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Course: Machine Learning from scratch through Python

Machine Learning from scratch through Python

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
About this Course

This course is for those who want to step into Artificial Intelligence domain, specially into Machine Learning, though I will be covering Deep Learning in deep as well.

This is a basic course for beginners, just if you can get basic knowledge of Python that would be great and helpful to you to grasp things quickly.

There are 4-5 Projects on real data set which will be very helpful to start your career in this domain, Right now if you don't see the project, don't panic, it might have gone old so I've put it down for modifications.

I will be updating course on daily basis, so stay tuned.

Enjoy and Good Luck.

Basic knowledge
  • Basic knowledge of Python, Numpy, Pandas, Mathematics and Statistics is a add on else all will be covered when necessary
What you will learn
  • Great knowledge of Machine Learning and Deep Learning Algorithms
  • Build your own ML Algorithm, Models and Predictions
  • Hands-on Numpy, Panda, Matplotlib, etc and many more
Curriculum
Number of Lectures: 14
Total Duration: 12:15:18
Lecture 1
  • Introduction to AI  

    Introduction to Artificial Intelligence and its subsets Machine Learning and Deep Learning with Curriculum and examples.

Lecture 2
  • Supervised and UnSupervised Learning  
Lecture 3
  • KNN (Lp Norms)  
Lecture 4
  • KNN (Euclidean and Manhattan Distance)  
Lecture 5
  • KNN ( Minkowski, Hamming and Cosine Distance )  
Lecture 6
  • Over and Under Fitting ( Cross Validation and K-Fold CV )  
Lecture 7
  • Project 1 ( creating our first Model and finding the accuracy of it)  
Lecture 8
  • Linear Regression  
Lecture 9
  • Project 2 ( Simple Linear Regression )  
Lecture 10
  • Project 3 ( Multi - Linear Regression )  
Lecture 11
  • HYPOTHESIS TESTING ( Statistics Fundamentals )  
Lecture 12
  • Decision Tree Part with Gini Index  
Lecture 13
  • Decision Tree with Information Gain  
Lecture 14
  • Project 4 ( Decision Tree )  
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