5 Best Python Online Courses on Simpliv

Learn Python Programming

Learn Python.jpg

A Note on the Python versions 2 and 3: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible.

What’s Covered:

  • Introductory Python: Functional language constructs; Python syntax; Lists, dictionaries, functions and function objects; Lambda functions; iterators, exceptions and file-handling
  • Database operations: Just as much database knowledge as you need to do data manipulation in Python
  • Auto-generating spreadsheets: Kill the drudgery of reporting tasks with xlsxwriter; automated reports that combine database operations with spreadsheet auto-generation
  • Text processing and NLP: Python’s powerful tools for text processing – nltk and others.
  • Website scraping using Beautiful Soup: Scrapers for the New York Times and Washington Post
  • Machine Learning : Use sk-learn to apply machine learning techniques like KMeans clustering
  • Hundreds of lines of code with hundreds of lines of comments
  • Drill #1: Download a zip file from the National Stock Exchange of India; unzip and process to find the 3 most actively traded securities for the day
  • Drill #2: Store stock-exchange time-series data for 3 years in a database. On-demand, generate a report with a time-series for a given stock ticker
  • Drill #3: Scrape a news article URL and auto-summarize into 3 sentences
  • Drill #4: Scrape newspapers and a blog and apply several machine learning techniques – classification and clustering to these

How We Need to Keep Grow Up

 

Spark for Data Science with Python

Data Science in R

Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.

Get your data to fly using Spark for analytics, machine learning and data science

Let’s parse that.

  • What’s Spark? If you are an analyst or a data scientist, you’re used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.
  • Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.
  • Machine Learning and Data Science : Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.

How We Need to Keep Grow Up

 

Machine Learning, NLP & Python-Cut to the Chase

Machine Learningawf.jpg

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

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 down-to-earth, shy but confident take on machine learning techniques that you can put to work today

Let’s parse that.

The course is down-to-earth : it makes everything as simple as possible – but not simpler

The course is shy but confident : It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

You can put ML to work today : If Machine Learning is a car, this car will have you driving today. It won’t tell you what the carburetor is.

The course is very visual : most of the techniques are explained with the help of animations to help you understand better.

This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python.

The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art – all shown by studies to improve cognition and recall.

 

How We Need to Keep Grow Up

 

Image Processing Applications on Raspberry Pi – From Scratch

Image Processing Applications on Raspberry Pi - From Scratch

Image Processing Applications on Raspberry Pi is a beginner course on the newly launched Raspberry Pi 3 and is fully compatible with Raspberry Pi 2 and Raspberry Pi Zero.

The course is ideal for those who are new to the Raspberry Pi and want to explore more about it.

You will learn the components of Raspberry Pi, connecting components to Raspberry Pi, installation of NOOBS operating system, basic Linux commands, Python programming and building Image Processing applications on Raspberry Pi.

This course will take beginners without any coding skills to a level where they can write their own programs.

Basics of Python programming language are well covered in the course.

Building Image Processing applications are taught in the simplest manner which is easy to understand.

Users can quickly learn hardware assembly and coding in Python programming for building Image Processing applications. By the end of this course, users will have enough knowledge about Raspberry Pi, its components, basic Python programming, and execution of Image Processing applications in the real time scenario.

The course is taught by an expert team of Electronics and Computer Science engineers, having PhD and Postdoctoral research experience in Image Processing.

Anyone can take this course. No engineering knowledge is expected. Tutor has explained all required engineering concepts in the simplest manner.

The course will enable you to independently build Image Processing applications using Raspberry Pi.

This course is the easiest way to learn and become familiar with the Raspberry Pi platform.

By the end of this course, users will build Image Processing applications which includes scaling and flipping images, varying brightness of images, perform bit-wise operations on images, blurring and sharpening images, thresholding, erosion and dilation, edge detection, image segmentation. User will also be able to build real-world Image Processing applications which includes real-time human face eyes nose detection, detecting cars in video, real-time object detection, human face recognition and many more.

The course provides complete code for all Image Processing applications which are compatible on Raspberry Pi 3/2/Zero.

 

How We Need to Keep Grow Up

 

Python for Beginners 2017

Machine Learning, NLP & Pythonz.jpg

See why over 350,000 Simpliv members learn coding from Mark Lassoff and LearnToProgram.tv!

Few programming languages provide you with the flexibility and pure power of Python.

If you’re becoming a professional developer, or are early in your development career, adding the Python skill set isn’t just a resume embellishment. It’s an empowering language that will allow you to write procedural code in many types of environments and for many uses.

Python is commonly used for server side programming for complex web applications or as a middle tier language providing web services or a communication layer with larger ecommerce systems. That being said, it’s also a great language for beginners. The clear syntax makes it very easy to learn, and the powerful libraries make all types of programming possible. There are libraries for everything from games and graphics to complex mathematics to network and embedded programming.

 

How We Need to Keep Grow Up

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Learn Python Programming

 

#cyber #onlinesecurityA Note on the Python versions 2 and 3: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible.

What’s Covered:

  • Introductory Python: Functional language constructs; Python syntax; Lists, dictionaries, functions and function objects; Lambda functions; iterators, exceptions and file-handling
  • Database operations: Just as much database knowledge as you need to do data manipulation in Python
  • Auto-generating spreadsheets: Kill the drudgery of reporting tasks with xlsxwriter; automated reports that combine database operations with spreadsheet auto-generation
  • Text processing and NLP: Python’s powerful tools for text processing – nltk and others.
  • Website scraping using Beautiful Soup: Scrapers for the New York Times and Washington Post
  • Machine Learning : Use sk-learn to apply machine learning techniques like KMeans clustering
  • Hundreds of lines of code with hundreds of lines of comments
  • Drill #1: Download a zip file from the National Stock Exchange of India; unzip and process to find the 3 most actively traded securities for the day
  • Drill #2: Store stock-exchange time-series data for 3 years in a database. On-demand, generate a report with a time-series for a given stock ticker
  • Drill #3: Scrape a news article URL and auto-summarize into 3 sentences
  • Drill #4: Scrape newspapers and a blog and apply several machine learning techniques – classification and clustering to these

Using discussion forums

Learn Python Programming qw.jpg

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?

