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Python Books Python Courses Udemy Courses

Complete Python Bootcamp 2020: With Practical Projects

What you’ll learn
  • Learn to build your own QR Code Scanner using Computer Vision.
  • Learn to use Google’s API for speech recognition using python.
  • Learn file handling by making an marks database project
  • Learn about functions by making an advanced calculator
  • Learn to use Object Oriented Programming with classes.
  • Learn to use Python 3 professionally
  • Learn advanced Python features, handle errors and work with modules
  • Understand how to use both the Jupyter Notebook and create .py files
Requirements
  • Access to a computer with internet facility
  • A burning desire to learn.
Description

Become a Python Programmer and learn one of employer’s most requested skills of 2020!

This is  a crisp, clear and comprehensive course for the Python programming language! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3.

With over 50 lectures and more than 2 hours of high quality video this refresher course leaves no stone unturned! This course includes a lot of interesting quizzes, and homework assignments as well as 2 major projects to create your own portfolio right away!

This course will teach you Python in a practical manner, with every lecture comes a full coding screen-cast, corresponding code notebook, interesting quizzes and homework assignment! Learn in whatever manner is best for you!

We will start by helping you get Python installed on your computer, regardless of your operating system, whether its Linux, MacOS, or Windows, we’ve got you covered!

We cover a wide variety of topics, including:

  • Command Line Basics
  • Installing Python
  • Running Python Code
  • Strings
  • Lists
  • Dictionaries
  • Tuples
  • Sets
  • Number Data Types
  • Print Formatting
  • Functions
  • args/kwargs
  • Debugging and Error Handling
  • Modules
  • Object Oriented Programming
  • File I/O
  • and more lectures will be added as required to keep the course updated!

Why this course is only 2.5 hrs long? Can we learn python in this duration?

This is the question I’m frequently asked from a lot of beginners. There are a number of Python courses on Udemy which extend upto 30 hours!!! But what you need to know here is that Python is an extremely easy language and you don’t need to waste much time learning python. Python is just the first step towards a number of technologies which you may learn after this. The technology you want learn depends on your interest and this course aims to prepare you for that in a very short amount of time but in a very powerful manner. By taking up the course you will feel confident about the python language and you will be able to tackle anything you desire.

You will get lifetime access to over 50 lectures plus corresponding Notebooks for the lectures!

In case you don’t believe me…. This course comes with a 30 day money back guarantee! If you are not satisfied in any way, you’ll get your money back. No questions asked!!

So what are you waiting for? Learn Python in a way that will advance your career and increase your knowledge, all in a fun and practical way!

Who this course is for:
  • Beginners who are getting into programming for the first time
  • Beginners who want to start a career in Artificial Intelligence/ Data Science/ Machine Learning/ Robotics
  • Programmers who want to switch to Python
  • Everyone who wants to learn how to code and apply the knowledge in real life
  • Everyone who wants to practice real world python projects

Take Course

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Machine Learning, AI & Deep Learning Python Courses

Deep Learning Foundation : Linear Regression And Statistics

What you’ll learn
  • Mathematics behind R-Squared, Linear Regression,VIF and more!
  • Deep understating of Gradient descent and Optimization
  • Program your own version of a linear regression model in Python
  • Derive and solve a linear regression model, and implement it appropriately to data science problems
  • Statistical background of Linear regression and Assumptions
  • Assumptions of linear regression hypothesis testing
  • Writing codes for T-Test, Z-Test and Chi-Squared Test in python
Requirements
  • Jupyter notebook and simple python programming
Description

Hi Everyone welcome to new course which is created to sharpen your linear regression and statistical basics. linear regression is starting point for a data science this course focus is on making your foundation strong for deep learning and machine learning algorithms. In this course I have explained hypothesis testing, Unbiased estimators, Statistical test , Gradient descent. End of the course you will be able to code your own regression algorithm from scratch.

Who this course is for:
  • Python developers curious about data science
  • data science and machine leaning engineers

Take Course

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MATLAB Tutorials Python Python Courses Udemy Courses

Signal processing problems, solved in MATLAB and in Python

Applications-oriented instruction on signal processing and digital signal processing (DSP) using MATLAB and Python codes

What you’ll learn

  • Understand commonly used signal processing tools
  • Design, evaluate, and apply digital filters
  • Clean and denoise data
  • Know what to look for when something isn’t right with the data or the code
  • Improve MATLAB or Python programming skills
  • Know how to generate test signals for signal processing methods
  • *Fully manually corrected English captions!

