Month 1: Introduction to Python for AI and ML
Introduction to Python programming language
00:00
Python libraries for AI and ML (NumPy, Pandas)
00:00
Basic data manipulation and preprocessing techniques
00:00
Month 2: Machine Learning Fundamentals
Introduction to machine learning concepts and algorithms
00:00
Supervised learning (linear regression, logistic regression, decision trees)
00:00
Model evaluation and validation techniques
00:00
Month 3: Advanced Machine Learning Techniques
Month 3: Advanced Machine Learning Techniques
Unsupervised learning (clustering, dimensionality reduction)
00:00
Ensemble methods (bagging, boosting)
00:00
Support vector machines (SVM)
00:00
Month 4: Deep Learning and Neural Networks
Introduction to neural networks
00:00
Feedforward neural networks and backpropagation
00:00
Convolutional Neural Networks (CNN) for image recognition
00:00
Recurrent Neural Networks (RNN) for sequence data
00:00
Month 5: Natural Language Processing (NLP)
Introduction to NLP concepts
00:00
Text preprocessing and tokenization
00:00
Sentiment analysis and text classification
00:00
Named Entity Recognition (NER) and text summarization
00:00
Month 6: Reinforcement Learning
Introduction to reinforcement learning concepts
00:00
Markov decision processes and Q-learning
00:00
Deep Q-networks (DQN)
00:00
Policy gradients and actor-critic methods
00:00
Month 7: Computer Vision
Image processing techniques
00:00
Object detection and tracking
00:00
Object detection and tracking
00:00
Image segmentation and instance segmentation
00:00
Introduction to Generative Adversarial Networks (GANs)
00:00
Month 8: Advanced Topics in AI and ML
Time series analysis and forecasting
00:00
Recommendation systems
00:00
Transfer learning and pre-trained models
00:00
Model interpretation and explainability
00:00
Month 9: Capstone Project and Deployment
Undertake a project applying AI/ML techniques
00:00
Build a deployable AI/ML model or system
00:00
Present the project findings and insights
00:00
Sign In 
