Day 1Introduction to AI, ML, and DL

Day 2Fundamentals of Machine Learning

Day 3Introduction to Python and NumPy

Day 4Exploratory Data Analysis (EDA)

Day 5Linear Regression

Day 6Logistic Regression

Day 7Decision Trees

Day 8Random Forests

Day 9Support Vector Machines (SVMs)

Day 10K-Nearest Neighbors (KNN)

Day 11Clustering Algorithms

Day 12Dimensionality Reduction

Day 13Introduction to Deep Learning

Day 14Convolutional Neural Networks (CNNs)

Day 15Recurrent Neural Networks (RNNs)

Day 16Generative Adversarial Networks (GANs)

Day 17Reinforcement Learning

Day 18Unsupervised Learning Techniques

Day 19Model Evaluation and Optimization

Day 20Intro to Natural Language Processing

Day 21Text Preprocessing & Feature Engineering

Day 22Classical NLP Algorithms

Day 23Deep Learning for NLP

Day 24Named Entity Recognition (NER)

Day 25Text Generation

Day 26Question Answering

Day 27Dialogue Systems and Chatbots

Day 28Multilingual NLP

Day 29NLP Applications in the Real World

Day 30Ethical Considerations in NLP