Machine Learning Certification Course

I. Introduction to Machine Learning

• Learning Objectives
• Relationship between Artificial Intelligence, Machine Learning, and Data Science
• Definition and Features of Machine Learning
• Machine Learning Approaches

II. Supervised Learning Regression and Classification

• Learning Objectives
• Supervised Learning
• Supervised Learning: Real Life Scenario
• Understanding the Algorithm
• Supervised Learning Flow
• Types of Supervised Learning
• Types of Classification Algorithms
• Types of Regression Algorithms
• Accuracy Metrics
• Cost Function
• Evaluating Coefficients
• Challenges in Prediction

III. Decision Trees and Random Forest

• Learning Objectives
• Decision Tree: Classifier
• Decision Tree: Examples
• Decision Tree: Formation
• Choosing the Classifier
• Overfitting of Decision Trees
• Random Forest Classifier Bagging and Bootstrapping

IV. Unsupervised Learning

• Learning Objectives
• Overview
• Example and Applications of Unsupervised Learning
• Clustering
• Hierarchical Clustering
• K-means Clustering
• Optimal Number of Clusters

V. Time Series Modelling

• Learning Objectives
• Overview of Time Series Modeling
• Time Series Pattern Types
• White Noise
• Stationarity
• Removal of Non Stationarity
• Time Series Models

VI. Ensemble Learning

• Learning Objectives
• Overview
• Ensemble Learning Methods
• Working of AdaBoost
• Gradient Boosting
• XGBoost
• Model Selection

VII. Recommender Systems

• Learning Objectives
• Introduction
• Purposes of Recommender Systems
• Paradigms of Recommender Systems
• Collaborative Filtering
• Association Rule Mining

VIII. Practice Projects