Intermediate Pack- Python, ML, DL & GenAI

Description

Intermediate Pack- Python, ML, DL & GenAI

🧭 Who is this for?

• Final-year students, graduates, and freshers looking to enter the AI field

• Working professionals aiming to shift into data science or AI roles

• Entrepreneurs and freelancers developing AI-powered applications

• Researchers and enthusiasts eager to explore GenAI & LLMs

🗂️ Course Modules Overview

🔹 Module 1: Python Programming for AI

Objective: Build a strong programming foundation in Python tailored for AI and data science.

Topics Covered:

• Python Basics: Variables, Data Types, Functions, Loops

• Data Structures: Lists, Tuples, Dictionaries, Sets

• File Handling and Exception Handling

• Libraries: NumPy, Pandas for data manipulation

• Matplotlib & Seaborn for data visualization

• Object-Oriented Programming

• Outcome: Confident coding skills and data handling capabilities in Python.

🔹 Module 2: Machine Learning (ML)

Objective: Understand and implement supervised and unsupervised ML models.

Topics Covered:

• ML Workflow: Data Preprocessing, Feature Engineering

• Supervised Learning:

  • Linear & Logistic Regression

  • Decision Trees & Random Forest

  • KNN, Naive Bayes, SVM

• Unsupervised Learning:

  • K-Means Clustering, Hierarchical Clustering

  • Dimensionality Reduction (PCA, t-SNE)

• Model Evaluation: Confusion Matrix, ROC-AUC, Precision, Recall

• Hyperparameter Tuning with Grid Search and Cross Validation

• Tools: Scikit-learn, Jupyter, Google Colab

• Case Study: Predicting churn, housing prices, fraud detection

Outcome: Ability to build, evaluate, and deploy machine learning models in real-world scenarios.

🔹 Module 3: Deep Learning (DL)

Objective: Dive into the foundations and applications of neural networks.

Topics Covered:

• Introduction to Neural Networks and Deep Learning

• ANN: Architecture, Forward & Backpropagation

• Activation Functions, Loss Functions

• Optimization: Gradient Descent, Adam

• CNN (Convolutional Neural Networks) for Image Recognition

• RNN & LSTM for Sequence Data (Time Series, Text)

• Frameworks: TensorFlow & Keras

• Hands-on Projects: Digit recognition, sentiment analysis, image classification

Outcome: Design, train, and deploy deep learning models using TensorFlow/Keras.

🔹 Module 4: Generative AI & LLMs

Objective: Explore the cutting-edge world of AI that creates — from text, code, and images to intelligent agents.

Topics Covered:

• Introduction to GenAI & Transformer Architecture

• Understanding Large Language Models (LLMs)

• Prompt Engineering:

  • Zero-shot, Few-shot, Chain of Thought

  • Prompt Templates & Role-based Prompts

• Fine-tuning LLMs (Intro) and using APIs

• Toolkits:

  • OpenAI (ChatGPT), Google Gemini, Claude

  • LangChain for chaining prompts and building GenAI workflows

  • Agents, Memory, and Vector DBs (Chroma/FAISS)

• Projects:

  • AI Chatbot

  • Resume Writer

  • SQL Generator from Natural Language

  • Auto PPT Creator using AI

• Bonus: Vision models, voice-to-text tools, and multimodal GenAI

Outcome: Build real-world applications using GenAI tools and integrate LLMs into your Python projects.

👨‍🏫 Add-Ons & Extras

• Weekly doubt clarification sessions (Live with mentor)

• Resume, LinkedIn & GitHub profile reviews

• Industry mock interviews & career guidance

📅 Course Duration & Format

• Pedagogy: Theory + Hands-on Labs + Projects + Case Studies

• Access: Lifetime access to course content and updates

🎯 Skills You Will Gain

• Python Programming

• Data Preprocessing & Feature Engineering

• ML & DL Model Building

• GenAI Prompt Engineering

• API Integration & Deployment