Course Overview
Learn how to leverage AI algorithms and data to create intelligent modern solutions. This course covers mathematical foundations, supervised and unsupervised ML, neural networks, Natural Language Processing (NLP), and deploying AI models to production.
Key Learning Highlights
- Python for Data Science (NumPy, Pandas, Matplotlib & Seaborn)
- Core ML Algorithms: Regression, Classification, Clustering & Ensembles
- Deep Learning with TensorFlow & Keras (CNNs, RNNs, Transformers)
- Natural Language Processing (NLP) & Generative AI Prompt Engineering
- Model Deployment with FastAPI, Streamlit, and Cloud Services
Curriculum & Key Modules
Module 125 Hours
Python for AI & Mathematical Foundations
- •Python OOP, Data Structures, NumPy Arrays & Vectorization
- •Data Manipulation and Cleaning with Pandas
- •Linear Algebra, Probability & Statistics
Module 235 Hours
Machine Learning Algorithms & Scikit-Learn
- •Supervised Learning: Linear/Logistic Regression, Random Forests, XGBoost
- •Unsupervised Learning: K-Means, Clustering, PCA
- •Model Evaluation & Cross-Validation
Module 340 Hours
Deep Learning & Neural Networks
- •Neural Networks, Backpropagation & Optimizers
- •Computer Vision with CNNs
- •Sequence Models with RNNs & LSTMs
Module 430 Hours
NLP, Large Language Models & AI Deployment
- •Text Embeddings, Transformers & Prompt Engineering
- •LangChain Basics & LLM Integration
- •Deploying AI Apps with Streamlit & FastAPI
Prerequisites & Eligibility
Basic knowledge of mathematics and foundational programming logic.