which includes 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with large-scale data processing jobs.

Oozie is like the formidable, yet super-efficient admin assistant who can get things done for you, if you know how to ask

Let’s parse that

formidable, yet super-efficient: Oozie is formidable because it is entirely written in XML, which is hard to debug when things go wrong. However, once you’ve figured out how to work with it, it’s like magic. Complex dependencies, managing a multitude of jobs at different time schedules, managing entire data pipelines are all made easy with Oozie

get things done for you: Oozie allows you to manage Hadoop jobs as well as Java programs, scripts and any other executable with the same basic set up. It manages your dependencies cleanly and logically.

if you know how to ask: Knowing the right configurations parameters which gets the job done, that is the key to mastering Oozie

What’s Covered:

computer and technology concept

  • Workflow Management: Workflow specifications, Action nodes, Control nodes, Global configuration, real examples with MapReduce and Shell actions which you can run and tweak
  • Time-based and data-based triggers for Workflows: Coordinator specification, Mimicing simple cron jobs, specifying time and data availability triggers for Workflows, dealing with backlog, running time-triggered and data-triggered coordinator actions
  • Data Pipelines using Bundles: Bundle specification, the kick-off time for bundles, running a bundle on Oozie
  • Using discussion forums
  • Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(
  • We’re super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.
  • The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.
  • We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

Oozie Orchestration Framework

  • Yep! Engineers, analysts and sysadmins who are interested in big data processing on Hadoop
  • Nope! Beginners who have no knowledge of the Hadoop eco-system
Basic knowledge
  • Students should have basic knowledge of the Hadoop eco-system and should be able to run MapReduce jobs on Hadoop
What you will learn

 

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Registration Link:
Simpliv Youtube Course & Tutorial :

Top 5 Recent Research Courses on Machine Learning | Simpliv

1. Statistics and Data Science in R

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Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples.

Let’s parse that.

  • Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings.
  • Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.
  • Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context.

What’s Covered:

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  • Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames
  • Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots
  • Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2
  • Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots
  • Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We’re super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

  • Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role
  • Yep! Engineers who want to understand basic statistics and lay a foundation for a career in Data Science
  • Yep! Analytics professionals who have mostly worked in Descriptive analytics and want to make the shift to being modelers or data scientists
  • Yep! Folks who’ve worked mostly with tools like Excel and want to learn how to use R for statistical analysis
Basic knowledge
  • No prerequisites : We start from basics and cover everything you need to know. We will be installing R and RStudio as part of the course and using it for most of the examples. Excel is used for one of the examples and basic knowledge of excel is assumed.
What you will learn
  • Harness R and R packages to read, process and visualize data
  • Understand linear regression and use it confidently to build models
  • Understand the intricacies of all the different data structures in R
  • Use Linear regression in R to overcome the difficulties of LINEST() in Excel
  • Draw inferences from data and support them using tests of significance
  • Use descriptive statistics to perform a quick study of some data and present results

 

Are you ready to join us to Keep Growing Up

 

2. Complete iOS 11 Machine Learning Masterclass

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If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging.

In this course, you will:

  • Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition
  • Develop an intuitive sense for using Machine Learning in your iOS apps
  • Create 7 projects from scratch in practical code-along tutorials
  • Find pre-trained ML models and make them ready to use in your iOS apps
  • Create your own custom models
  • Add Image Recognition capability to your apps
  • Integrate Live Video Camera Stream Object Recognition to your apps
  • Add Siri Voice speaking feature to your apps
  • Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit.
  • Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience
  • Get FREE unlimited hosting for one year
  • And more!

This course is also full of practical use cases and real-world challenges that allow you to practice what you’re learning. Are you tired of courses based on boring, over-used examples? Yes? Well then, you’re in a treat. We’ll tackle 5 real-world projects in this course so you can master topics such as image recognition, object recognition, and modifying existing trained ML models. You’ll also create an app that classifies flowers and another fun project inspired by Silicon Valley™ Jian Yang’s masterpiece: a Not-Hot Dog classifier app!

