• Understanding the fundamentals of machine learning
• Types of machine learning (supervised, unsupervised, reinforcement learning)
• Python programming essentials for machine learning

• Data collection and cleaning
• Feature engineering and selection
• Data visualization and EDA techniques

• Linear and logistic regression
• Decision trees and random forests
• Support vector machines (SVM)
• Naive Bayes
• Model evaluation and performance metrics

• Clustering algorithms (K-Means, Hierarchical)
• Principal Component Analysis (PCA)
• Dimensionality reduction techniques
• Anomaly detection

• Introduction to artificial neural networks (ANN)
• Building deep learning models with TensorFlow and Keras
• Convolutional Neural Networks (CNN) for image data
• Recurrent Neural Networks (RNN) for sequential data

• Text preprocessing and tokenization
• Sentiment analysis
• Named Entity Recognition (NER)
• Building chatbots and language models

• Basics of reinforcement learning
• Q-Learning and Deep Q-Networks (DQN)
• Markov Decision Processes (MDP)
• Applications in game playing and robotics

• Model deployment techniques (e.g., Flask, Docker)
• Deploying machine learning models in cloud platforms (e.g., AWS, Azure)
• Handling large datasets and distributed computing

• Ethical considerations in machine learning
• Fairness and bias in algorithms
• Strategies to mitigate bias in models

• Resume building and interview preparation
• Effective communication in a technical role
• Time management and project planning
• Building a professional online presence

• Work on real-world machine learning projects
• Apply machine learning techniques to solve practical problems
• Collaborate on a capstone project with guidance from mentors

• Image Classification for Medical Diagnosis
• Recommendation System
• Sentiment Analysis for Social Media
• Chatbot Development
• Handwriting Recognition