Course description

This course is your full journey into the universe of AI, Machine Learning, and Data Science — a path where logic meets intuition and numbers turn into stories.

We start with the foundations: statistics, probability, linear algebra, and optimization — the math that quietly powers every ML model. You’ll learn the art of understanding data through descriptive and inferential statistics, hypothesis testing, distributions, and the iconic Central Limit Theorem.

From there, we shift into machine learning: KNN, SVM, Naive Bayes, Decision Trees, Random Forests, PCA, and hands-on time series forecasting. You’ll explore NLP with tokenization, Word2Vec, transformers, BERT, and even peek into the soul of GPT.

But learning ML isn’t just algorithms — it’s also about managing data, version control, experiment tracking, dashboards, and practical deployment. So you'll work with MongoDB, Git/GitHub, Power BI, and Tableau to handle real datasets like a pro.

Finally, you’ll enter the creative frontier of Generative AI — GANs, prompt engineering, LLMs, and building your own chatbot.

By the end, you don’t just learn AI… you start speaking its language.    

What will i learn?

• Understand and apply core statistics & probability concepts
• Work confidently with matrices, vectors & optimization basics
• Perform data cleaning, preprocessing & visualization
• Build machine learning models in Python
• Apply NLP techniques for text analytics
• Forecast trends using ARIMA & other time-series models
• Analyze business data using Power BI & Tableau
• Use MongoDB for storing and querying data
• Manage projects using Git & GitHub
• Build and fine-tune Generative AI workflows
• Design effective prompts for modern LLMs
• Create ML-driven solutions ready for real-world deployment

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