Why Machine Learning on iOS

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One of the hottest growing fields in technology today, Machine Learning is an excellent skill to boost your your career prospects and expand your professional tool kit. Many of Silicon Valley’s hottest companies are working to make Machine Learning an essential part of our daily lives. Self-driving cars are just around the corner with millions of miles of successful training. IBM’s Watson can diagnose patients more effectively than highly-trained physicians. AlphaGo, Google DeepMind’s computer, can beat the world master of the game Go, a game where it was thought only human intuition could excel.

In 2017, Apple has made Machine Learning available in iOS 11 so that anyone can build smart apps and games for iPhones, iPads, Apple Watches and Apple TVs. Nowadays, apps and games that do not have an ML layer will not be appealing to users. Whether you wish to change careers or create a second stream of income, Machine Learning is a highly lucrative skill that can give you an amazing sense of gratification when you can apply it to your mobile apps and games.

Why This Course Is Different

Machine Learning is very broad and complex; to navigate this maze, you need a clear and global vision of the field. Too many tutorials just bombard you with the theory, math, and coding. In this course, each section focuses on distinct use cases and real projects so that your learning experience is best structured for mastery.

This course brings my teaching experience and technical know-how to you. I’ve taught programming for over 10 years, and I’m also a veteran iOS developer with hands-on experience making top-ranked apps. For each project, we will write up the code line by line to create it from scratch. This way you can follow along and understand exactly what each line means and how to code comes together. Once you go through the hands-on coding exercises, you will see for yourself how much of a game-changing experience this course is.

As an educator, I also want you to succeed. I’ve put together a team of professionals to help you master the material. Whenever you ask a question, you will get a response from my team within 48 hours. No matter how complex your question, we will be there–because we feel a personal responsibility in being fully committed to our students.

By the end of the course, you will confidently understand the tools and techniques of Machine Learning for iOS on an instinctive level.

Don’t be the one to get left behind. Get started today and join millions of people taking part in the Machine Learning revolution.

topics: ios 11 swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios11 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios 11 swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios11 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios 11 swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios11 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios 11 swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios11 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios 11 swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios11 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection

Who is the target audience?

  • People with a basic foundation in iOS programming who would like to discover Machine Learning, a branch of Artificial Intelligence
  • People who want to pursue a career combining app development and Machine Learning to become a hybrid iOS developer and ML expert
  • Developers who would like to apply their Machine Learning skills by creating practical mobile apps
  • Entrepreneurs who want to leverage the exponential technology of Machine Learning to create added value to their business could also take this course. However, this course does assume that you are familiar with basic programming concepts such as object oriented programming, variables, methods, classes, and conditional statements
Basic knowledge
  • Basic understanding of programming
  • Have access to a MAC computer or MACinCloud website
What you will learn
  • Build smart iOS 11 & Swift 4 apps using Machine Learning
  • Use trained ML models in your apps
  • Convert ML models to iOS ready models
  • Create your own ML models
  • Apply Object Prediction on pictures, videos, speech and text
  • Discover when and how to apply a smart sense to your apps

 

Are you ready to join us to Keep Growing Up

 

3. Introduction to Data Science with Python

Machine Learning

This course introduces Python programming as a way to have hands-on experience with Data Science. It starts with a few basic examples in Python before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

Basic knowledge
  • This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs
What you will learn
  • Writing simple Python scripts to do basic mathematical and logical operations
  • Loading structured data in a Python environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

 

Are you ready to join us to Keep Growing Up

 

4. Introduction to Data Science with R

Machine learning made easy5

This course introduces R programming environment as a way to have hands-on experience with Data Science. It starts with a few basic examples in R before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

Basic knowledge
  • This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs
What you will learn
  • Writing simple R programs to do basic mathematical and logical operations
  • Loading structured data in a R environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

 

Are you ready to join us to Keep Growing Up

5. Machine Learning In The Cloud With Azure Machine Learning

Machine Learning6.png

The history of data science, machine learning, and artificial Intelligence is long, but it’s only recently that technology companies – both start-ups and tech giants across the globe have begun to get excited about it… Why? Because now it works. With the arrival of cloud computing and multi-core machines – we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data.

This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis.

