Advanced Course

AI/ML with Python

Master artificial intelligence and machine learning using Python, TensorFlow, and scikit-learn. Build intelligent systems and predictive models.

Limited offer2k/mo2.5k/mo
18 weeks
Advanced Level
Mode Online

Course Roadmap

1Python Fundamentals for AI/ML (Weeks 1-3)

  • Python programming essentials and advanced concepts
  • NumPy and Pandas for data manipulation
  • Matplotlib and Seaborn for data visualization
  • Jupyter Notebooks and development environment setup

2Mathematics for Machine Learning (Weeks 4-6)

  • Linear algebra: vectors, matrices, and eigenvalues
  • Statistics and probability theory
  • Calculus for optimization algorithms
  • Information theory and entropy

3Machine Learning Fundamentals (Weeks 7-10)

  • Supervised learning: regression and classification
  • Unsupervised learning: clustering and dimensionality reduction
  • Model evaluation and cross-validation techniques
  • Scikit-learn library and practical implementations

4Deep Learning with TensorFlow (Weeks 11-14)

  • Neural networks architecture and backpropagation
  • Convolutional Neural Networks (CNNs) for image processing
  • Recurrent Neural Networks (RNNs) for sequence data
  • TensorFlow and Keras framework mastery

5Advanced AI Topics (Weeks 15-17)

  • Natural Language Processing (NLP) and transformers
  • Computer vision and image recognition
  • Reinforcement learning fundamentals
  • Generative AI and large language models

6Capstone Project & Deployment (Week 18)

  • End-to-end ML project development
  • Model deployment using cloud platforms
  • MLOps and production best practices
  • Portfolio presentation and peer review

Hands-on Projects

Build 8+ real-world AI/ML projects

Industry Tools

Master TensorFlow, PyTorch, and scikit-learn

Career Ready

Portfolio and interview preparation

Prerequisites

Required Knowledge:

  • • Basic Python programming experience
  • • High school level mathematics
  • • Familiarity with programming concepts

Recommended:

  • • CS Fundamentals course completion
  • • Basic statistics knowledge
  • • Experience with data analysis