Pythonhsh.jpg

  • Yep! Folks with zero programming experience looking to learn a new skill
  • Machine Learning and Language Processing folks looking to apply concepts in a full-fledged programming language
  • Yep! Computer Science students or software engineers with no experience in Java, but experience in Python, C++ or even C#. You might need to skip over some bits, but in general the class will still have new learning to offer you 🙂
Basic knowledge
  • No prior programming experience is needed 🙂
  • The course will use a Python IDE (integrated development environment) called iPython from Anaconda. We will go through a step-by-step procedure on downloading and installing this IDE.
What you will learn
  • Pick up programming even if you have NO programming experience at all
  • Write Python programs of moderate complexity
  • Perform complicated text processing – splitting articles into sentences and words and doing things with them
  • Work with files, including creating Excel spreadsheets and working with zip files
  • Apply simple machine learning and natural language processing concepts such as classification, clustering and summarization
  • Understand Object-Oriented Programming in a Python context

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Basic Python Interview Questions and Answers

Q1. What are the key features of Python?

Ans: These are the few key features of Python:

  • Python is an interpreted language. That means that, unlike languages like C and its variants, Python does not need to be compiled before it is run. Other interpreted languages include PHP and Ruby.
  • Python is dynamically typed, this means that you don’t need to state the types of variables when you declare them or anything like that. You can do things like x=111 and then x="I'm a string" without error
  • Python is well suited to object orientated programming in that it allows the definition of classes along with composition and inheritance. Python does not have access specifiers (like C++’s publicprivate), the justification for this point is given as “we are all adults here”
  • In Python, functions are first-class objects. This means that they can be assigned to variables, returned from other functions and passed into functions. Classes are also first class objects
  • Writing Python code is quick but running it is often slower than compiled languages. Fortunately,Python allows the inclusion of C based extensions so bottlenecks can be optimized away and often are. The numpy package is a good example of this, it’s really quite quick because a lot of the number crunching it does isn’t actually done by Python
  • Python finds use in many spheres – web applications, automation, scientific modelling, big data applications and many more. It’s also often used as “glue” code to get other languages and components to play nice.

Q2. What is the difference between deep and shallow copy?

Ans: Shallow copy is used when a new instance type gets created and it keeps the values that are copied in the new instance. Shallow copy is used to copy the reference pointers just like it copies the values. These references point to the original objects and the changes made in any member of the class will also affect the original copy of it. Shallow copy allows faster execution of the program and it depends on the size of the data that is used.

Deep copy is used to store the values that are already copied. Deep copy doesn’t copy the reference pointers to the objects. It makes the reference to an object and the new object that is pointed by some other object gets stored. The changes made in the original copy won’t affect any other copy that uses the object. Deep copy makes execution of the program slower due to making certain copies for each object that is been called.

Q3. What is the difference between list and tuples?

Ans: Lists are mutable i.e they can be edited. Syntax: list_1 = [10, ‘Chelsea’, 20]

Tuples are immutable (tuples are lists which can’t be edited). Syntax: tup_1 = (10, ‘Chelsea’ , 20)

Q4. How is Multithreading achieved in Python?

Ans:

  1. Python has a multi-threading package but if you want to multi-thread to speed your code up.
  2. Python has a construct called the Global Interpreter Lock (GIL). The GIL makes sure that only one of your ‘threads’ can execute at any one time. A thread acquires the GIL, does a little work, then passes the GIL onto the next thread.
  3. This happens very quickly so to the human eye it may seem like your threads are executing in parallel, but they are really just taking turns using the same CPU core.
  4. All this GIL passing adds overhead to execution. This means that if you want to make your code run faster then using the threading package often isn’t a good idea.

Q5. How can the ternary operators be used in python?

Ans: The Ternary operator is the operator that is used to show the conditional statements. This consists of the true or false values with a statement that has to be evaluated for it.

Syntax:

The Ternary operator will be given as:
[on_true] if [expression] else [on_false]x, y = 25, 50big = x if x < y else y

Example:

The expression gets evaluated like if x<y else y, in this case if x<y is true then the value is returned as big=x and if it is incorrect then big=y will be sent as a result.

GET STARTED WITH PYTHON

Q6. How is memory managed in Python?

Ans:

  1. Python memory is managed by Python private heap space. All Python objects and data structures are located in a private heap. The programmer does not have an access to this private heap and interpreter takes care of this Python private heap.
  2. The allocation of Python heap space for Python objects is done by Python memory manager. The core API gives access to some tools for the programmer to code.
  3. Python also have an inbuilt garbage collector, which recycle all the unused memory and frees the memory and makes it available to the heap space.

Q7. Explain Inheritance in Python with an example.

Ans: Inheritance allows One class to gain all the members(say attributes and methods) of another class. Inheritance provides code reusability, makes it easier to create and maintain an application. The class from which we are inheriting is called super-class and the class that is inherited is called a derived / child class.

They are different types of inheritance supported by Python:

  1. Single Inheritance – where a derived class acquires the members of a single super class.
  2. Multi-level inheritance – a derived class d1 in inherited from base class base1, and d2 is inherited from base2.
  3. Hierarchical inheritance – from one base class you can inherit any number of child classes
  4. Multiple inheritance – a derived class is inherited from more than one base class.

Q8. Explain what Flask is and its benefits?

Ans: Flask is a web micro framework for Python based on “Werkzeug, Jinja2 and good intentions” BSD license. Werkzeug and Jinja2 are two of its dependencies. This means it will have little to no dependencies on external libraries.  It makes the framework light while there is little dependency to update and less security bugs.

A session basically allows you to remember information from one request to another. In a flask, a session uses a signed cookie so the user can look at the session contents and modify. The user can modify the session if only it has the secret key Flask.secret_key.

Q9. What is the usage of help() and dir() function in Python?

Ans: Help() and dir() both functions are accessible from the Python interpreter and used for viewing a consolidated dump of built-in functions.

