About This Course
This program takes you from the fundamentals of Python and math for machine learning to building real‑world AI solutions. You will understand how modern algorithms work, how to prepare and explore data, and how to evaluate and improve models.
As you progress you will work with supervised and unsupervised learning, deep learning, computer vision and basic NLP. By the end of the course you will have implemented several end‑to‑end projects and will be ready to contribute to AI & ML initiatives in industry.
Syllabus Covered
Module 1
Foundations of AI & Python
- Introduction to AI, ML and industry use‑cases
- Python essentials for data & ML (NumPy, Pandas, Matplotlib)
- Statistics & linear algebra for ML intuition
- Exploratory Data Analysis (EDA) on real datasets
Module 2
Core Machine Learning Algorithms
- Regression & classification models (LR, k‑NN, trees, forests)
- Model evaluation & cross‑validation
- Feature engineering, scaling and handling missing data
- Unsupervised learning: clustering & dimensionality reduction
Module 3
Deep Learning & Neural Networks
- Neural network basics & backpropagation
- Building models with TensorFlow / Keras
- Image classification with CNNs
- Intro to NLP and word embeddings
Module 4
Industry Applications & Capstone
- Case studies from finance, healthcare and e‑commerce
- End‑to‑end ML pipeline: data, training, deployment
- Serving models via simple REST APIs & MLOps basics
- Capstone project: design, build and present an AI solution