Requirements

  • Basic programming experience in MATLAB or Python
  • High-school mathn

Description

Why you need to learn digital signal processing.

Nature is mysterious, beautiful, and complex. Trying to understand nature is deeply rewarding, but also deeply challenging. One of the big challenges in studying nature is data analysis. Nature likes to mix many sources of signals and many sources of noise into the same recordings, and this makes your job difficult.

Therefore, one of the most important goals of time series analysis and signal processing is to denoise: to separate the signals and noises that are mixed into the same data channels.

The big idea of DSP (digital signal processing) is to discover the mysteries that are hidden inside time series data, and this course will teach you the most commonly used discovery strategies.

What’s special about this course?

The main focus of this course is on implementing signal processing techniques in MATLAB and in Python. Some theory and equations are shown, but I’m guessing you are reading this because you want to implement DSP techniques on real signals, not just brush up on abstract theory.

The course comes with over 10,000 lines of MATLAB and Python code, plus sample data sets, which you can use to learn from and to adapt to your own coursework or applications.

In this course, you will also learn how to simulate signals in order to test and learn more about your signal processing and analysis methods.

Are there prerequisites?

You need some programming experience. I go through the videos in MATLAB, and you can also follow along using Octave (a free, cross-platform program that emulates MATLAB). I provide corresponding Python code if you prefer Python. You can use any other language, but you would need to do the translation yourself.

I recommend taking my Fourier Transform course before or alongside this course. However, this is not a requirement, and you can succeed in this course without taking the Fourier transform course.

What should you do now?

Watch the sample videos, and check out the reviews of my other courses — many of them are “best-seller” or “top-rated” and have lots of positive reviews. If you are unsure whether this course is right for you, then feel free to send me a message. I hope you to see you in class!

Who this course is for:

  • Students in a signal processing or digital signal processing (DSP) course
  • Scientific or industry researchers who analyze data
  • Developers who work with time series data
  • Someone who wants to refresh their knowledge about filtering
  • Engineers who learned the math of DSP and want to learn about implementations in software

Created by Mike X Cohen
Last updated 10/2019

Size: 5.70 GB

Categories
IT & Software Python Python Courses Udemy Courses

Python for beginners – Learn all the basics of python 2020

Learn how to program in python- python functions-python basic apps – python tips and tricks – Other Python features

What you’ll learn

  • Learn how to use Python 3 the right way
  • Understand complex functions in python
  • Be able to use python on a daily basis
  • Create your own basic programs with python

Requirements

  • Having a computer
  • Wanting to learn programming in python
  • no experience required

Description

Have you always wanted to learn programming but didn’t know where to start ?  Well now you are at the right place ! I created this python course to help everyone learn all the basics of this programming language. This course is really straight to the point and will give you all the notion about python. Also, the course is not that long so and the way the material is presented is very easy to assimilate. So if python is something that you are interested about, then you will definitely like this course.

Who this course is for:

  • People interested to learn how to program in python
  • people curious about programming

Size: 1.47 GB

Categories
PHP Scripts | Source Code Python Books Python Courses

Social Distancing Detector in Python with Source code

Free Download Advance Social Distancing Detector in Python with Deep learning Tutorial & Source code and Database. Advance Social Distancing Detector is Develop in Python with open_CV , Deep Learning that can detect if people are keeping a safe distance from each other by analyzing real time video streams from the CCTV or Safety camera.

Face Recognition system Develop Using 

1-OpenCV
2-Python
3-Learning

How to perform Object Detection Using ImageAI

  • Install Python on your computer system
  • Install ImageAI and its dependencies
  •  Download the Object Detection model file
  •  Run the sample codes

How to Start

1-Install Python 3 in your System From Python Official website

2-Install the following dependencies via pip

-TensorFlow

pip3 install tensorflow

-OpenCV

pip3 install opencv-python

-Keras

pip3 install keras

-ImageAI

pip3 install imageai –upgrade

3-Download the RetinaNet model file that will be used for object detection

For More Detail You Can Download Full Tutorial and Source code  

Size: 340 MB

Categories
Machine Learning, AI & Deep Learning PHP Scripts | Source Code Python Books Python Courses

Python Face Detection System with Source Code

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Data Science Machine Learning Python Python Courses Udemy Courses