  1. Help() function: The help() function is used to display the documentation string and also facilitates you to see the help related to modules, keywords, attributes, etc.
  2. Dir() function: The dir() function is used to display the defined symbols.

Q10. Whenever Python exits, why isn’t all the memory de-allocated?

Ans:

  1. Whenever Python exits, especially those Python modules which are having circular references to other objects or the objects that are referenced from the global namespaces are not always de-allocated or freed.
  2. It is impossible to de-allocate those portions of memory that are reserved by the C library.
  3. On exit, because of having its own efficient clean up mechanism, Python would try to de-allocate/destroy every other object.

Q11. What is dictionary in Python?

Ans: The built-in datatypes in Python is called dictionary. It defines one-to-one relationship between keys and values. Dictionaries contain pair of keys and their corresponding values. Dictionaries are indexed by keys.

Let’s take an example:

The following example contains some keys. Country, Capital & PM. Their corresponding values are India, Delhi and Modi respectively.

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dict={'Country':'India','Capital':'Delhi','PM':'Modi'}
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print dict[Country]
India
1
print dict[Capital]
Delhi
1
print dict[PM]
Modi

Q12. What is monkey patching in Python?

Ans: In Python, the term monkey patch only refers to dynamic modifications of a class or module at run-time.

Consider the below example:

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# m.py
class MyClass:
def f(self):
print "f()"

We can then run the monkey-patch testing like this:

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import m
def monkey_f(self):
print "monkey_f()"
m.MyClass.f = monkey_f
obj = m.MyClass()
obj.f()

The output will be as below:

monkey_f()

As we can see, we did make some changes in the behavior of f() in MyClass using the function we defined, monkey_f(), outside of the module m.

Q13. What does this mean: *args**kwargs? And why would we use it?

Ans: We use *args when we aren’t sure how many arguments are going to be passed to a function, or if we want to pass a stored list or tuple of arguments to a function. **kwargsis used when we don’t know how many keyword arguments will be passed to a function, or it can be used to pass the values of a dictionary as keyword arguments. The identifiers args and kwargs are a convention, you could also use *bob and **billy but that would not be wise.

Q14. Write a one-liner that will count the number of capital letters in a file. Your code should work even if the file is too big to fit in memory.

Ans:  Let us first write a multiple line solution and then convert it to one liner code.

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with open(SOME_LARGE_FILE) as fh:
count = 0
text = fh.read()
for character in text:
    if character.isupper():
count += 1

We will now try to transform this into a single line.

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count sum(1 for line in fh for character in line if character.isupper())

Q15. What are negative indexes and why are they used?

Ans: The sequences in Python are indexed and it consists of the positive as well as negative numbers. The numbers that are positive uses ‘0’ that is uses as first index and ‘1’ as the second index and the process goes on like that.

The index for the negative number starts from ‘-1’ that represents the last index in the sequence and ‘-2’ as the penultimate index and the sequence carries forward like the positive number.

The negative index is used to remove any new-line spaces from the string and allow the string to except the last character that is given as S[:-1]. The negative index is also used to show the index to represent the string in correct order.

Q16. How can you randomize the items of a list in place in Python?

Ans: Consider the example shown below:

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from random import shuffle
x = ['Keep', 'The', 'Blue', 'Flag', 'Flying', 'High']
shuffle(x)
print(x)

The output of the following code is as below.

['Flying', 'Keep', 'Blue', 'High', 'The', 'Flag']

Q17. What is the process of compilation and linking in python?

Ans: The compiling and linking allows the new extensions to be compiled properly without any error and the linking can be done only when it passes the compiled procedure. If the dynamic loading is used then it depends on the style that is being provided with the system. The python interpreter can be used to provide the dynamic loading of the configuration setup files and will rebuild the interpreter.

The steps that is required in this as:

  1. Create a file with any name and in any language that is supported by the compiler of your system. For example file.c or file.cpp
  2. Place this file in the Modules/ directory of the distribution which is getting used.
  3. Add a line in the file Setup.local that is present in the Modules/ directory.
  4. Run the file using spam file.o
  5. After successful run of this rebuild the interpreter by using the make command on the top-level directory.
  6. If the file is changed then run rebuildMakefile by using the command as ‘make Makefile’.

Q18. Write a sorting algorithm for a numerical dataset in Python.

Ans: The following code can be used to sort a list in Python:

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list = ["1", "4", "0", "6", "9"]
list = [int(i) for i in list]
list.sort()
print (list)

Q19. Looking at the below code, write down the final values of A0, A1, …An.

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A0 = dict(zip(('a','b','c','d','e'),(1,2,3,4,5)))
A1 = range(10)A2 = sorted([i for i in A1 if i in A0])
A3 = sorted([A0[s] for s in A0])
A4 = [i for i in A1 if i in A3]
A5 = {i:i*i for i in A1}
A6 = [[i,i*i] for i in A1]
print(A0,A1,A2,A3,A4,A5,A6)

Ans: The following will be the final outputs of A0, A1, … A6

A0 = {'a': 1, 'c': 3, 'b': 2, 'e': 5, 'd': 4} # the order may vary
A1 = range(0, 10) 
A2 = []
A3 = [1, 2, 3, 4, 5]
A4 = [1, 2, 3, 4, 5]
A5 = {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}
A6 = [[0, 0], [1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36], [7, 49], [8, 64], [9, 81]]

Q20. Explain split(), sub(), subn() methods of “re” module in Python.

Ans: To modify the strings, Python’s “re” module is providing 3 methods. They are:

  • split() – uses a regex pattern to “split” a given string into a list.
  • sub() – finds all substrings where the regex pattern matches and then replace them with a different string
  • subn() – it is similar to sub() and also returns the new string along with the no. of replacements.

Q21. How can you generate random numbers in Python?

