Machine Learning in Python Syllabus

• Introduction
• Concepts
• Environment Setup
• Types of Learning
• Data Preprocessing, Analysis & Visualization
• Training Data and Test Data
• Techniques
• Algorithms
• Applications
• Basics
• Python Ecosystem
• Methods for Machine Learning
• Data Loading for ML Projects
• Understanding Data with Statistics
• Understanding Data with Visualization
• Preparing Data
• Data Feature Selection
• ML Algorithms − Classification
• Introduction
• Logistic Regression
• Support Vector Machine(SVM)
• Decision Tree
• Naïve Bayes
• Random Forest
• ML Algorithms − Regression
• Overview
• Linear Regression
• ML Algorithms − Clustering
• Overview
• K-Means Algorithm
• Mean Shift Algorithm
• Hierarchical Clustering
• ML Algorithms − KNN Algorithm
• Finding Nearest Neighbors
• Automatic Workflows
• Improving Performance of ML Models
• Improving Performance of ML Model(contd..)