Beginning with Machine Learning & Data Science in Python

Fundamentals of Data Science : Exploratory Data Analysis (EDA), Regression (Linear & logistic), Visualization, Basic ML

What you’ll learn

  • You will be able to apply data science algorithms for solving industry problems
  • You will have a clear understanding of industry standards and best practices for predictive model building
  • Will be able to derive key insights from data using exploratory data analysis techniques
  • You will be able to efficiently handle data in a structured way using Pandas
  • Have a strong foundation of linear regression, multiple regression and logistic regression
  • You will be able to use python scikit-learn for building different types of regression models
  • Will be able to use cross validation techniques for comparing models, select parameters
  • You will know about common pitfalls in modeling like over-fitting, bias-variance trade off etc..
  • You will be able to regularize models for reliable predictions

Requirements

  • Basic programming in any language
  • Basic Mathematics
  • Some exposure to Python (but not mandatory)

Description

This course will help you create a solid foundation of the essential topics of data science. With a solid foundation, you will be able to go a long way, understand any method easily, and create your own predictive analytics models.

At the end of this course, you will be able to:

  • Get your hands dirty by building machine learning models
  • Master logistic and linear regression, the workhorse of data science
  • Build your foundation for data science
  • Fast-paced course with all the basic & intermediate level concepts
  • Learn to manage data using standard tools like Pandas

This course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications.

Special emphasis is given to regression analysis. Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well. Concepts of over fitting, regularization etc. are discussed in details.

These fundamental understandings are crucial as these can be applied to almost every machine learning methods.

This course also provide an understanding of the industry standards, best practices for formulating, applying and maintaining data driven solutions. It starts off with basic explanation of Machine Learning concepts and how to setup your environment. Learning the industry standard best practices and evaluating the models for sustained development comes next.

Final learning are around some of the core challenges and how to tackle them in an industry setup. This course supplies in-depth content that put the theory into practice.

Who this course is for:

  • Anyone willing to take the first step towards data science
  • Anyone willing to develop a solid foundation for data science
  • Planning to build the first regression / machine learning models
  • Anyone willing to learn exploratory data analysis

Size: 542.72 MB

Categories
Python Python Courses Udemy Courses

100 Python Challenges to Boost Your Python Skills Course

Solve 100 carefully crafted Python exercises directly on the interactive U-platform to solidify your Python skills.

What you’ll learn

100 Python Challenges to Boost Your Python Skills Course

  • Learn Python 3 by solving Python problems on your own
  • Solve data structures and algorithm exercises to help on your data science journey
  • Create 50+different functions
  • Learn the basics of Python classes
  • Read, write, and extract information from files
  • Extract data from text
  • Optimize your code in terms of processing speed and memory efficiency
  • Introspect your code
  • Learn Python tricks

Requirements

  • To make the best of this course you should be familiar with Python basics: loops, conditionals, functions, and datatypes.

Description

Video tutorials are great to learn the basics of Python. You can try out and experiment with the code that the teacher demonstrates in the video. However, never did anyone became a programmer by just watching videos. That is because your brain hardly learns anything when it’s not in problem solving mode.
When you put your mind in problem solving mode your mind will devour every piece of information it finds in an effort to solve the given problem. Therefore, if you want to learn Python programming your only way is to sit and solve Python problems on your own.

That is what this course is all about.

Here you will find an interactive Python problem solving interface where you will try to solve more than 100 Python exercises and quizzes. Moreover, you will find timed exams that will rigorously test the knowledge you gained when you were solving the preceding exercises. The problems cover different areas of Python such as functions, data structures and algorithms, files, classes, code introspection, code optimization, and more. That means you can even do this course on your mobile phone while traveling on a bus or train.
The course assumes you have some basic knowledge of Python so if you know loops, functions, conditionals, and datatypes, then this course is for you. If you don’t have any knowledge of Python you can still take the course, but you will have a hard time solving the exercises. Python is both fun and extremely useful. Don’t miss the chance to learn it.

