Machine Learning

Introduction to Machine Learning

Hands-on with basic Python libraries (NumPy, Pandas, Matplotlib, Scikit-learn).

Data Preprocessing

Implement data preprocessing using Scikit-learn.

Exploratory Data Analysis (EDA)

EDA on a real-world dataset using Pandas, Matplotlib and Seaborn.

Supervised Learning — Regression

Build and evaluate regression models.

Supervised Learning — Classification

Implement classification models on datasets.

Model Evaluation & Optimization

Evaluate models and optimize hyperparameters using GridSearchCV.

Unsupervised Learning

Implement clustering algorithms.

Feature Engineering & Feature Selection

Feature selection using SelectKBest, PCA.

Deep Learning Basics

Implement a simple neural network using TensorFlow/Keras.

Convolutional Neural Networks (CNNs)

Build a CNN model using Keras.

Recurrent Neural Networks (RNNs)

Implement an LSTM model.

Natural Language Processing (NLP)

Implement text classification using NLP models.

Reinforcement Learning

Implement a simple RL agent using OpenAI Gym.

Time Series Forecasting

Forecast stock prices using time series models.

Explainable AI (XAI)

Apply explainability techniques to a trained model.

AutoML & Model Selection

Use AutoML frameworks like TPOT, H2O AutoML.

Model Deployment

Deploy a trained model as an API.

Edge AI & On-Device ML

Deploy a TensorFlow Lite model on a mobile device.

AI Ethics & Bias

Analyze bias in a trained ML model.

Project Presentation & Wrap-up

Showcase individual and group projects.