programData Science & AI/MLintermediate

Applied AI and Machine Learning

Become an AI/ML engineer in 6 months with practical model development, evaluation, and deployment experience.

Duration: 6 months
Mode: online/offline
Language: Hindi/English
View Curriculum

Curriculum

Module 1: Python & Data Foundations for AI/ML

2 weeks

Topics Covered:

  • Python Refresher for AI/ML
  • - Advanced Python (List comprehensions, Generators, Lambda functions)
  • - NumPy Arrays & Vectorized Operations
  • - Pandas Deep Dive (Data cleaning, GroupBy, Merge/Join)
  • - Handling Missing Values & Outliers
  • - Feature Engineering Techniques
  • - Exploratory Data Analysis (EDA) with Pandas & Seaborn
  • - Data Visualization (Matplotlib, Seaborn)
  • - SQL for ML Data Extraction Basics
  • - Working with Time Series Data

Projects:

  • Exploratory Data Analysis on Real-World Dataset
  • Data Cleaning & Feature Engineering Pipeline

Module 2: Machine Learning Foundations

4 weeks

Topics Covered:

  • Machine Learning Types & Workflow
  • Train-Test Split & Cross-Validation
  • Bias-Variance Tradeoff
  • Regression Algorithms:
  • - Linear Regression (Simple, Multiple)
  • - Ridge & Lasso Regression
  • - Decision Tree Regression
  • - Random Forest Regression
  • Classification Algorithms:
  • - Logistic Regression
  • - K-Nearest Neighbors (KNN)
  • - Support Vector Machines (SVM)
  • - Decision Tree Classification
  • - Random Forest Classification
  • Evaluation Metrics:
  • - Regression (MAE, MSE, RMSE, R²)
  • - Classification (Accuracy, Precision, Recall, F1-Score, ROC-AUC)
  • Hyperparameter Tuning (Grid Search, Randomized Search)
  • Feature Importance & Model Interpretation (SHAP, LIME)
  • Ensemble Methods (Bagging, Boosting, Stacking)
  • XGBoost, LightGBM
  • Handling Imbalanced Data (SMOTE)
  • Scikit-Learn Pipelines

Projects:

  • Customer Churn Prediction with Ensemble Models
  • House Price Prediction with Hyperparameter Tuning

Module 3: Unsupervised Learning & Deep Learning Basics

4 weeks

Topics Covered:

  • Clustering Algorithms:
  • - K-Means Clustering (Elbow Method, Silhouette Score)
  • - Hierarchical Clustering (Agglomerative)
  • - DBSCAN
  • Dimensionality Reduction:
  • - Principal Component Analysis (PCA)
  • - t-SNE for Visualization
  • Anomaly Detection (Isolation Forest)
  • Recommendation Systems:
  • - Collaborative Filtering (User-Based, Item-Based)
  • - Matrix Factorization (SVD)
  • Introduction to Neural Networks
  • Perceptron & Activation Functions (Sigmoid, Tanh, ReLU, Softmax)
  • Forward Propagation & Backpropagation
  • Gradient Descent Optimizers (SGD, Adam)
  • Building Artificial Neural Networks (ANN)
  • Regularization (Dropout, Batch Normalization, Early Stopping)
  • Convolutional Neural Networks (CNN):
  • - Convolution, Pooling, Flatten Layers
  • - Transfer Learning (Using Pre-trained Models)
  • - Image Classification
  • GPU Training with Google Colab

Projects:

  • Customer Segmentation for E-commerce
  • Movie Recommendation System
  • Image Classification with CNN & Transfer Learning

Module 4: Natural Language Processing (NLP)

3 weeks

Topics Covered:

  • NLP Fundamentals & Use Cases
  • Text Preprocessing:
  • - Tokenization, Lowercasing, Stop Words
  • - Stemming, Lemmatization
  • - Part-of-Speech (POS) Tagging
  • - Named Entity Recognition (NER)
  • Text Vectorization:
  • - Bag of Words (CountVectorizer)
  • - TF-IDF (Term Frequency-Inverse Document Frequency)
  • - Word Embeddings (Word2Vec, GloVe)
  • NLP Tasks:
  • - Text Classification (Spam Detection)
  • - Sentiment Analysis (VADER, ML Approaches)
  • Transformers & Hugging Face:
  • - Attention Mechanism
  • - BERT & GPT Basics
  • - Using Hugging Face Transformers Pipelines
  • spaCy & NLTK Libraries

