Ai & machine learning
In the age of intelligent systems, AI literacy is the new career currency. Don't just consume technology, build it. Gain hands-on skills to design, train, and deploy real-world AI solutions, securing your place in the most transformative industry of the decade.
What You'll Learn
- Fundamentals of Python for Data Science
- Statistical Modeling and Data Analysis
- Supervised and Unsupervised Machine Learning
- Deep Learning and Neural Networks (DNNs, CNNs, RNNs)
- Natural Language Processing (NLP) and Generative AI
- Model Evaluation, Tuning, and Optimization
- MLOps (Machine Learning Operations) and Deployment
Who This Course is For
- Complete Beginners: Individuals with a strong aptitude for math and logic who want to begin a career as a Data Scientist or Machine Learning Engineer.
- Analysts and Statisticians: Professionals seeking to transition their mathematical skills into practical, code-driven AI and ML applications.
- Software Developers: Engineers looking to integrate advanced predictive and generative capabilities into applications.
- Students & Graduates: Those aiming to build a specialized, high-impact portfolio for entry-level roles in the AI field.
Prerequisites
- A strong foundation in Algebra and basic Calculus is highly recommended (concepts will be reviewed).
- Familiarity with fundamental programming concepts (loops, functions, variables).
- A commitment to rigorous mathematical and statistical thinking.
- A reliable computer and internet connection for online classes.
What's Included
This course features a robust hybrid learning model:
- Physical Classes: 3 days per week — hands-on coding, collaborative projects, and core lectures.
- Online Classes: 2 days per week — code reviews, debugging, Q&A, and deep dives.
- Physical Weekend Sessions: Admits students who can only attend weekend classes - Saturdays and Sundays.
- Course Materials
- Code Mentorship
- Portfolio Development
- Certificate of Completion
Tools & Technologies You Will Use
- Programming Language: Python
- Core Libraries: NumPy, Pandas, Matplotlib, Scikit-learn
- Deep Learning Frameworks: TensorFlow, Keras, PyTorch
- Development Environment: Jupyter Notebooks/Labs, Google Colab
- Cloud & MLOps: AWS SageMaker, Google Vertex AI, or similar cloud tools for deployment
Capstone Project
Capstone Project
End-to-End Predictive or Generative AI System
You will design, build, and deploy a complete AI application based on a selected domain (or instructor-approved proposal), demonstrating mastery from data preparation to production.