AI & Machine Learning Masterclass

Part I: Python & Data Science Foundations

Module 1: Python for Data Science Duration: 2 Weeks

Advanced Python programming: Iterators, Generators, and OOP.
Mastering NumPy for high-performance multidimensional array processing.
Data manipulation, cleaning, and filtering using Pandas DataFrames.

Module 2: Exploratory Data Analysis (EDA) Duration: 2 Weeks

Handling missing values, outliers, and feature scaling.
Data Visualization using Matplotlib, Seaborn, and Plotly.
Statistical foundations: Probability distributions, Hypothesis testing, and Correlation.

Part II: Classical Machine Learning

Module 3: Supervised Learning (Regression & Classification) Duration: 4 Weeks

Linear and Multiple Regression, Gradient Descent optimization.
Logistic Regression, Support Vector Machines (SVM), and K-Nearest Neighbors.
Decision Trees, Random Forests, and Ensemble Methods (XGBoost, Gradient Boosting).
Model evaluation: Confusion Matrix, ROC-AUC, Precision, and Recall.

Module 4: Unsupervised Learning & Feature Engineering Duration: 3 Weeks

Clustering algorithms: K-Means, Hierarchical Clustering, and DBSCAN.
Dimensionality Reduction using Principal Component Analysis (PCA).
Feature encoding, selection techniques, and hyperparameter tuning using GridSearch.

Part III: Deep Learning & Generative AI

Module 5: Neural Networks & Computer Vision Duration: 4 Weeks

Introduction to Artificial Neural Networks (ANN), Backpropagation, and Activation Functions.
Building deep learning models using TensorFlow and Keras.
Convolutional Neural Networks (CNNs) for image classification and object detection.

Module 6: NLP & LLM Architecture Duration: 3 Weeks

Natural Language Processing: Text preprocessing, TF-IDF, and Word Embeddings (Word2Vec).
Recurrent Neural Networks (RNNs), LSTMs, and Sequence modeling.
Introduction to Transformer architecture and fine-tuning Large Language Models (LLMs).
Model Deployment via Flask/FastAPI and cloud hosting.

About the Mentor

Susheel Singh

Senior Machine Learning Engineer

With extensive experience building predictive models and deploying AI solutions in production environments, our lead mentor demystifies complex mathematics into practical code. The curriculum is designed to prevent "black box" thinking; you won't just import libraries, you will understand the mathematical intuition behind the algorithms. Learn the exact methodologies used by top tech companies to train, optimize, and deploy robust AI models.

Enrollment Options

Data Scientist Track

₹ 30,000

Master Data & Predictive Modeling.

Access to Part I & II (EDA & ML)
Extensive Kaggle Dataset Practice
Predictive Regression Project
Certificate of Completion

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Advanced Tech

AI Engineer Track

₹ 50,000

The complete Machine & Deep Learning suite.

Everything in the Data Scientist track
Access to Part III (Deep Learning & NLP)
Computer Vision Capstone Project
AI Model Deployment Guide
Technical Interview Prep & Math Review

Join AI Track

Frequently Asked Questions

Do I need a strong background in Mathematics?

A basic understanding of high-school algebra and statistics is helpful, but not strictly required to start. We cover the necessary mathematical intuition (like probability, matrices, and derivatives) as we introduce each algorithm, focusing more on practical Python implementation.

Do I need to know Python before joining?

Yes, foundational knowledge of Python (variables, loops, functions) is expected. However, Module 1 provides a rapid refresher on advanced Python concepts specifically geared toward data science (like list comprehensions and OOP).

Does my laptop need a powerful GPU?

For Part I and Part II (Classical ML), a standard modern laptop is perfectly fine. For Part III (Deep Learning), while a GPU helps, we will teach you how to use free cloud-based resources like Google Colab, which provide access to powerful GPUs for training neural networks.

Will we build real-world portfolios?

Absolutely. The course is highly project-driven. You will build end-to-end projects such as real estate price predictors, sentiment analysis engines, and image classification models, which you can showcase on your GitHub profile.

Master Artificial Intelligence

Predictive Machine Learning

Deep Learning & Generative AI