Complete Python Course(Bundle)

Give your career a new dimension with complete Python course. Create your own Machine Learning application.

₹51150 ($629)

About Our Course:

Designed by the OK Google Developer, this course will help you master the most popular and highly-demanded programming language Python along with Data Structures and Machine Learning. Create your own Machine Learning applications by the end of this course. 

Complete Python Course Thumbnail
Complete Python Course
What you will learn ?
  • Why Python is Important
  • Transition from Python2 to Python3
  • Installation of Python
  • How to use your machine to code online
  • Build your 1st Python code
  • Constants, Reserved Words, & Variables
  • Data Types in Python
  • Using Conditions in Python
  • Using Comparisons in Python
  • Functions in Python
  • Loops in Python
  • Iterations in Python
  • Introduction to Data Structure
  • Strings & Functions
  • Handling files in Python
  • List & Dictionaries
  • Sets & Tuples
  • Constructors
  • Inheritance & Polymorphism
  • Regressions in Python
  • Clustering and Cluster Analysis
  • Projects
  • Lecture 4 Lectures
  • Introduction to the course Preview
  • Need of Programming: What it takes to become a top notch programmer Preview
  • Introduction to Python: Why it is the most demanded language and how tech giants are using it
  • Introduction to Python: Transition from Python 2 to Python 3
  • Lecture 4 Lectures
  • Instructions: Installation of Python on Windows and Mac
  • Demo: Step-by-step guide to installing Python on your PC
  • Python Usage: Introduction to practical use case of Python
  • Python Online: How to use your Machine to code online
  • Lecture 1 Lectures
  • Build your 1st Python code: Hello world assignment
  • Lecture 9 Lectures
  • Module Assignment Solutions: Expressions and User
  • Constants in Python: Introduction, application and use cases
  • Variable Reserved Words in Python: Introduction, application and use cases
  • Data Types in Python
  • Conversions in Python: Type and string conversions
  • Taking user inputs in Python: Introduction and application
  • Module Quiz: Constant Resever Words Variables
  • Constants Reserved Words Variables Assignment
  • Module Assignment Solution: Expressions and User inputs
  • Lecture 8 Lectures
  • Using Comparison Operator in Python
  • Using IF ELIF Conditions in Python
  • Demo: Using conditions in Python
  • Using EXCEPT in Python
  • Demo: Using EXCEPT in Python
  • Module Quiz: Conditions and Comparisons
  • Conditions And Comparisons Assignment
  • Module Assignment Solution: TRY EXCEPT Assignment
  • Lecture 7 Lectures
  • Understanding Functions in Python
  • Understanding Functions with Arguments in Python
  • Return Functions: Introduction and use cases
  • Multiple Arguments in Python: Introduction and use cases
  • Module Quiz: Functions in Python
  • Module Assignment: Functions in Python
  • Module Assignment Solution: Functions in Python
  • Lecture 9 Lectures
  • Loops in Python: Introduction
  • Break Continue: Understanding and Use Cases
  • FOR Loop: Understanding and Use Cases
  • Demo: Loops Code Implmentation
  • Loops and interation
  • Demo: Mastering Loops with more Use Cases
  • Module Quiz: Loops in Python
  • Module Assignment: Loops in Python
  • Module Assignment Solution: Loops in Python
  • Lecture 2 Lectures
  • Data Structures in Python: Course Introduction
  • Machine Learning in Python: Course Introduction
  • Lecture 2 Lectures
  • Live Class Python Interview Questions: Top 15 Python Programming Questions
  • Social Media Links for reference
  • Lecture 10 Lectures
  • Introduction Video: Data Structures Course Preview
  • Introduction to Strings and LEN function Preview
  • String Looping: Introduction and Use Cases
  • String Slicing: Introduction and Use Cases
  • String Library Function: Introduction and Use Cases
  • Stripping Whitespace: Introduction and Use Cases
  • Module Quiz: Strings in Python
  • Module Assignment: Strings in Python
  • Module Assignment Solution: Strings in Python
  • Lecture 8 Lectures
  • Introduction to Files in Python
  • Counting Lines in a File in Python: Introduction and use cases
  • Reading files in Python: Introduction and use cases
  • Searching Lines in a File in Python: Introduction and use cases
  • Naming Files with User Data: Introduction and use cases
  • Module Quiz: Files in Python
  • Module Assignment: Handling files in Python
  • Module Assignment Solution: Handling files in Python
  • Lecture 8 Lectures
  • Introduction to Lists in Python
  • Finding Length of Range Objects in Python: len()
  • Continuation of lists and their use cases
  • Calculating Sum and Average of Lists in Python
  • Spliting Strings in to Lists in Python: split()
  • Moduel Quiz: Lists
  • Module Assignment: Lists in Python
  • Module Assignment Solution: Lists in Python
  • Lecture 7 Lectures
  • Understanding Dictionaries in Python
  • Distinguishing Lists and Dictionaries in Python: Lists Vs. Dictionaries
  • Demo: Understanding Get Function in Python
  • Iterating over dictionaries with loops
  • Module Quiz: Dictionaries in Python
  • Module Assignment: Dictionaries in Python
  • Module Assignment Solution: Dictionaries in Python
  • Lecture 6 Lectures
  • Understanding Tuples in Python
  • Distinguishing Tuples and Dictionaries in Python: Tuples Vs. Dictionaries
  • Sorting a Tuple in Python
  • Module Quiz: Tuples
  • Module Assignment: Tuples in Python
  • Module Assignment Solution: Tuples in Python
  • Lecture 10 Lectures
  • Uderstanding Sets in Python
  • Creating Sets in Python
  • Creating Empty Dictionaries in Python: Empty Set