Who this course is for:

  • Beginner Python Developers
  • Content From: https://www.udemy.com/course/the-python-fitness-program/
Categories
Data Science Machine Learning Python Courses

MACHINE LEARNING A-Z™: HANDS-ON PYTHON & R IN DATA SCIENCE

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

  • Master Machine Learning on Python & R
  • Have a great intuition of many Machine Learning models
  • Make accurate predictions
  • Make powerful analysis
  • Make robust Machine Learning models
  • Create strong added value to your business
  • Use Machine Learning for personal purpose
  • Handle specific topics like Reinforcement Learning, NLP and Deep Learning
  • Handle advanced techniques like Dimensionality Reduction
  • Know which Machine Learning model to choose for each type of problem
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Requirements

  • Just some high school mathematics level.

Description

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:

  • Part 1 – Data Preprocessing
  • Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
  • Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
  • Part 4 – Clustering: K-Means, Hierarchical Clustering
  • Part 5 – Association Rule Learning: Apriori, Eclat
  • Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
  • Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
  • Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
  • Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
  • Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.Who is the target audience?

  • Anyone interested in Machine Learning.
  • Students who have at least high school knowledge in math and who want to start learning Machine Learning.
  • Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science.
  • Any data analysts who want to level up in Machine Learning.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.
  • Any people who want to create added value to their business by using powerful Machine Learning tools.

Curriculum For This Course287 Lectures

41:12:35

Size: 6.84G

Categories
Development Python Python Courses Udemy Courses

Python Programming for Beginners – Learn in 100 Easy Steps Course Free Download

Python for Absolute Beginners. Learn Python Programming using a Step By Step Approach with 200+ code examples.

What you’ll learn

Python Programming for Beginners – Learn in 100 Easy Steps Course Free Download

  • You will Learn Python the MODERN WAY – Step By Step – With 200 HANDS-ON Code Examples
  • You will Understand the BEST PRACTICES in Writing High Quality Pythonic Code
  • You will Solve a Wide Range of Hands-on Programming EXERCISES with Python
  • You will Learn to Write AWESOME Object Oriented Programs with Python
  • You will Acquire ALL the Python Skills needed to TRANSITION into Analytics, Machine Learning and Data Science Roles
  • You will Acquire ALL the SKILLS to demonstrate an EXPERTISE with Python Programming in Your Job Interviews
  • You will learn about a wide variety of Python Data Structures – List, Set, Dictionary and Tuples
  • You will learn the basics of PyCharm IDE and Python Shell
  • You will learn how to think as a Python Programmer
  • You will learn the basics of programming – variables, choosing a data type, conditional execution, loops, writing great methods, breaking down problems into sub problems and implementing exception handling.
  • You will learn the basics of Object Oriented Programming – Inheritance, Abstract Class and Constructors
  • You will learn the important concepts of Object Oriented Programming – Abstraction and Inheritance

Requirements

  • You have an attitude to learn while having fun 
  • We will help you install Python 3 and PyCharm
  • You have ZERO Python Programming Experience

Description

Python is one of the most popular programming languages. Python offers both object oriented and structural programming features.We take an hands-on approach using a combination of Python Shell and PyCharm as an IDE to illustrate more than 150 Python Coding Exercises, Puzzles and Code Examples.We love Programming. Our aim with this course is to create a love for Programming.

What our Learners say?

Best Course on Python ever in-depth explanation and Experienced Instructor. If this course would had any fee i would have payed it happily :)”

“It was a such an amazing experience, loved the way he teaches. I have learned a lot so far still so much more to learn. I would highly recommend this course to beginners in Python. Thank you!”
This is a great course for those who have no idea what programming involves. It teaches you in a very simple and easy to follow manner that is fun and really rewording. Definitely what you need to get your head around computer programming basics, so you can progress on to cool stuff that lead you hear in the first place. :)”

Python Programming for Beginners – Learn in 100 Easy Steps Course Free Download

I like the way of teaching. I have really learned a lot from some few lessons! I’m a completely newbie to programming, but everything is clear so far. Keep it up!”

“Great overview of python for a beginner with programming. Covers with just sufficient depth the topics to understand the basics of python. Highly recommendable for anyone who is just beginning programming.

This was one hell of a journey. The 100 steps took me a great distance in getting to know Python with depth. I loved the friendly instructor and admired his in-depth knowledge on the subject.”

“This course may seem to move at a slow pace at first, but this is essential. pay attention to his methods and his logic – this course is the one in a million course that changed the direction my coding journey has taken.”

“I was learning python for the first time. This is the best course for beginners.

“Course was very helpful and instructor teaching method was awesome.”

Concepts are beautifully explained for a beginner.Well done!!”

This guy is the best instructor ever!