You may have experienced various examples of Machine Learning in your daily life (in some cases without even realizing it). Take for example

Credit scoring, which helps the banks to decide whether to grant the loans to a particular customer or not – based on their credit history, historical loan applications, customers’ data and so on

Or the latest technological revolution from right from science fiction movies – the self-driving cars, which use Computer vision, image processing, and machine learning algorithms to learn from actual drivers’ behavior.

Or Amazon’s recommendation engine which recommends products based on buying patterns of millions of consumers.

In all these examples, machine learning is used to build models from historical data, to forecast the future events with an acceptable level of reliability. This concept is known as Predictive analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling.

Machine Learning4

This progress in the field of machine learning is great news for the tech industry and humanity in general.

But the downside is that there aren’t enough data scientists or machine learning engineers who understand these complex topics.

Well, what if there was an easy to use a web service in the cloud – which could do most of the heavy lifting for us? What if scaled dynamically based on our data volume and velocity?

The answer – is new cloud service from Microsoft called Azure Machine Learning. Azure Machine Learning is a cloud-based data science and machine learning service which is easy to use and is robust and scalable like other Azure cloud services. It provides visual and collaborative tools to create a predictive model which will be ready-to-consume on web services without worrying about the hardware or the VMs which perform the calculations.

The advantage of Azure ML is that it provides a UI-based interface and pre-defined algorithms that can be used to create a training model. And it also supports various programming and scripting languages like R and Python.

In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset.

Do you know what it takes to build sophisticated machine learning models in the cloud?

How to expose these models in the form of web services?

Machine learning made easy4

Do you know how you can share your machine learning models with non-technical knowledge workers and hand them the power of data analysis?

These are some of the fundamental problems data scientists and engineers struggle with on a daily basis.

This course teaches you how to design, deploy, configure and manage your machine learning models with Azure Machine Learning. The course will start with an introduction to the Azure ML toolset and features provided by it and then dive deeper into building some machine learning models based on some real-world problems.

If you’re serious about building scalable, flexible and powerful machine learning models in the cloud, then this course is for you.

These data science skills are in great demand, but there’s no easy way to acquire this knowledge. Rather than rely on hit and trial method, this course will provide you with all the information you need to get started with your machine learning projects.

Startups and technology companies pay big bucks for experience and skills in these technologies They demand data science and cloud engineers make sense of their dormant data collected on their servers – and in turn, you can demand top dollar for your abilities.

You may be a data science veteran or an enthusiast – if you invest your time and bring an eagerness to learn, we guarantee you real, actionable education at a fraction of the cost you can demand as a data science engineer or a consultant. We are confident your investment will come back to you many-fold in no time.

So, if you’re ready to make a change and learn how to build some cool machine learning models in the cloud, click the “Add to Cart” button below.

Look, if you’re serious about becoming an expert data engineer and generating a greater income for you and your family, it’s time to take action.

Imagine getting that promotion which you’ve been promised for the last two presidential terms. Imagine getting chased by recruiters looking for skilled and experienced engineers by companies that are desperately seeking help. We call those good problems to have.

Imagine getting a massive bump in your income because of your newly-acquired, in-demand skills.

That’s what we want for you. If that’s what you want for yourself, click the “Add to Cart” button below and get started today with our “Machine Learning In The Cloud With Azure Machine Learning”.

Let’s do this together!

Who is the target audience?

  • Data science enthusiasts
  • Software and IT engineers
  • Statisticians
  • Cloud engineers
  • Software architects
  • Technical and non-technical tech founders
Basic knowledge
  • Access to a free or paid account for Azure
  • Basic knowledge about cloud computing and data science
  • Basic knowledge about IT infrastructure setup
  • Desire to learn something new and continuous improvement
What you will learn
  • Learn about Azure Machine Learning
  • Learn about various machine learning algorithms supported by Azure Machine Learning
  • Learn how to build and run a machine learning experiment with real world datasets
  • Learn how to use classification machine learning algorithms
  • Learn how to use regression machine learning algorithms
  • Learn how to expose the Azure ML machine learning experiment as a web service or API
  • Learn how to integrate the Azure ML machine learning experiment API with a web application

 

Are you ready to join us to Keep Growing Up

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