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AI & Machine Learning
Build intelligent applications with AI and machine learning. From fundamentals to advanced topics including LLMs, neural networks, and practical AI integration.
16 Modules
Video Tutorials
AI Projects
Course Curriculum
Module 1: Python for AI/ML
Master Python programming essentials for AI and machine learning development.
- • Python Fundamentals
- • NumPy and Pandas
- • Data Visualization (Matplotlib, Seaborn)
- • Jupyter Notebooks
Module 2: Machine Learning Fundamentals
Learn core ML concepts, algorithms, and model evaluation techniques.
- • Supervised vs Unsupervised Learning
- • Linear & Logistic Regression
- • Decision Trees and Random Forests
- • Model Evaluation Metrics
Module 3: Deep Learning with TensorFlow
Build neural networks and deep learning models with TensorFlow.
- • Neural Network Basics
- • TensorFlow and Keras
- • Convolutional Neural Networks (CNNs)
- • Recurrent Neural Networks (RNNs)
Module 4: PyTorch for Deep Learning
Master PyTorch for research and production deep learning applications.
- • PyTorch Fundamentals
- • Building Custom Models
- • Transfer Learning
- • Model Optimization
Module 5: Large Language Models (LLMs)
Work with modern LLMs like GPT, Claude, and open-source alternatives.
- • Understanding Transformers
- • OpenAI API Integration
- • Prompt Engineering
- • Fine-tuning LLMs
Module 6: Natural Language Processing
Process and analyze text data with modern NLP techniques.
- • Text Preprocessing
- • Sentiment Analysis
- • Named Entity Recognition
- • Text Generation
Module 7: Computer Vision
Build applications that can see and understand images and videos.
- • Image Classification
- • Object Detection (YOLO, R-CNN)
- • Image Segmentation
- • Face Recognition
Module 8: AI Integration & Deployment
Deploy AI models to production and integrate with applications.
- • Model Serving (FastAPI, Flask)
- • Cloud AI Services (AWS, Azure, GCP)
- • Model Monitoring
- • MLOps Best Practices