In more than 150 Steps, we explore the most important Python Programming Language Features

  • Basics of Python Programming – Expressions, Variables and Printing Output
  • Python Operators – Python Assignment Operator, Relational and Logical Operators, Short Circuit Operators
  • Python Conditionals and If Statement
  • Methods – Parameters, Arguments and Return Values
  • An Overview Of Python Platform
  • Object Oriented Programming – Class, Object, State and Behavior
  • Basics of OOPS – Encapsulation, Inheritance and Abstract Class.
  • Basics about Python Data Types
  • Basics about Python Built in Modules
  • Conditionals with Python – If Else Statement, Nested If Else
  • Loops – For Loop, While Loop in Python, Break and Continue
  • Immutablity of Python Basic Types
  • Python Data Structures – List, Set, Dictionary and Tuples
  • Introduction to Variable Arguments
  • Basics of Designing a Class – Class, Object, State and Behavior. Deciding State and Constructors.
  • Introduction to Exception Handling – Your Thought Process during Exception Handling. try, except, else and finally. Exception Hierarchy. Throwing an Exception. Creating and Throwing a Custom Exception.

Step By Step Details

Introduction To Python Programming With Multiplication Table

  • Step 01 – Getting Started with Programming
  • Step 02 – Introduction to Multiplication Table challenge
  • Step 03 – Break Down Multiplication Table Challenge
  • Step 04 – Python Expression – An Introduction
  • Step 05 – Python Expression – Exercises
  • Step 06 – Java Expression – Puzzles
  • Step 07 – Printing output to console with Python
  • Step 08 – Calling Functions in Python – Puzzles
  • Step 09 – Advanced Printing output to console with Python
  • Step 10 – Advanced Printing output to console with Python – Exercises and Puzzles
  • Step 11 – Introduction to Variables in Python
  • Step 12 – Introduction to Variables in Python – Puzzles
  • Step 13 – Assignment Statement
  • Step 14 – Tip – Using formatted strings in print method
  • Step 15 – Using For Loop to Print Multiplication Table
  • Step 16 – Using For Loop in Python – Puzzles
  • Step 17 – Using For Loop in Python – Exercises
  • Step 18 – Getting Started with Programming – Revise all Terminology

Introduction To Methods – MultiplicationTable

  • Step 00 – Section 02 – Methods – An Introduction
  • Step 01 – Your First Python Method – Hello World Twice and Exercise Statements
  • Step 02 – Introduction to Python Methods – Exercises
  • Step 03 – Introduction to Python Methods – Arguments and Parameters
  • Step 04 – Introduction to Python Method Parameters – Exercises
  • Step 05 – Introduction to Python Method – Multiple Parameters
  • Step 06 – Getting back to Multiplication Table – Creating a method
  • Step 07 – Tip – Indentation is king
  • Step 08 – Introduction to Python Method – Puzzles – Named Parameters
  • Step 09 – Introduction to Python Method – Return Values
  • Step 10 – Introduction to Python Method – Return Values – Exercises

Introduction To Python Platform

  • Step 01 – Writing and Executing your First Python Script
  • Step 02 – Python Virtual Machine and bytecode

Introduction To PyCharm

Step 01 – Installing and Introduction to PyCharm
Step 02 – Write and Execute a Python File with PyCharm
Step 03 – Execise – Write Multiplication Table Method with PyCharm
Step 04 – Debugging Code with PyCharm
Step 05 – PyCharm Tips : Tool Windows
Step 06 – PyCharm Tips : Keyboard Shortcuts

Basic Numeric Data Types and Conditional Execution

Step 01 – Introduction to Numeric Data Types
Step 02 – Exercise – Calculate Simple Interest
Step 03 – Introduction to Numeric Data Types – Puzzles
Step 04 – Introduction to Boolean Data Type
Step 05 – Introduction to If Condition
Step 06 – Introduction to If Condition – Exercises
Step 07 – Logical Operators – and or not
Step 08 – Logical Operators – and or not – Puzzles
Step 09 – Introduction to If Condition – else and elif
Step 10 – if, else and elif – Menu Exercise – Part 1
Step 11 – if, else and elif – Menu Exercise – Part 2
Step 12 – if, else and elif – Puzzles

