• • Getting Started with Machine Learning
• • How to use LearnVern

• • Introduction to Machine Learning
• • Environment Setup Part 1
• • Environment Setup Part 2
• • Environment Setup Part 3
• • Quiz : Introduction to Machine Learning

• • Data Wrangling
• • Importing Libraries and Dataset
• • Handling Missing Data
• • Handling Missing Data - Practical
• • Encoding Categorical Data
• • Encoding Catergorical Data - Practical
• • Splitting Dataset
• • Splitting Dataset - Practical
• • Normalizing the Data - Part 1
• • Normalizing the Data - Part 2
• • Finding Machine Learning Datasets
• • Exploratory Data Analysis
• • Plotting Graphs - Part 1
• • Plotting Graphs - Part 2
• • Distribution Models - Part 1
• • Distribution Models - Part 2
• • Assignment : Data Preprocessing for Machine Learning
• • Quiz : Data Preprocessing for Machine Learning

• • Machine Learning Paradigms
• • Assignment : Machine Learning Paradigms
• • Quiz : Machine Learning Paradigms

• • Decision Tree Iterative Dichotomiser 3
• • Random Forest
• • Support Vector Machine Classifier
• • Support Vector Machine Classifier - Practical 1
• • Support Vector Machine Classifier - Practical 2
• • Naive Bayes Classifier
• • Naive Bayes Classifier - Practical 1
• • Naive Bayes Classifier - Practical 2
• • Evaluating Classification Models Performance
• • Evaluating Classification Models Performance - Practical
• • Overview of Classification
• • Logistic Regression
• • Logistic Regression - Practical - 1
• • Logistic Regression - Practical - 2
• • KNN
• • KNN Practical - 1
• • KNN - Practical 2
• • Decision Trees for Classification
• • Decision Trees for Classification - Practical 1
• • Decision Trees for Classification - Practical 2
• • Assignment : Supervised Learning Algorithms
• • Quiz : Supervised Learning Algorithms

• • Simple Linear Regression
• • Simple Linear Regression - Practical
• • Salary Prediction using Linear Regression
• • Multi-Linear Regression
• • Startup Prediction using Multiple Regression
• • Support Vector Regressor
• • Support Vector Regressor - Practical 1
• • Support Vector Regressor - Practical 2
• • Decision Tree Regressor
• • Decision Tree Regressor - Practical 1
• • Decision Tree Regressor - Practical 2
• • Regressor Model Selection
• • Evaluating Regression Model Performance
• • Evaluating Regression Model Performance - Practical
• • Assignment : Regression Algorithms
• • Quiz : Regression Algorithms

• • Distance Metrics
• • K-Means Clustering
• • K-Means Clustering - Practical
• • Mall Customers Prediction using K Means Clustering
• • Hierarchical Clustering - Agglomerative , Divisive
• • Agglomerative Clustering - Practical
• • Divisive Clustering - Practical
• • DBscan Spatial Clustering
• • Mall Customers Prediction using Hierarchical Clustering
• • Assignment : Unsupervised Learning Algorithms
• • Quiz : Unsupervised Learning Algorithms

• • Association Rule Learning - Apriori, FP Growth
• • Association Rule Learning - Apriori Practical
• • Market Basket Analysis using Apriori
• • FP Growth
• • Market Basket Analysis using FP Growth
• • Assignment : Association Rule Mining
• • Quiz : Association Rule Mining

• • Reinforcement Learning Theory - Multi Armed Bandits
• • Upper Confidence Bound - Practical
• • Thompson Sampling - Practical
• • Q Learning
• • Assignment : Reinforcement Learning
• • Quiz : Reinforcement Learning

• • Overview of Dimensoionality Reduction
• • Princinpal Component Analysis
• • Principal Component Analysis - Practical
• • Linear Discriminant Analysis
• • Linear Discriminant Analysis - Practical
• • Assignment : Dimensionality Reduction
• • Quiz : Dimensionality Reduction

• • Basics of Regularization and Optimization
• • Cross Validation
• • Hyperparameter Tuning
• • Sampling Methods
• • Underfitting and Overfitting in Models
• • Variance and Bias
• • Assignment : Regularization and Optimization
• • Quiz : Regularization and Optimization

Advance Trends in Machine Learning

• • Introduction to Keras and Deep Learning
• • Practical Demonstration -Keras

• • Reinforcement Learning Project - Teach a Taxi Part 1
• • Reinforcement Learning Project - Teach a Taxi Part 2
• • Reinforcement Learning Project - Teach a Taxi Part 3
• • Reinforcement Learning Project - Teach a Taxi Part 4
• • Loan Prediction Project Part 1
• • Loan Prediction Project Part 2

Course Summary

• • Interview Questions Part 1
• • Interview Questions Part 2
• • Interview Questions Part 3

Career Guidelines