data science

‘The Complete Machine Learning Course with Python’ course will help you to Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning and more.

By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, detect cancer cells and much more. The course is usually available for INR 2,599 on Udemy but students can click on the link and get the ‘The Complete Machine Learning Course with Python’ for INR 449.

Who all can opt for this course?

  • Anyone who wants to study Python-based machine learning algorithms
  • Anyone with a keen interest in the application of machine learning in practise to issues in the real world
  • Anyone who wants to study more than the fundamentals and gain a comprehensive understanding of machine learning methods
  • Anyone who is unable to work with huge datasets and is an intermediate to advanced EXCEL user
  • Someone with an interest in effectively and professionally communicating their findings
  • Anyone looking to begin or advance their career as a data scientist
  • Whomever wants to use machine learning into their industry

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 2,59984% off
Duration17 Hours
Student Enrollment33,965 students
InstructorCodestars • over 2 million students worldwide!•over2millionstudentsworldwide!
Topics CoveredMachine learning, Python, SVM, Regression
Course LevelIntermediate
Total Student Reviews6,015

Learning Outcomes

  • With this course, you can become an excellent candidate for the $166,000 average salary of machine learning engineers
  • With the help of robust machine learning models, you can solve any issue in your business, career, or personal life
  • Teach computer programmes to recognise handwriting, detect cancer cells, and other things
  • Study Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning, etc

Course Content

S.No.Module (Duration)Topics
1.Introduction (04 minutes)What Does the Course Cover?
How to Succeed in This Course
Project Files and Resources
2.Getting Started with Anaconda (56 minutes)Installing Applications and Creating Environment
Hello World
Iris Project 1: Working with Error Messages
Iris Project 2: Reading CSV Data into Memory
Iris Project 3: Loading data from Seaborn
Iris Project 4: Visualization
3.Regression (04 hours 06 minutes)Scikit-Learn
Correlation Analysis and Feature Selection
Correlation Analysis and Feature Selection
Linear Regression with Scikit-Learn
Five Steps Machine Learning Process
Robust Regression
Evaluate Regression Model Performance
Multiple Regression 1
Multiple Regression 2
Regularized Regression
Polynomial Regression
Dealing with Non-linear Relationships
Feature Importance
Data Preprocessing
Variance-Bias Trade Off
Learning Curve
Cross Validation
CV Illustration
4.Classification (01 hour 44 minutes)Logistic Regression
Introduction to Classification
Understanding MNIST
Performance Measure and Stratified k-Fold
Confusion Matrix
Precision Recall Tradeoff
Altering the Precision Recall Tradeoff
5.Support Vector Machine (SVM) (39 minutes)Support Vector Machine (SVM) Concepts
Linear SVM Classification
Polynomial Kernel
Radial Basis Function
Support Vector Regression
6.Tree (01 hour 05 minutes)Introduction to Decision Tree
Training and Visualizing a Decision Tree
Visualizing Boundary
Tree Regression, Regularization and Over Fitting
End to End Modeling
Project HR
Project HR with Google Colab
7.Ensemble Machine Learning (01 hour 12 minutes)Ensemble Learning Methods Introduction
Random Forests and Extra-Trees
Gradient Boosting Machine
XGBoost Installation
Project HR – Human Resources Analytics
Ensemble of Ensembles Part 1
Ensemble of ensembles Part 2
8.k-Nearest Neighbours (kNN) (39 minutes)kNN Introduction
Project Cancer Detection
Addition Materials
Project Cancer Detection Part 1
9.Unsupervised Learning: Dimensionality Reduction (36 minutes)Dimensionality Reduction Concept
PCA Introduction
Project Wine
Kernel PCA
Kernel PCA Demo
Project Abalone
10.Unsupervised Learning: Clustering (24 minutes)Clustering
k_Means Clustering
11.Deep Learning (01 hour 11 minutes)Estimating Simple Function with Neural Networks
Neural Network Architecture
Motivational Example – Project MNIST
Binary Classification Problem
Natural Language Processing – Binary Classification
12.Appendix A1: Foundations of Deep Learning (02 hours 03 minutes)Introduction to Neural Networks
Differences between Classical Programming and Machine Learning
Learning Representations
What is Deep Learning
Learning Neural Networks
Why Now?
Building Block Introduction
Tensor Operations
Gradient Based Optimization
Getting Started with Neural Network and Deep Learning Libraries
Categories of Machine Learning
Over and Under Fitting
Machine Learning Workflow
13.Computer Vision and Convolutional Neural Network (CNN) (02 hours 34 minutes)Outline
Neural Network Revision
Motivational Example
Visualizing CNN
Understanding CNN
Layer – Input
Layer – Filter
Activation Function
Pooling, Flatten, Dense
Training Your CNN 1
Training Your CNN 2
Loading Previously Trained Model
Model Performance Comparison
Data Augmentation
Transfer Learning
Feature Extraction
State of the Art Tools

Resources Required

  • Python programming fundamentals are required
  • Excellent knowledge of linear algebra

Featured Review

Victor Oluwamayowa Olatunji (5/5) : the class was awesome!!!! but i will need to tutor to provide link to the resources to avoid being passive learner. with adequate and comprehensive we can learn and archive real quick.


  • Raymond Carreon (5/5) : This is simply an awesome course.! An excellent reference material too.
  • Mirza Baig (5/5) : It was a very good training which helped me to learn about machine language
  • Raja Ranjith Garikapati (5/5) : Very good content and learned a lot of concepts on ML with this course.
  • Johannes Tannert (4/5) : So it is not the ideal course to code along and learn.


  • Russell Ritenour (2/5) : At the point where we get to bagging, the code provided in the github repository is useless for experimentation and bears no resemblance to the lecture.
  • Ahmed Bagais (2/5) : I am still unable to get to the Anaconda Navigator screen.
  • Niklas Gustafsson (2/5) : A lot of unecessary words are being used, it’s slightly irritating.
  • Irene Pérez (2/5) : For the moment is it really slow, not on the content but on the way of speaking.

About the Author

The instructor of this course is Codestars • over 2 million students worldwide! who is a Teaching the Next Generation of Coders. With 4.5 Instructor Rating and 465,698 Reviews on Udemy, he/she offers 80 Courses and has taught 2,215,618 Students so far.

  • Best-selling Rob Percival, an Udemy instructor, aims to transform the way people learn to code by making it easy, rational, entertaining, and above all, accessible
  • Rob was able to develop some of the courses that his more than 500,000 students requested, but he was only one man
  • Rob founded Codestars with that in mind
  • Together, the teachers who make up the Codestars team design well-structured, incredibly dynamic, and simple-to-understand courses on all the subjects that students are interested in learning
  • For students of all ages and skill levels, Codestars strives to make it as simple as possible for them to create useful websites and apps

Comparison Table

ParametersThe Complete Machine Learning Course with PythonData Science: Modern Deep Learning in PythonData Science: Deep Learning and Neural Networks in Python
OffersINR 449 (INR 2,599) 84% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration17.5 hours11.5 hours12 hours
Student Enrollments33,95433,62652,709
InstructorsCodestars • over 2 million students worldwide!Lazy Programmer Inc.Lazy Programmer Inc.
Register HereApply Now!Apply Now!Apply Now!

Leave feedback about this

  • Rating