Machine Learning A-Z: Become Kaggle Master

Master Machine Learning Algorithms Using Python From Beginner to Super Advance Level including Mathematical Insights.

4.45 (514 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Master Machine Learning on Python

Learn to use MatplotLib for Python Plotting

Learn to use Numpy and Pandas for Data Analysis

Learn to use Seaborn for Statistical Plots

Learn All the Mathmatics Required to understand Machine Learning Algorithms

Implement Machine Learning Algorithms along with Mathematic intutions

Projects of Kaggle Level are included with Complete Solutions

Learning End to End Data Science Solutions

All Advanced Level Machine Learning Algorithms and Techniques like Regularisations , Boosting , Bagging and many more included

Learn All Statistical concepts To Make You Ninza in Machine Learning

Real World Case Studies

Model Performance Metrics

Deep Learning

Model Selection

Why take this course?

Want to become a good Data Scientist? Then this is a right course for you.

This course has been designed by IIT professionals who have mastered in Mathematics and Data Science. We will be covering complex theory, algorithms and coding libraries in a very simple way which can be easily grasped by any beginner as well.

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 from beginner to advance level.

We have solved few Kaggle problems during this course and provided complete solutions so that students can easily compete in real world competition websites.

We have covered following topics in detail in this course:

1. Python Fundamentals

2. Numpy

3. Pandas

4. Some Fun with Maths

5. Inferential Statistics

6. Hypothesis Testing

7. Data Visualisation

8. EDA

9. Simple Linear Regression

10. Multiple Linear regression

11. Hotstar/ Netflix: Case Study

12. Gradient Descent

13. KNN

14. Model Performance Metrics

15. Model Selection

16. Naive Bayes

17. Logistic Regression

18. SVM

19. Decision Tree

20. Ensembles - Bagging / Boosting

21. Unsupervised Learning

22. Dimension Reduction

23. Advance ML Algorithms

24. Deep Learning

Screenshots

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Reviews

Ronit

October 22, 2022

The way of teaching is just amazinggggg!!! very lively like things tought here.....just started the course.......exited to see the future contents along the way to progress this course

Ankit

May 4, 2021

Support from author is not there. Explanations were good but sample data missing from few section, informed the author but no response.

Daniel

February 1, 2021

He teaches the subject matter relatively well. There are other teachers more engaging and clear to bring students to become active, questioned, challenged, or to play with these things on their own, especially with Jupiter notebooks to make it easier to play around with these concepts. But the course is not bad. It might put you to sleep after a little while, but if you want to learn the subject and hang in there, you'll learn it. It's definitely worthwhile.

Ajeetesh

January 6, 2021

The content is good but the instructor needs to speak clearly. Recorded video lecture is way different from teaching in person. And in a flow, without much going here and there

Yogesh

November 17, 2020

Good course.. but poor grammar and typos on screen spoil the fun. Sometimes, it feels like ppt deck would have been a better choice instead of black board approach.

Victor

October 20, 2020

Spoken in a terrible English and horrible hand-writing. Why not using slides? All in all, a very low quality course.

May

August 3, 2020

The course unfortunately did not offer any help in the Q&A section. Codes were not updated and had many errors where had to set aside and move on to the next section. No one on the other side was available either. Many of the questions asked were not answered. Instructor kept saying to ask in the lecture Q&A, which was in no use. Class notes could be prepared in a way where they could be downloaded for future reference, not with screenshots but with actual reviewable and searchable format, .doc .ppt .ipynb

Saurabh

July 19, 2020

Goods: Well Structured good for basic concepts Bads: No Responses to questions Examples are very very basic Sometimes there are errors in the content

Sushant

June 30, 2020

The regression model topic is taught very very fast. Chapter for data pre-processing was all theoretical. Could not understand how we can do it in notebook. Also, the data in regression model was very perfect, so could not understand how do we exactly perform data pre-processing and exploratory data analysis

Amir

June 27, 2020

Thanks for providing this information and all the insight. There were some small issues in mathematical terms, and a little part of some Python codes were out of date.

Karan

May 10, 2020

all the coding and maths behind the machine learning algorithm is explained perfectly. the only problem is that you won't get any reply of your query ..But you can google it may be in stactoverflow.

Hariom

March 23, 2020

the mentor is very good and at this price, I can get more content which is really helpful for your Kaggle competition,

Jocker18

February 25, 2020

I cannot thank you enough for this great course. Thank you and keep up the good work. Looking forward to see the Deep Learning course that you mentioned.

Andrew

January 25, 2020

This course is pretty comprehensive and friendly to newbies. Also, you can get access to the sample codes from the instructor. It is great in general!

Siddharth

January 12, 2020

There are concepts which are not covered in detail. Like SGD (which is used in the final project), Factor analysis (as PCA was covered) etc. Otherwise the course was pretty good and interesting. The final project could have been even more exhaustive to the point where the best solution was arrived.

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