Ans: Random module is the standard module that is used to generate the random number. The method is defined as:

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import random
random.random

The statement random.random() method return the floating point number that is in the range of [0, 1). The function generates the random float numbers. The methods that are used with the random class are the bound methods of the hidden instances. The instances of the Random can be done to show the multi-threading programs that creates different instance of individual threads. The other random generators that are used in this are:

  1. randrange(a, b): it chooses an integer and define the range in-between [a, b). It returns the elements by selecting it randomly from the range that is specified. It doesn’t build a range object.
  2. uniform(a, b): it chooses a floating point number that is defined in the range of [a,b).Iyt returns the floating point number
  3. normalvariate(mean, sdev): it is used for the normal distribution where the mu is a mean and the sdev is a sigma that is used for standard deviation.
  4. The Random class that is used and instantiated creates an independent multiple random number generators.

Q22. What is the difference between range & xrange?

Ans: For the most part, xrange and range are the exact same in terms of functionality. They both provide a way to generate a list of integers for you to use, however you please. The only difference is that range returns a Python list object and x range returns an xrange object.

This means that xrange doesn’t actually generate a static list at run-time like range does. It creates the values as you need them with a special technique called yielding. This technique is used with a type of object known as generators. That means that if you have a really gigantic range you’d like to generate a list for, say one billion, xrange is the function to use.

This is especially true if you have a really memory sensitive system such as a cell phone that you are working with, as range will use as much memory as it can to create your array of integers, which can result in a Memory Error and crash your program. It’s a memory hungry beast.

Q23. What is pickling and unpickling?

Ans: Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling.

Django – Python Interview Questions

Q24. Mention the differences between Django, Pyramid and Flask.

Ans:

  • Flask is a “microframework” primarily build for a small application with simpler requirements. In flask, you have to use external libraries. Flask is ready to use.
  • Pyramid is built for larger applications. It provides flexibility and lets the developer use the right tools for their project. The developer can choose the database, URL structure, templating style and more. Pyramid is heavy configurable.
  • Django can also used for larger applications just like Pyramid. It includes an ORM.

Q25. Discuss the Django architecture.

Ans: Django MVT Pattern:

Django Architecture - Python Interview Questions - EdurekaFigure:  Python Interview Questions – Django Architecture

The developer provides the Model, the view and the template then just maps it to a URL and Django does the magic to serve it to the user.

Q26. Explain how you can set up the Database in Django.

Ans: You can use the command edit mysite/setting.py , it is a normal python module with module level representing Django settings.

Django uses SQLite by default; it is easy for Django users as such it won’t require any other type of installation. In the case your database choice is different that you have to the following keys in the DATABASE ‘default’ item to match your database connection settings.

  • Engines: you can change database by using ‘django.db.backends.sqlite3’ , ‘django.db.backeneds.mysql’, ‘django.db.backends.postgresql_psycopg2’, ‘django.db.backends.oracle’ and so on
  • Name: The name of your database. In the case if you are using SQLite as your database, in that case database will be a file on your computer, Name should be a full absolute path, including file name of that file.
  • If you are not choosing SQLite as your database then settings like Password, Host, User, etc. must be added.

Django uses SQLite as default database, it stores data as a single file in the filesystem. If you do have a database server—PostgreSQL, MySQL, Oracle, MSSQL—and want to use it rather than SQLite, then use your database’s administration tools to create a new database for your Django project. Either way, with your (empty) database in place, all that remains is to tell Django how to use it. This is where your project’s settings.py file comes in.

We will add the following lines of code to the setting.py file:

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DATABASES = {
     'default': {
          'ENGINE' : 'django.db.backends.sqlite3',
          'NAME' : os.path.join(BASE_DIR, 'db.sqlite3'),
     }
}

GET PYTHON CERTIFIED TODAY

Q27. Give an example how you can write a VIEW in Django?

Ans: This is how we can use write a view in Django:

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from django.http import HttpResponse
import datetime
def Current_datetime(request):
     now = datetime.datetime.now()
     html = "<html><body>It is now %s</body></html>" % now
     return HttpResponse(html)

Returns the current date and time, as an HTML document

Q28. Mention what the Django templates consists of.

Ans: The template is a simple text file.  It can create any text-based format like XML, CSV, HTML, etc.  A template contains variables that get replaced with values when the template is evaluated and tags (% tag %) that controls the logic of the template.

Django Template - Python Interview Questions - EdurekaFigure: Python Interview Questions – Django Template

Q29. Explain the use of session in Django framework?

Ans: Django provides session that lets you store and retrieve data on a per-site-visitor basis. Django abstracts the process of sending and receiving cookies, by placing a session ID cookie on the client side, and storing all the related data on the server side.

Django Framework - Python Interview Questions - EdurekaFigure: Python Interview Questions – Django Framework

So the data itself is not stored client side. This is nice from a security perspective.

Q30. List out the inheritance styles in Django.

Ans: In Django, there is three possible inheritance styles:

  1. Abstract Base Classes: This style is used when you only wants parent’s class to hold information that you don’t want to type out for each child model.
  2. Multi-table Inheritance: This style is used If you are sub-classing an existing model and need each model to have its own database table.
  3. Proxy models: You can use this model, If you only want to modify the Python level behavior of the model, without changing the model’s fields.

Web Scraping – Python Interview Questions

Q31. How To Save An Image Locally Using Python Whose URL Address I Already Know?

Ans: We will use the following code to save an image locally from an URL address

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import urllib.request
urllib.request.urlretrieve("URL", "local-filename.jpg")

Q32. How can you Get the Google cache age of any URL or web page?

Ans: Use the following URL format:

http://webcache.googleusercontent.com/search?q=cache:URLGOESHERE

Be sure to replace “URLGOESHERE” with the proper web address of the page or site whose cache you want to retrieve and see the time for. For example, to check the Google Webcache age of edureka.co you’d use the following URL:

http://webcache.googleusercontent.com/search?q=cache:edureka.co

Q33. You are required to scrap data from IMDb top 250 movies page. It should only have fields movie name, year, and rating.