Projects:

  • Sentiment Analysis of Product Reviews
  • Fake News Detection with NLP
  • Spam Email Classifier with Transformers

Module 5: Generative AI & Large Language Models (LLMs)

3 weeks

Topics Covered:

  • Introduction to Generative AI
  • How LLMs Work (GPT, Claude, Gemini, Llama)
  • Prompt Engineering:
  • - Zero-Shot, Few-Shot, Chain-of-Thought
  • - System Prompts & Role Prompting
  • - Handling Hallucinations
  • OpenAI API:
  • - Setup, Authentication
  • - Chat Completions, Function Calling
  • - Building Applications with GPT API
  • LangChain Framework:
  • - Chains, Agents, Tools, Memory
  • - Document Loaders & Text Splitters
  • - Retrieval Augmented Generation (RAG)
  • - Vector Databases (ChromaDB, Pinecone)
  • Open Source LLMs (Llama 3, Mistral)
  • Running LLMs Locally (Ollama)
  • Hugging Face for LLMs
  • Responsible AI & Ethics

Projects:

  • AI-Powered Document Q&A System (RAG + LangChain)
  • Customer Support Chatbot with GPT API
  • Content Summarization Tool

Module 6: MLOps, Deployment & Capstone

4 weeks

Topics Covered:

  • MLOps Fundamentals
  • Experiment Tracking (MLflow)
  • Deploying ML Models as APIs:
  • - Flask/FastAPI for Model Serving
  • - REST API Endpoints
  • - API Documentation (Swagger/OpenAPI)
  • Containerization with Docker Basics
  • Cloud Deployment:
  • - AWS (EC2, S3)
  • - Google Cloud (Cloud Run)
  • CI/CD Pipelines for ML (GitHub Actions)
  • Model Monitoring (Data Drift, Concept Drift)
  • Streamlit & Gradio for ML Demos
  • Capstone Project:
  • - Problem Definition & Business Context
  • - Data Collection & EDA
  • - Model Selection & Training
  • - Hyperparameter Tuning
  • - API Development & Deployment
  • - Dashboard Creation
  • - Final Presentation
  • Career Preparation:
  • - Portfolio Building
  • - Resume & LinkedIn Optimization
  • - Interview Preparation

Projects:

  • Deploy ML Model with FastAPI + Docker
  • Interactive ML Demo with Streamlit
  • Capstone: End-to-End ML Application

Learning Objectives

  • ✓Master supervised and unsupervised machine learning algorithms with Scikit-Learn
  • ✓Build and train deep learning models using TensorFlow/Keras (ANN, CNN)
  • ✓Apply NLP techniques including text preprocessing, vectorization, and transformers
  • ✓Deploy ML models as REST APIs using Flask/FastAPI and Docker
  • ✓Work with Large Language Models and Generative AI (GPT, LangChain, RAG)
  • ✓Build an end-to-end AI portfolio with 6+ production-ready projects

Why Choose Applied AI and Machine Learning?

The Applied AI and Machine Learning program is an intensive 6-month journey designed for professionals who already have basic programming knowledge and want to build practical AI/ML skills. This accelerated program focuses on hands-on application with real-world projects.

  • Complete AI Stack: ML, Deep Learning, NLP, Generative AI, MLOps
  • 6+ Real-World Projects: Build churn predictors, recommendation systems, chatbots, and Gen AI applications
  • Generative AI & LLMs: Dedicated module on GPT, LangChain, RAG, Vector Databases
  • Industry-Ready MLOps: Docker, FastAPI, cloud deployment, CI/CD
  • Portfolio Focus: Every module produces production-ready projects
  • Career Support: Resume building, mock interviews, and job referrals

Who Should Enroll?

  • Software Developers wanting to transition into AI/ML roles
  • Data Analysts wanting to upgrade to ML Engineer roles
  • Fresh Graduates with programming background seeking AI/ML careers
  • Working Professionals wanting to add AI/ML skills
  • Anyone with basic Python knowledge passionate about AI

Prerequisite: Basic Python programming knowledge required.

