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Section-1
What is AI
What is Machine Learning?
Types of Machine Learning
Machine Learning Workflow
Machine Learning Life Cycle
Section-2
Basic Python
Getting Started with Anaconda
NumPy
Pandas
Matplotlib
Sklearn
Seaborn
Section-3
Correlation Analysis and Feature Selection
Linear Regression with Scikit-Learn
Evaluate Regression Model Performance
Dealing with Non-linear Relationships
Feature Importance
Data Preprocessing
Introduction to Classification
Logistic Regression
Confusion Matrix
Section-4
Support Vector Machine (SVM) Concepts
Support Vector Regression
Section-5
Training and Visualizing a Decision Tree
Tree Regression, Regularization and Over Fitting
Matplotlib
Sklearn
Seaborn
Section-6
Ensemble Learning Methods Introduction
Random Forests and Extra-Trees
Project
Section-7
kNN Introduction
Project
Section-8
Dimensionality Reduction Concept
PCA Introduction
Project
Section-9
Introduction to Neural Networks
Neural Network Architecture Foundations of Deep Learning Binary Classification Problem Natural Language Processing
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