Ans: We will use the following lines of code:

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from bs4 import BeautifulSoup
import requests
import sys
response = requests.get(url)
soup = BeautifulSoup(response.text)
tr = soup.findChildren("tr")
tr = iter(tr)
next(tr)
for movie in tr:
title = movie.find('td', {'class': 'titleColumn'} ).find('a').contents[0]
year = movie.find('td', {'class': 'titleColumn'} ).find('span', {'class': 'secondaryInfo'}).contents[0]
rating = movie.find('td', {'class': 'ratingColumn imdbRating'} ).find('strong').contents[0]
row = title + ' - ' + year + ' ' + ' ' + rating
print(row)

The above code will help scrap data from IMDb’s top 250 list

Data Analysis – Python Interview Questions

Q34. What is map function in Python?

Ans: map function executes the function given as the first argument on all the elements of the iterable given as the second argument. If the function given takes in more than 1 arguments, then many iterables are given. #Follow the link to know more similar functions.

Q35. How to get indices of N maximum values in a NumPy array?

Ans: We can get the indices of N maximum values in a NumPy array using the below code:

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import numpy as np
arr = np.array([1, 3, 2, 4, 5])
print(arr.argsort()[-3:][::-1])

Output

[ 4 3 1 ]

Q36. How do you calculate percentiles with Python/ NumPy?

Ans: We can calculate percentiles with the following code

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import numpy as np
a = np.array([1,2,3,4,5])
p = np.percentile(a, 50)  #Returns 50th percentile, e.g. median
print(p)

Output

3

Q37. What advantages do NumPy arrays offer over (nested) Python lists?

Ans:

  1. Python’s lists are efficient general-purpose containers. They support (fairly) efficient insertion, deletion, appending, and concatenation, and Python’s list comprehensions make them easy to construct and manipulate.
  2. They have certain limitations: they don’t support “vectorized” operations like elementwise addition and multiplication, and the fact that they can contain objects of differing types mean that Python must store type information for every element, and must execute type dispatching code when operating on each element.
  3. NumPy is not just more efficient; it is also more convenient. You get a lot of vector and matrix operations for free, which sometimes allow one to avoid unnecessary work. And they are also efficiently implemented.
  4. NumPy array is faster and You get a lot built in with NumPy, FFTs, convolutions, fast searching, basic statistics, linear algebra, histograms, etc.

Q38. Explain the use of decorators.

Ans: Decorators in Python are used to modify or inject code in functions or classes. Using decorators, you can wrap a class or function method call so that a piece of code can be executed before or after the execution of the original code. Decorators can be used to check for permissions, modify or track the arguments passed to a method, logging the calls to a specific method, etc.

Q39. What is the difference between NumPy and SciPy?

Ans:

  1. In an ideal world, NumPy would contain nothing but the array data type and the most basic operations: indexing, sorting, reshaping, basic elementwise functions, et cetera.
  2. All numerical code would reside in SciPy. However, one of NumPy’s important goals is compatibility, so NumPy tries to retain all features supported by either of its predecessors.
  3. Thus NumPy contains some linear algebra functions, even though these more properly belong in SciPy. In any case, SciPy contains more fully-featured versions of the linear algebra modules, as well as many other numerical algorithms.
  4. If you are doing scientific computing with python, you should probably install both NumPy and SciPy. Most new features belong in SciPy rather than NumPy.

Q40. How do you make 3D plots/visualizations using NumPy/SciPy?

Ans: Like 2D plotting, 3D graphics is beyond the scope of NumPy and SciPy, but just as in the 2D case, packages exist that integrate with NumPy. Matplotlib provides basic 3D plotting in the mplot3d subpackage, whereas Mayavi provides a wide range of high-quality 3D visualization features, utilizing the powerful VTK engine.

LEARN PYTHON FROM EXPERTS

Multiple Choice Questions

Q41. Which of the following statements create a dictionary? (Multiple Correct Answers Possible)

a) d = {}
b) d = {“john”:40, “peter”:45}
c) d = {40:”john”, 45:”peter”}
d) d = (40:”john”, 45:”50”)

Answer: b, c & d.

Dictionaries are created by specifying keys and values.

Q42. Which one of these is floor division?

a) /
b) //
c) %
d) None of the mentioned

Answer: b) //

When both of the operands are integer then python chops out the fraction part and gives you the round off value, to get the accurate answer use floor division. For ex, 5/2 = 2.5 but both of the operands are integer so answer of this expression in python is 2. To get the 2.5 as the answer, use floor division using //. So, 5//2 = 2.5

Q43. What is the maximum possible length of an identifier?

a) 31 characters
b) 63 characters
c) 79 characters
d) None of the above

Answer: d) None of the above

Identifiers can be of any length.

Q44. Why are local variable names beginning with an underscore discouraged?

a) they are used to indicate a private variables of a class
b) they confuse the interpreter
c) they are used to indicate global variables
d) they slow down execution

Answer: a) they are used to indicate a private variables of a class

As Python has no concept of private variables, leading underscores are used to indicate variables that must not be accessed from outside the class.

Q45. Which of the following is an invalid statement?

a) abc = 1,000,000
b) a b c = 1000 2000 3000
c) a,b,c = 1000, 2000, 3000
d) a_b_c = 1,000,000

Answer: b) a b c = 1000 2000 3000

Spaces are not allowed in variable names.

Q46. What is the output of the following?

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try:
    if '1' != 1:
        raise "someError"
    else:
        print("someError has not occured")
except "someError":
    print ("someError has occured")

a) someError has occured
b) someError has not occured
c) invalid code
d) none of the above

Answer: c) invalid code

A new exception class must inherit from a BaseException. There is no such inheritance here.

Q47. Suppose list1 is [2, 33, 222, 14, 25], What is list1[-1] ?

a) Error
b) None
c) 25
d) 2

Answer: c) 25

The index -1 corresponds to the last index in the list.

Q48. To open a file c:\scores.txt for writing, we use

a) outfile = open(“c:\scores.txt”, “r”)
b) outfile = open(“c:\\scores.txt”, “w”)
c) outfile = open(file = “c:\scores.txt”, “r”)
d) outfile = open(file = “c:\\scores.txt”, “o”)

Answer: b) The location contains double slashes ( \\ ) and w is used to indicate that file is being written to.

Q49. What is the output of the following?

