The Complete Machine Learning

The Complete Machine Learning

The Complete Machine Learning

Level : Basic

Price : 29999   15999

5 Month(s)

Designed by: IIT Madras Alumni

Welcome to our advanced Machine Learning course, where you'll delve into the fascinating world of ML under the guidance of experts who graduated from IIT Madras.

Course Overview

Embark on a transformative journey through our comprehensive curriculum, featuring:

  • 80 Audio-Video Lessons: Immerse yourself in the intricacies of Machine Learning with our engaging multimedia content. Each lesson is thoughtfully curated to cater to diverse learning styles, ensuring maximum comprehension and retention.

  • Language Flexibility: Choose your preferred language of instruction – English or Hindi. We believe in making learning accessible to all, regardless of linguistic background.

  • Practical Projects: Apply theoretical concepts to real-world scenarios with hands-on projects tailored to reinforce your understanding of ML algorithms and techniques.

  • Comprehensive Content: From foundational concepts like regression and classification to advanced topics such as deep learning and reinforcement learning, our course covers the entire spectrum of Machine Learning.

Work on Live Projects, Master Machine Learning

Gain valuable hands-on experience by working on live projects that simulate real-world ML applications. By applying your knowledge in practical settings, you'll not only enhance your skills but also build a robust portfolio to showcase your expertise to potential employers.

Key Features

  • Comprehensive Training: Our course offers a holistic approach to Machine Learning, covering everything from the basics to advanced methodologies. Whether you're new to ML or looking to deepen your understanding, our expertly designed curriculum has you covered.

  • Prep for Advanced Studies: Lay a strong foundation for pursuing advanced studies in ML and related fields. Understand the underlying principles and algorithms that drive ML models, setting yourself up for success in higher education and research.

  • Expert Guidance: Benefit from the expertise of our instructors, who bring a wealth of knowledge and experience to the table. Learn from industry professionals who have successfully applied ML techniques in diverse domains.

Prerequisites & Requirements

  • Basic Programming Skills: While prior experience in programming is not mandatory, familiarity with Python or a similar language will be beneficial.

  • PC/Laptop Access: Access to a computer or laptop is essential for practicing coding and working on projects. Our course is designed to be accessible from any standard computing device.

  • English Proficiency: A basic understanding of English is recommended to fully engage with the course content and instructions.

Start Your Machine Learning Journey Today

Ready to unlock the potential of Machine Learning and embark on a transformative learning experience? Enroll in our advanced ML course today and take the first step towards mastering one of the most sought-after skills in the digital age. Join us as we explore the limitless possibilities of ML and empower ourselves to innovate, create, and shape the future. Let's embark on this journey together – the world of Machine Learning awaits!


Machine learning Part 1 

1. Introduction to Machine Learning 
 2. Need for Machine Learning 
 3. Machine Learning Process 
 4. Types of Machine Learning 
 5. Problems which can be solved by using machine learning 
 6. Surprised learning Algorithms 
 7. Decision Tree 
 8. Decision Tree Algorithm 
9.Information gain& entropy 
 10. Random Forest 
 11. Creating a Random Tree 
 12. Navie Bayes 
 13. SVM 
 14. K Means Clustering 
 15. K nearest Neighbour 
 16. Reinforcement learning 
 17. Terminologies used in RL 
 18. Reward Maximization 
 19. Markov’s Decision 
 20. Understanding Q learning 
 21. AI VS ML VS DL 
 22. Overfitting & Underfitting 
 23. RNN Module 

Machine learning Part 2 (Probability) 

1. Introduction to Probability 
 2. How to calculate expected values? 
 3. Events & their Complements 
 4. Combinatorics 
 5. Permutation 
 6. Operations with factorial 
 7. Variations 
 8. Variations without repetition 
 9. Combination 
 10. Sets & Elements 
 11. How can sets interact? 
 12. Intersection of sets 
 13. Union of sets 
 14. Mutually Exclusive Set 
 15. Independent & Dependent Events 
 16. Conditional Probability 
 17. Bayes law 
 18. Baye’s Theorem Example 
 19. Total probability law 
 20. Addition Rule 
 21. Multiplication Rule 
 22. Probability Distributions Fundamental 
 23. Types of Probability Distributions Fundamental Part 1 
 24. Types Probability Distributions Fundamental Part 2 
 25. Features of Continuous Distributions 
 26. Features of Discreet Distributions 
 27. Union Distributions 
 28. Bernoulli Distributions 
 29. Binomial Distributions 
 30. Poisson Distributions 
 31. Normal Distributions 

Machine learning Part 3 (Statistics) 

1. Population and Sample 
 2. Types of data 
 3. Measurement levels 
 4. Categorical and Numerical Variables 
 5. Histogram 
 6. Mean, Median and Mode 
 7. Skewness 
 8. Variance 
 9. Standard Deviation 
 10. Correlation Coefficient 
 11. Introduction to Distribution 
 12. Standard Normal Distribution 
 13. Central Limit Theorem 
 14. Estimator and Estimate 
 15. Confidence Interval 
 16. Students T Distribution 
 17. Null and Alternative Hypothesis 
 18. Type 1 and Type 2 Error 
 19. P value 

 Machine learning Part 4 (Advanced Statistics) 

 1. Regression Analysis 
 2. Correlation Vs Regression 
 3. Python Packages Installation 
 4. How to install Jupiter Notebook 
 5. First Linear Regression in Python 
 6. Decomposition of Variability 
 7. OLS 
 8. R Squared 
 9. Multiple Linear Regression 
 10. Adjusted R Squared 
 11. Linearity 
 12. Logistic Regression 
 13. Logistic vs Logit Function 
 14. Overfitting and Underfitting 
 15. Cluster Analysis 
 16. Classification vs Clustering 
 17. K means Clustering 
 18. Choose the value of K number of clustering. 
 19. K means Clustering Limitations 
 20. Types of Clustering

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