Definition of Machine Learning

Types of Machine Learning (Supervised Learning, Unsupervised Learning, Reinforcement Learning)

Applications of Machine Learning

Overview of Machine Learning Workflow

Linear Algebra Concepts

Calculus Basics

Probability and Statistics for Machine Learning

Linear Regression

Logistic Regression

Decision Trees and Random Forests

Support Vector Machines (SVM)

K-Nearest Neighbors (K-NN)

Model Evaluation Metrices (Accuracy, Precision, Recall, F1 Score)

Cross-Validation

Hyperparameter Tunning Techniques

K-Means Clustering

Hierarchical Clustering

Principal Component Analysis (PCA)

Association Rule Learning

Basics of Neural Networks

Perceptron and Multilayer Perceptron

Backpropagation Algorithm

Text Preprocessing

Bags of Words Model

Word Embeddings (e.g. Word2Vec, GloVe)

Sentiment Analysis

Image Processing Basics

Convolutional Neural Networks for Image Classifications

Object Detection

What is Machine Learning

Different Stages of ML Project

Supervised vs Unsupervised ML

Algorithms in Supervised and Unsupervised Learning

Introduction to Sklearn

Data Preprocessing

Scaling techniques

Training/Testing/Validation Datasets

Feature Engineering

How to Deal with Categorical Variables – Dummy Variables

Categorical Embedding

Basics of Reinforcement Learning

Markov Decision Processes

Q-Learning and Deep Q Networks (DQN)

Model Development Strategies

Integration and Web Applications

Model Monitoring and Maintanance

Bias and Fairness in Machine Learning

Ethical Considerations in AI

Responsible AI Practices