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Machine Learning With Python – 3 Day Virtual Workshop

₹3,999.00 ₹2,999.00

Course Content

Total learning: 50 lessons Time: 10 weeks

Introduction to ML 0/7
- Lecture1.1
  
  Artificial Intelligence & Machine Learning Introduction
- Lecture1.2
  
  Who uses AI?
- Lecture1.3
  
  Supervised & Unsupervised Learning
- Lecture1.4
  
  Regression & Classification Problems
- Lecture1.5
  
  What makes a Machine Learning Expert?
- Lecture1.6
  
  What to learn to become a Machine Learning Developer?
- Lecture1.7
  
  Overview of Machine Learning Algorithms
Linear Regression - Case Study & Project 0/8
- Lecture2.1
  
  Regression Problem Analysis
- Lecture2.2
  
  Mathematical modelling of Regression Model
- Lecture2.3
  
  Gradient Descent Algorithm
- Lecture2.4
  
  Use cases
- Lecture2.5
  
  Model Specification
- Lecture2.6
  
  L1 & L2 Regularization
- Lecture2.7
  
  Building simple Univariate Linear Regression Model
- Lecture2.8
  
  Multivariate Regression Model
Multivarite Linear Regression 0/3
- Lecture3.1
  
  Polynomial Linear Regression
- Lecture3.2
  
  Ridge Regression
- Lecture3.3
  
  Lasso Regression
KNN 0/3
- Lecture4.1
  
  KNN Theory
- Lecture4.2
  
  KNN with Python
- Lecture4.3
  
  KNN Exercise with real data
Decision Trees 0/4
- Lecture5.1
  
  Forming a Decision Tree
- Lecture5.2
  
  Components of Decision Tree
- Lecture5.3
  
  Mathematics of Decision Tree
- Lecture5.4
  
  Decision Tree Evaluation for use cases
Bagging and Boosting 0/6
- Lecture6.1
  
  Random Forest Mathematics
- Lecture6.2
  
  Bias Variance Tradeoffs
- Lecture6.3
  
  Bagging
- Lecture6.4
  
  Boosting
- Lecture6.5
  
  Bootstrapping
- Lecture6.6
  
  Ensemble models with real world data
Support Vector Machine 0/5
- Lecture7.1
  
  Concept and Working Principle
- Lecture7.2
  
  Mathematical Modelling
- Lecture7.3
  
  Optimization Function Formation
- Lecture7.4
  
  The Kernel Method and Nonlinear Hyperplanes
- Lecture7.5
  
  Use Cases & Programming SVM using Python
Logistic Regression 0/7
- Lecture8.1
  
  Assumptions
- Lecture8.2
  
  Reason for the Logit Transform
- Lecture8.3
  
  Logit Transformation
- Lecture8.4
  
  Hypothesis
- Lecture8.5
  
  Variable and Model Significance
- Lecture8.6
  
  Maximum Likelihood Concept
- Lecture8.7
  
  Log Odds and Interpretation
Clustering 0/5
- Lecture9.1
  
  Hierarchical Clustering
- Lecture9.2
  
  K Means Clustering
- Lecture9.3
  
  Use Cases for K Means Clustering
- Lecture9.4
  
  Programming for K Means using Python
- Lecture9.5
  
  Cluster Size Optimization vs Definition Optimization
Recommendation Systems 0/2
- Lecture10.1
  
  Market basket Analysis
- Lecture10.2
  
  Collaborative Filtering

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