Text in Python

Step 01 – Text in Python – Methods in str class
Step 02 – Data Type Conversion – Puzzles
Step 03 – Strings are immutable
Step 04 – There is no seperate Character data type
Step 05 – String moduleEDIT
Step 06 – Exercise – is_vowel, print lower case and upper case characters
Step 07 – String – Exercises and Puzzles
Step 08 – String – Conclusion

Python Loops

Step 01 – For loop basics
Step 02 – For loop exercise 1 – is_prime
Step 03 – For loop exercise 2 – sum_upto_n
Step 04 – For loop exercise 3 – sum of divisors
Step 05 – For loop exercise 4 – print a number triangle
Step 06 – Introduction to while loop in Python
Step 07 – While loop – Exercises
Step 08 – Choosing a Loop – Menu Exercise
Step 09 – Loops – Puzzles – break and continue

Beginner Tips

Tip 1 – Using Predefined Python Modules
Tip 2 – Loop – Getting Index Element
Tip 3 – Python is Strongly Typed and Dynamic Language
Tip 4 – Beginners Mistakes – Shadowing
Tip 8 – Defining Equality for Classes
Tip 5 – Beginners Mistakes – Indentation
Tip 6 – PEP8 – Python Style Guide
Tip 7 – PEP20 – Zen of Python

Introduction To Object Oriented Programming

Step 00 – Introduction to Object Oriented Programming – Section Overview
Step 01 – Introduction to Object Oriented Programming – Basics
Step 02 – Introduction to Object Oriented Programming – Terminology – Class, Object, State and Behavior
Step 03 – Introduction to Object Oriented Programming – Exercise – Online Shopping System and Person
Step 04 – First Class and Object – Country class
Step 05 – Create Motor Bike Python Class and a couple of objects
Step 06 – Class and Objects – a few Puzzles
Step 07 – Constructor for MotorBike class
Step 08 – Constructor for Book class – Exercise
Step 09 – Constructors – Puzzles
Step 10 – Class and Objects – Methods and Behavior
Step 11 – Exercise – Enhance Book class with copies
Step 12 – Class and Objects – Methods and Behavior – Puzzles on self
Step 13 – Advantages of Encapsulation
Step 14 – Everything is Object in Python

Python Data Structures

Step 01 – Python Data Structures – Why do we need them?
Step 02 – Operations on List Data Structure
Step 03 – Exercise with List – Student class
Step 04 – Puzzles with Strings Lists
Step 05 – List Slicing
Step 06 – List Sorting, Looping and Reversing
Step 07 – List as a Stack and Queue
Step 08 – List with a custom class – Country and representation
Step 08 – List with a custom class – Part 2 – sorting, max and min
Step 09 – List Comprehension
Step 10 – Introduction to Set
Step 11 – Introduction to Dictionary
Step 12 – Exercise with Dictionary – Word and Character Occurances
Step 13 – Puzzles with Data Structures

Object Oriented Programming Again

Step 01 – OOPS Basics Revised
Step 02 – Designing a Fan Class
Step 03 – Object Composition – Book and Reviews
Step 04 – Why do we need Inheritance
Step 05 – All classes in Python 3 inherit from object
Step 06 – Multiple Inheritance
Step 07 – Creating and Using an Abstract Class
Step 08 – Template Method Pattern with Recipe Class
Step 09 – A Quick Revision

Error Handling with Python

Step 01 – Introduction to Error Handling – Your Thought Process during Error Handling
Step 02 – Basics of Exception Hierarchy
Step 03 – Basics of Error Handling – try except
Step 04 – Handling Multiple Errors with Multiple except blocks
Step 05 – Error Handling – Puzzles – Exception Details and
Step 06 – Error Handling – finally and else
Step 07 – Error Handling – Puzzles 2
Step 08 – Raising Exceptions
Step 09 – Raising Custom Exceptions
Step 10 – Exception Handling Best Practices

Final Tips

Tip 1 – Math Module and Decimal Class
Tip 2 – Statistics Module – find mean and median
Tip 3 – Collections Module – deque for Queue and Stack
Tip 4 – Methods and Arguments – Basics
Tip 5 – Methods and Arguments – Keyword Arguments
Tip 6 – Methods and Arguments – Unpacking Lists and Dictionaries
Tip 7 – Creating Custom Modules and Using Them

Who this course is for:

  • You want to Learn Programming with Python
  • You are a Beginner with No Programming Experience
  • You want to automate things with Python
  • Content From: http://www.udemy.com/python-tutorial-for-beginners/

Size: 4.2GB