  • Understanding Add and Update in Sets
  • Deleting Elements of Sets in Python
  • Distinguishing Sets, Lists and Dictionaries in Python: Sets Vs. Lists Vs. Dictionaries
  • Python Set Operations (Union, Intersection, Difference and Symmetric Difference)
  • Understanding Python Set Methods
  • Module Quiz: Sets in Python
  • Module Assignment: Python Sets
  • Lecture 2 Lectures
  • Machine Learning: Introduction
  • Social Media Links
  • Lecture 3 Lectures
  • Data Structure Interview Questions: Top Data Structure Programming Questions
  • Data Structures: Implementing Stacks and Queues
  • Live Class: Searching, Sorting and Complexity Analysis in Data Structures
  • Lecture 5 Lectures
  • Understanding programming concepts in relation to OOPs Preview
  • Distinguishing Procedural and OOPs: Procedural Programming Vs. Object Oriented Programming Preview
  • Understanding Objects in OOPs
  • Understanding Classes in OOPs
  • Demo: Objects and Classes in OOPs
  • Lecture 2 Lectures
  • Understanding Constructors
  • Understanding Types of Constructors
  • Lecture 2 Lectures
  • Introduction to Encapsulation and Abstraction in Python
  • Use Cases of Encapsulation and Abstraction in Python
  • Lecture 5 Lectures
  • Understanding Inheritance in Python
  • Demo: Use Cases of Inheritance
  • Understanding Polymorphism in Python
  • Demo: Use Cases of Polymorphism
  • Various Functions in Inheritance and Polymorphism
  • Lecture 2 Lectures
  • Module Quiz: OOPs
  • Module Assignment : OOPs
  • Lecture 16 Lectures
  • Python OOPs Concepts
  • Python Seaborn Tutorial
  • Sets in Python Tutorial
  • H2O Tutorial : Machine Learning Framework
  • Types of data distribution
  • Erros in Machine Learning Models
  • Dealing with Missing Values in Python
  • Understanding Machine Learning: Use Case Scenarios
  • Project: Medical Cost Prediction
  • Python Libraries Guides
  • Matplotlib: Creating Static, Animated, and Interactive visualizations in Python
  • Pandas: Analysing Data in Python
  • *Args And **Kwargs: Passing a variable number of arguments to a Python function
  • NumPy: Working with Arrays in Python
  • Red Wine Project
  • Project - Stop Watch
  • Lecture 6 Lectures
  • Course Walkthrough Preview
  • Overview to Machine Learning Preview
  • Artificial Intelligence Vs Machine Learning Vs Deep Learning
  • Machine Learning: Python Libraries
  • Supervised Vs. Unsupervised Learning
  • Module Quiz: Machine Learning Overview
  • Lecture 12 Lectures
  • Introduction to Regression
  • Various Regression Techniques: Linear, Polynomial, Stepwise, Ridge, Lasso and ElasticNet
  • Linear Regression using Python
  • Linear Regression Techniques: Slopes And Intercept
  • Linear Regression using SKLEARN Coding
  • Linear Regression Demo using Scikit Lean Code Link
  • Demo Code Explaination: Linear Regression using SKLEARN Coding
  • Errors in Linear Techniques
  • R-Squared or Coefficient of Determination
  • Multiple Linear Regression (MLR)
  • Polynomial Vs. Non Linear Regression
  • Module Quiz: Regression in Python
  • Lecture 8 Lectures
  • Introduction to Classification
  • K-Nearest Neighbor(KNN) Algorithm for Machine Learning Introduction
  • Working with K-Nearest Neighbor(KNN) Algorithm for Machine Learning
  • Evaluation Metrics for Classification
  • Machine Learning Decision Tree Classification Algorithm
  • Support Vector Machine (SVM) Algorithm in Machine Learning
  • Machine Learning Random Forest Algorithm
  • Module Quiz: Classification
  • Lecture 4 Lectures
  • Clustering Algorithms in Machine Learning
  • Clustering Vs Classifications: Machine Learning
  • K means Clustering - Introduction
  • Module Quiz: Clusterin in Machine Learning
  • Lecture 4 Lectures
  • Understanding Recommender Systems in Machine Learning
  • Content-based Filtering: Recommendation Systems
  • Collaborative Filtering: Recommendation Systems
  • Module Quiz: Recommender Systems in Machine Learning
  • Lecture 3 Lectures
  • Introduction to the Project: Guess Game
  • Project Implementation: Undestanding the Concepts to Build Guess Game
  • Project Code : Guess Game using Python
  • Lecture 4 Lectures
  • Understanding Python Libraries
  • Three-dimensional Plotting in Python using Matplotlib
  • Tri-Surface Plot in Python using Matplotlib
  • Data Science Project Code: 3D Plot
  • Lecture 6 Lectures
  • Understanding Machine Learning Concepts
  • Flowcharts - Problem Solving with Python
  • Deep Learning: Training and Testing the Network with Tensorflow and Keras
  • Tensorflow and Keras For Neural Networks and Deep Learning
  • Final Certification Quiz; Complete Course
  • Machine Learning Final Assignment
  • Lecture 1 Lectures
  • Machine Learning Live Project
  • Lecture 4 Lectures
  • Logistic Regression for Malignancy Prediction in Cancer (Breast Cancer)
  • Spam Mail Prediction using Machine Learning with Python
  • Personal Assistant
  • Weather recommendation system
  • Lecture 7 Lectures
  • Understanding Machine Learning Workflow
  • Decoding Model Selection for Machine Learning
  • Understanding Overfitting in Machine Learning
  • Understanding Underfitting in Machine Learning
  • Understanding the Bias-Variance Tradeoff
  • Introduction to Dimensionality Reduction for Machine Learning
  • Gradient Descent algorithm and its Variants
  • Lecture 1 Lectures
  • Hands-On-Guide To Machine Learning Model Deployment Using Flask
  • Lecture 1 Lectures
  • GitHub Tutorial and Profile Buidling
  • Lecture 2 Lectures
  • Introduction - Overleaf, Online LaTeX Editor Preview
  • Building Professional/Educational Resume using LaTeX Preview
Our Mentors:
Jyoti Sharma

