What you'll learn?
- Great knowledge of Machine Learning and Deep Learning Algorithms.
- Work on real case studies
- 5 projects to work on which can be easily put up on resume for better placements.
- Build your own ML Algorithm, Models and Predictions.
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. If you want to take python lectures I have created a ' Python for Data Science from Scratch ' course also , you can enroll and go through that as well before starting with this.
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.
Enjoy and Good Luck.
- Basic Python, Mathematics and Statistics are the prerequisites for this course, however I will be covering everything.
- Beginner or Stepping into AI, ML, DL domain with 4-5 Projects on real data set.
Curriculum 14 Lectures 00:16:02
Section 1 : Unpublished Section
- Lecture 1 :
- Introduction to Artificial Intelligence Preview
- Lecture 2 :
- Introduction to Machine Learning with Supervised and Un-Supervised Learning.
- Lecture 3 :
- ML : KNN( Lp Norms )
- Lecture 4 :
- ML : KNN ( Euclidean and Manhattan Distance)
- Lecture 5 :
- ML : KNN ( Minkowski, Hamming and Cosine Distance )
- Lecture 6 :
- ML : Over and Under Fitting( Cross Validation and K-Fold CV )
- Lecture 7 :
- Project 1 : Creating the First Model using KNN and finding the Accuracy.
- Lecture 8 :
- ML : Linear Regression
- Lecture 9 :
- Project 2 : based on SIMPLE LINEAR REGRESSION
- Lecture 10 :
- Project 3 : based on MULTIPLE LINEAR REGRESSION
- Lecture 11 :
- ML : HYPOTHESIS TESTING ( Statistics Fundamentals )
- Lecture 12 :
- ML : Decision Tree with Gini Index
- Lecture 13 :
- ML : Decision Tree with Information Gain
- Lecture 14 :
- Project 4 : CASE STUDY based on DECISION TREE
Our learners work at
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