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f = None
for i in range (5):
    with open("data.txt", "w") as f:
        if i > 2:
            break
print f.closed

a) True
b) False
c) None
d) Error

Answer: a) True

The WITH statement when used with open file guarantees that the file object is closed when the with block exits.

Q50. When will the else part of try-except-else be executed?

a) always
b) when an exception occurs
c) when no exception occurs
d) when an exception occurs in to except block

Answer: c) when no exception occurs

The else part is executed when no exception occurs.

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How to give a eulogy is a troubling question. Simpliv’s course on How to Give a Eulogy will help you handle it.

eulogy is a troubling question

 How to Give a Eulogy will help you handle it.

A difficult situation for many people is in how to give a eulogy. Definitely, this is not an occasion that one looks forward to. Apart from being unpleasant, a eulogy also brings up many fond memories of a loved one who has departed from this world. A eulogy has to be from the heart. It is not a corporate PowerPoint presentation, nor is it an occasion to show one’s presentation skills.

Yet, a great deal of communication skill of a different kind is required for giving a eulogy. A eulogy is an occasion on which one has to strike a balance between one’s thoughts and words. One has to be careful in the choice of one’s words, because the emotions of people who hear the eulogy are likely to be raw. A wrong utterance, even if unintended, can have a bad effect and spoil the occasion.

Just like any other presentation, a eulogy can also be learnt and planned, so that it is delivered with the utmost sensitivity and empathy to the grief of the people who take part in it. A course from Simpliv, a highly popular, Fremont, CA-based platform that brings a wide variety of video libraries on both IT and non-IT subjects from renowned experts from around the world, will be showing you how to give a eulogy.

Learn from the world-famous expert

At this how to give a eulogy course, T J Walker, whom Viacom News Producer Stu Miller rates as the world’s leading media trainer, will be the expert. T J Walker has trained thousands of people from across the globe. Most of his students have gone on to make an impression on their professions. It is this expertise that will be on display at this course on how to give a eulogy.

A eulogy is an occasion for which the eulogy giver has to be as aware of what not to speak, as much as of what to. it is not an occasion for settling scores, no matter how much bad blood may have flowed between the eulogy giver and the deceased. These are just some of the delicate matters that T J Walker will draw learners’ attention to at this course on how to give a eulogy.

Concentrate on the good aspects

How to Give a Eulogy will help you handle it.

A nice strategy to adapt will be on focusing how the deceased person made an impact on somebody’s life or on some of the pleasant times that person shared with the eulogy giver. These and other finer aspects of a eulogy will be highlighted at this course on how to give a eulogy.

For this course on how to give a eulogy, the learner does not need to have any skill or experience. All that is needed is a sympathetic and willing heart that reaches out to the family of the deceased. T J Walker will show how one can rehearse for such a somber occasion as this at this course on how to give a eulogy. So, he expects learners to practice the module either on webcam or a phone. These are about the only requirements for this course on how to give a eulogy.

Learning that is pertinent and important

At this course on how to give a eulogy, T J Walker will impart important learning on the core aspects of how to give a eulogy. These include:

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Make your ceremonial speech an affair to remember with Simpliv’s course on how to deliver an excellent ceremonial speech

how to deliver an excellent ceremonial speech1

A ceremonial speech is a crucial speech. It is the most heartfelt tribute a person can pay to an institute or a person, or an event or even a strong idea. Obviously, it has to be classy, sincere and very evocative. The ceremonial speech should befit the occasion to the core. But not many people, even very gifted presenters, may get a ceremonial speech right down to the last bit all the time.

A ceremonial speech can be made memorable for the right reasons if it is prepared and delivered well. It can become memorable for the wrong reasons too, if it is fuddled and messed up. Like many other presentations, a ceremonial speech can come out superbly well if the person giving it prepares well for it and practices it. Any person who plans to give a ceremonial speech would gain immensely from course on a well-planned and well-constructed ceremonial speech.

A proper course on how to deliver an excellent ceremonial speech is thus a very important learning to have for anyone who plans to deliver one or many in the future.

Learn the elements of a great ceremonial speech

Simpliv, a Fremont, CA-based, very well-known platform which brings together a vast collection of video libraries on an extremely broad range of highly valuable subjects on all kinds of IT and non-IT courses from experts from all over the globe, will be bringing you a lively and very valuable learning at a course on how to deliver an excellent ceremonial speech.

This very interactive and educative course on how to deliver an excellent ceremonial speech will explain the elements of a kicker ceremonial speech. Let us introduce you to the speaker of this course on how to deliver an excellent ceremonial speech: it is T J Walker, a media trainer nonpareil. As the old expression goes, T J Walker needs no introduction in the world of media training. Encomiums have consistently poured in for T J Walker from all quarters over the past 30 years for which he has been training people. And did you wonder what makes him so special?

A casual looksee at some of the people he has trained over the long tenure of his career will give you an idea of why he is so highly rated: Presidents, Prime Ministers, Miss Universes, Nobel Prize winners, Members of Parliament, CEO’s of Fortune 500 companies, Super Bowl winners…whew! All these personalities and personas are part of the over 100,000 people who have enrolled for his courses. Not for nothing does Bob Bowden, Anchor/Reporter from Bloomberg Television rate T J Walker the #1 expert for executives seeking guidance on speaking to the public and media.

Business associates shaking hands in office

All the learning needed to make your ceremonial speech unforgettable

Back to this course on how to deliver an excellent ceremonial speech, what is Walker going to teach you? Well, he will run you though the steps needed to deliver an excellent speech in a delightfully thought out manner. He will show you:

The ceremonial speech has to be intuitive and unique to the occasion. So, although it is not something that can be learnt by rote, it requires a lot of preparation. This is because even if some part of the ceremonial speech is impromptu; a good deal of preparation will ensure that the right words are uttered at the right time and with the right punch.

This course on how to deliver an excellent ceremonial speech will introduce you to the aspects of a memorable ceremonial speech. Towards making this speech a very effective one, T J Walker will show how it can be got right with the right amount of learning and reinforcement of what is learnt. He will show various exercises that learners can self-record while demonstrating. Once this recording is played over many times, it will help the learner be a worthy self-critic. This practicality is one of the highlights of this course on how to deliver an excellent ceremonial speech.