Jyoti Sharma

OK Google Developer

4.6 Rating

Sample Certificate:
What Jyoti Sharma has to say:

B.Tech & M.Tech Computer Science at NIT Kurukshetra (Department Ranker 1, CGPA: 9.8) with experience in Android App Development and Machine Learning. I am highly enthusiastic about learning and teaching cutting-edge technologies. My interests include Teaching, Data Structures, Algorithms, and Problem Solving. Worked at Infoedge(Naukri), Ok Google, and got offers from various tech companies including Adobe, UrbanCompany, etc, I would love to create more developers by imparting what I know. Happy learning to all the students!

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Success Stories

The words of these guys are pure pleasure.

Thank you Perfect eLearning for such informative course. The learning material, practical knowledge and technical expertise helped me grab the job. I would like to thank the entire team at Perfect Plan B for empowering me.
Every accomplishment start with the decision to try. Taking the course at Perfect eLearning is one of the crucial decision I have made and Perfect eLearning made it as the best. Thank you Perfect eLearning to help me to grab ...
I got 45% hike after doing the MACHINE LEARNING Course. Perfect eLearning officially inspired me learn more and more. Now I have the skill set that is required by today's industries, with ample of oppertunities in front of me.
Perfect eLearning helped me to learn the technologies from scratch which game me strong command in building applications using the latest technologies. Finally I fulfilled my dream with the help of Perfect elearning
I just want to say Thank you all for being so helpful supportive and friendly throughout this placement. I really had a fantastic time and learnt so much along the way. The things that I have learnt will be invaluable to my future placement...
My name is Ramya Pokala.I got placed in VLink.I just want to Thank you all for your support and guidance throughout this placement.Before joining in Perfect eLearning I really have zero knowledge on python and machine learning but afte...