Note: This is an intensive bootcamp requiring 15-20 hours per week commitment.

What You'll Build

Machine Learning Projects

  • Customer Churn Prediction
  • House Price Prediction
  • Customer Segmentation
  • Movie Recommendation System

Deep Learning Projects

  • Image Classification with CNN & Transfer Learning

NLP Projects

  • Sentiment Analysis
  • Fake News Detection
  • Spam Email Classifier

Generative AI Projects

  • Document Q&A System (RAG + LangChain)
  • Customer Support Chatbot
  • Content Summarization Tool

MLOps Projects

  • ML Model Deployment with FastAPI + Docker
  • Interactive ML Demo with Streamlit
  • End-to-End ML Application (Capstone)

Career Opportunities

Entry-Level (₹5-9 LPA)

  • Junior Machine Learning Engineer
  • AI/ML Developer

Mid-Level (₹9-18 LPA)

  • Machine Learning Engineer
  • Data Scientist
  • NLP Engineer
  • MLOps Engineer

Senior-Level (₹18-30+ LPA)

  • Senior ML Engineer
  • AI/ML Lead

Top Hiring Companies

  • FAANG: Google, Amazon, Microsoft
  • Product: Flipkart, Swiggy, Zomato
  • AI Startups: OpenAI, Anthropic, Cohere
  • Consulting: Deloitte, EY, KPMG
  • Banks & Finance: JPMC, Goldman Sachs

Learning Experience

Learning Format

  • Duration: 6 months
  • Mode: Online/Offline
  • Schedule: 15-20 hours per week
  • 6 modules from ML foundations to Generative AI

Learning Methodology

  • 20% Theory & Concepts
  • 80% Hands-on Coding, Labs & Projects
  • Weekly assignments and coding exercises
  • Capstone project with mentor evaluation
  • Mock interviews and career preparation

Course Includes

  • Course Material: Notes, code notebooks, datasets, project templates
  • Cloud Credits: AWS/GCP credits for deployment
  • Assessments: Weekly quizzes and project evaluations
  • Certification: Applied AI & ML Certificate
  • Job Support: Resume, LinkedIn, GitHub portfolio, mock interviews
  • Community: Alumni network and peer support

Fee Structure

Course Fee: ₹28,000 (6 months)

Payment Options:

  1. One-time Payment: ₹25,000 (₹3,000 discount)
  2. Quarterly: ₹7,000 × 4
  3. Monthly: ₹4,667 × 6

Frequently Asked Questions

Is prior Python required?

Basic Python programming knowledge is required. We provide a 2-week refresher module covering Python for AI/ML. If you're completely new to Python, we recommend taking our Python Fundamentals course first.

Is this course fast-paced?

Yes, this is an intensive bootcamp designed for 15-20 hours per week. We cover essential AI/ML topics in a compressed format focused on job readiness.

What's the difference between this and the 10-month program?

The 6-month bootcamp is more intensive (15-20 hours/week) and covers core AI/ML topics for faster job readiness. The 10-month program has more depth, additional modules (Computer Vision, advanced Time Series), and more projects.

Will I learn Generative AI and LLMs?

Yes! Module 5 is dedicated to Generative AI, covering GPT, LangChain, RAG, Vector Databases, and building AI-powered applications.

Can I get a job after this course?

Yes! With 6+ projects, a deployed capstone, and career preparation module, you'll be well-prepared for ML Engineer and Data Scientist roles.

Is deep learning covered?

Yes. Module 3 covers ANN and CNN with transfer learning using TensorFlow/Keras. NLP and Generative AI modules also use deep learning architectures.

What if I don't have a powerful laptop?

We use Google Colab and Kaggle Notebooks which provide free GPU access. For deployment, we use cloud platforms with free tiers.

What if I fall behind?

We provide recorded lectures, mentor support, and flexible catch-up options. The program is designed for motivated learners who can commit 15-20 hours weekly.

Will I get a certificate?

Yes, you will receive a certificate of completion upon successfully finishing the program and capstone project.

Tags

Machine LearningDeep LearningNLPMLOpsGenerative AIPython

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