Requirements for taking up this course on how to deliver an excellent ceremonial speech

To take up this course on how to deliver an excellent ceremonial speech, one needs a mobile phone or a video recorder. This is for the reason just explained: recording one’s speech with the intention of playing it over and over is an integral part of this course on how to deliver an excellent ceremonial speech.

T J Walker believes that delivering a kickass ceremonial speech is a bit like learning cycling. We fall down many times before we get it right. This video recording is like the dress rehearsal that we do when learning cycling. The recording is the testing ground for all our trials and tribulations. The more we fall, the more careful we will be in the future. This is the principle on which this idea of self-critiquing is built. When all the falling and faltering is done in one’s own location, the actual ceremonial speech will become a breeze!

So, this course on how to deliver an excellent ceremonial speech is imparted to give learners a complete heads-up on preparing and delivering ceremonial speeches that will make a great impression. Enroll today!

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Learn to use Spark for Data Science with Python

Learn to use Spark for Data Science with Python1.png

Data becomes more dynamic, and its whole character changes with Spark. What is Spark, and what makes it so unique? Well, Spark is for data scientists and analysts who use multiple systems for working with data, SQL, Python, R, Java, etc. Here is where Spark comes into the picture. With Spark, you:

  • have a single engine where you can explore and play with large amounts of data;
  • can run machine learning algorithms; and
  • can then use the same system to productionize your code.

How’s that for practicality! In simple words, your data comes to life with Spark when you use it for analytics, machine learning and data science.

Intensive and interesting learning

Want to learn this method? Then, Simpliv brings the right course for you. It offers a highly practical, hands-on course on Spark for Data Science with Python. This course on Spark for Data Science with Python is among the hundreds of courses this well-known Fremont, CA-based platform for a wide range of learning on IT and non-IT courses offers.

Simpliv specializes in simplifying learning for almost any subject or topic under the sun. Towards this end, it brings an elite assembly of the best-known names in the many industries to impart learning that is simple, practical and valuable. There is almost no topic that is precluded from Simpliv’s expansive collection of courses.

At this course on Spark for Data Science with Python, the experts that Simpliv brings is the technology and management pair comprising Janani Ravi and Vitthal Srinivasan, which goes by the name Loonycorn. With years and years of experience of working in the Bay Area, New York, Singapore and Bangalore; this Stanford, IIT and IIM-educated pair has worked at Google, Flipkart and Microsoft.

Learn to use Spark for Data Science with Python

Valuable learning

The proper method of using Spark for analytics, machine learning and data science is a skill that, when learnt, can go a long way in easing analysis of interesting datasets. This course on Spark for Data Science with Python will help learners understand Spark in the following applications:

Analytics: When used for analytics, Spark and Python help the user analyze and explore data in an interactive environment with fast feedback. At this course on Spark for Data Science with Python, Loonycorn will demonstrate how to leverage the power of Resilient Distributed Datasets (RDDs) and Dataframes to manipulate data easily.

Machine Learning and Data Science: Given Spark’s core functionality and built-in libraries; is easy to implement complex algorithms like Recommendations with very few lines of code. At this course on Spark for Data Science with Python from Simpliv, the experts will cover many datasets and algorithms including PageRank, MapReduce and Graph datasets.

 

This course on Spark for Data Science with Python from Simpliv will help you with a variety of learning. You will be able to:

  • Use Spark for a variety of analytics and Machine Learning tasks
  • Implement complex algorithms like PageRank or Music Recommendations
  • Work with a variety of datasets from Airline delays to Twitter, Web graphs, Social networks and Product Ratings
  • Use all the different features and libraries of Spark: RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming and GraphX

Requirements

Learn to use Spark for Data Science with Python3

Those who want to take up this course on Spark for Data Science with Python are expected to have working knowledge of Python. Learners can directly write Python code in the PySpark shell. The experts at this course on Spark for Data Science with Python will show learners how to configure IPython Notebook for Spark if they have it already installed.

For the Java section, learners for this course on Spark for Data Science with Python are also expected to have some knowledge of Java. An IDE which supports Maven, like IntelliJ IDEA/Eclipse could add value.

Some of the examples offered at this course on Spark for Data Science with Python work with Hadoop, and some others, without. Learners of this course on Spark for Data Science with Python who would like to use Spark with Hadoop will need to have Hadoop installed, either in pseudo-distributed or cluster mode.

This is a great time to enhance your knowledge of Spark for Data Science with Python. Enroll for this course on Spark for Data Science with Python now, and get going!

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Best computer programming for beginners

Computer Programming for Beginners3
DESCRIPTION

This course is meant to introduce people who have no programming experience to the world of computer science. With the tech industry becoming one of the most trending fields in the job market, learning how to program can be one of the most important and meaningful skills. This course will teach the basic, foundation concepts of programming in an easy-to-follow manner.

The first part of the course will get students acquainted with some basic concepts used in programming and will lay the conceptual groundwork that the rest of the course will build upon. After learning the basic terms and concepts of computer programming, the next two sections of the course allow students to practice these concepts hand-on.

Students will follow along with basic examples in two programming languages: Python and JavaScript. Both languages are easy for beginners to learn and are very user friendly. This course won’t make you an expert programmer, but it will give you an exciting first look at programming and a foundation of basic concepts with which you can start your journey learning computer programming.

Who is the target audience?

BASIC KNOWLEDGE
Computer Programming for Beginners4
  • You don’t need any prior programming knowledge or experience
  • You should be able to use a PC at beginner level.
WHAT YOU WILL LEARN

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The Complete Cyber Security Course! Volume 2 : Network Security

Cyber Security Course
DESCRIPTION

After this course, you will be able to discover security vulnerabilities across an entire network, by using network hacking techniques and vulnerability scanning .

You will be able to architect your network for maximum security and prevent local and remote attacks. We also cover the use of custom router firmware to provide you with better network security services.

You will understand the various types of firewalls that are available and what threats each help mitigate.

