Learn Machine Learning Maths Behind

Course Curriculum

Chapter 1: Prerequisite : Machine Learning Basic and Introduction With Naive Bayes

Lecture 1: Machine Learning ( Algorithms) Types – Part 1

Lecture 2: Machine Learning ( Algorithms) Types – Part 2

Lecture 3: 5 Types of Different Problems Which Can be Solved With Machine Learning

Lecture 4: Machine Learning Algorithms and Why It Matters

Lecture 5: Rating Machine Learning Algorithms

Lecture 6: Algorithms We are Going to Cover and What They Can Do

Lecture 7: Starting With Naive Bayes

Lecture 8: How Naive Bayes Works and Proof

Lecture 9: How We Can Say Naive Bayes is Better

Lecture 10: Naive Bayes Graphical Proof

Lecture 11: Expert System With Naive Bayes

Chapter 2: Sprint 4.2 – Machine Learning Model Maths and Implementation on GCP

Lecture 1: General Machine Learning Algorithm Steps – Part 1

Lecture 2: General Machine Learning Algorithm Steps – Part 2

Lecture 3: Common Queries Your Have Regarding ML – Part 1

Lecture 4: Common Queries Your Have Regarding ML – Part 2

Lecture 5: Understanding Maths Side of ML With Example – P, Q and K matrix

Lecture 6: Maths Side of ML With Example – R and R^ Matrix

Lecture 7: Maths Side of ML With Example – Error for R^

Lecture 8: Maths Side of ML With Example – Error Minimization

Lecture 9: Maths Side of ML With Example – Error Minimization

Lecture 10: Maths Side of ML With Example – Final Formula to Reach to Minimum Error

Lecture 11: Maths Side of ML With Example-Graph to Show What is Happening in Iteration Part1

Lecture 12: Maths Side of ML With Example-Graph to Show What is Happening in Iteration Part2

Lecture 13: Over View of the Hands-on Part of ML

Lecture 14: Hand-Book and Spinning Up Spark Cluster

Lecture 15: Uploading the Product Rater File For Upload

Lecture 16: Upload the Product Rater Files to VM and Hadoop

Lecture 17: Upload, Transform and Divide Data into Test and Training

Lecture 18: Starting the Machine Learning Process With ALS Algo

Lecture 19: Evaluate the Root Mean Square Error With Test Data

Lecture 20: Summary of the Machine Learning Section