Including layer 4 firewalls like Iptables on Linux and PF on MacOS and BSD. Virtual firewalls, host-based firewalls and application based firewalls like Pfsence. We cover firewalls on all platforms including Windows, Mac OS X and Linux for all types of use scenarios.

Cyber Security Course3

We explore in detail wireless security, the configurations that are required for maximum security and why. How Wi-Fi is hacked and how to mitigate those attacks. Covering everything from encryption weaknesses to evil twins, RF isolation, and Wi-Fi crackers.

You will master network monitoring to discover and identify potential hackers, malware and other adversaries that might be lurking on your network. Using tools like Wireshark, Tcpdump and Syslog.

We then move away from network security and onto the details of how we are tracked online by corporations, nation-states your ISP and others. You will understand the techniques used like zombie super cookies, browser fingerprinting and how browser profiling works so third parties can establish who you are online.

We look at search engine privacy – and how to mitigate the tracking and privacy issues of search engines and their associated services.

Protection background. Technology security.

Browser security – We cover one of the largest risks online, the browser. The doorway into your system. How to best reduce the attack surface of the browser and harden it for maximum security and privacy. A critical consideration for reducing your risk.

Finally you will fully understand how to best use methods of authentication including passwords and multi-factor authentication – soft tokens and hard tokens .

The best password managers to use and why. How passwords are cracked, and how to mitigate the cracking.

This is volume 2 of 4 of your complete guide to cyber security privacy and anonymity.

BASIC KNOWLEDGE
  • This course is designed for personal and home cyber security, privacy and anonymity. Most of the topics apply in the same way to a business, but the course is delivered as if to an individual for personal cyber security, privacy, and anonymity.
  • It is recommended that you watch volume 1 of this complete course before watching this volume (2) although it is not required.
  • You can take this volume as a stand-alone course.
  • You should have a basic understanding of networking concepts.
  • Please note this is volume 2 of 4 of the complete course. After the completion of all 4 volumes, you will know more than 80% of security professionals, government and law enforcement agents and even expert hackers about maintaining security, privacy, and anonymity.
WHAT YOU WILL LEARN

An advanced practical skill-set in assuring network security against all threats including – advanced hackers, trackers, exploit kits, Wi-Fi attacks and much more.

 In this volume, we take a detailed look at network security.

 The very latest up-to-date information and methods.

Discover security vulnerabilities across an entire network, by using network hacking techniques and vulnerability scanning.

 You will be able to configure firewalls on all platforms including Windows, MacOS, and Linux for all types of attack scenarios.

 Learn to configure and architect a small network for maximum physical and wireless security.

 Perform network monitoring to discover and identify potential hackers and malware using tools like Wireshark, Tcpdump, and Syslog.

 Understand how we are tracked online by corporations, nation-states your ISP and others.

 We look at search engine privacy – we will best understand how to mitigate the tracking and privacy issues of search engines and their associated services.

 Understand how to best use methods of authentication including passwords, multi-factor authentication including soft tokens and hard tokens.

 What are the best password managers to use and why. How passwords are cracked, and how to mitigate the password attacks.

 

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Image Processing Applications on Raspberry Pi – From Scratch

Image Processing Applications on Raspberry Pi - From Scratch
DESCRIPTION

Image Processing Applications on Raspberry Pi is a beginner course on the newly launched Raspberry Pi 3 and is fully compatible with Raspberry Pi 2 and Raspberry Pi Zero.

The course is ideal for those who are new to the Raspberry Pi and want to explore more about it.

You will learn the components of Raspberry Pi, connecting components to Raspberry Pi, installation of NOOBS operating system, basic Linux commands, Python programming and building Image Processing applications on Raspberry Pi.

This course will take beginners without any coding skills to a level where they can write their own programs.

Basics of Python programming language are well covered in the course.

Building Image Processing applications are taught in the simplest manner which is easy to understand.

Users can quickly learn hardware assembly and coding in Python programming for building Image Processing applications. By the end of this course, users will have enough knowledge about Raspberry Pi, its components, basic Python programming, and execution of Image Processing applications in the real time scenario.

The course is taught by an expert team of Electronics and Computer Science engineers, having PhD and Postdoctoral research experience in Image Processing.

Anyone can take this course. No engineering knowledge is expected. Tutor has explained all required engineering concepts in the simplest manner.

The course will enable you to independently build Image Processing applications using Raspberry Pi.

Image Processing Applications on Raspberry Pi - From Scratch 3

This course is the easiest way to learn and become familiar with the Raspberry Pi platform.

By the end of this course, users will build Image Processing applications which includes scaling and flipping images, varying brightness of images, perform bit-wise operations on images, blurring and sharpening images, thresholding, erosion and dilation, edge detection, image segmentation. User will also be able to build real-world Image Processing applications which includes real-time human face eyes nose detection, detecting cars in video, real-time object detection, human face recognition and many more.

The course provides complete code for all Image Processing applications which are compatible on Raspberry Pi 3/2/Zero.

Who is the target audience?

  • Anyone who wants to explore Raspberry Pi and interested in building Image Processing applications
BASIC KNOWLEDGE
  • Only High School Maths
  • No prior programming knowledge is expected
  • All the code files and images used in this course will be provided
  • Hardware needed: Raspberry Pi 3/2/Zero, Monitor, Mouse, Keyboard, HDMI-VGA connector, USB flash drive (minimum storage capacity 2 GB), Micro SD card (minimum storage capacity 8 GB), Micro SD card reader, Power adapter (2 Amp, Micro-USB charger is preferred), USB Webcam (minimum 5 Megapixel resolution)
WHAT YOU WILL LEARN
  • What is Raspberry Pi? and what are its components?
  • Understand peripherals that need to be connected to Raspberry Pi
  • Wire up your Raspberry Pi to create a fully functional computer
  • Easily learn preparing SD Card to load Operating System for Raspberry Pi
  • Install packages needed to build Image Processing applications
  • Learn basic programming aspects of Python
  • Create simple Image Processing applications using Python and OpenCV
  • Build real-world Image Processing applications on Raspberry Pi

 

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