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:English / Hindi
Price:₹25,000₹28,000
View Curriculum

Course Syllabus & 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

Hands-On 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

Hands-On 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

Hands-On 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

Hands-On 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

Hands-On 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

Hands-On Projects:

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

What You'll Learn

  • 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

Requirements & Prerequisites

  • •Basic Python programming knowledge (variables, loops, functions)
  • •Understanding of basic mathematics (linear algebra, calculus fundamentals)
  • •Laptop with internet connection
  • •Eagerness to learn applied AI/ML concepts in an intensive format

Certification

Industry-Recognized Credential

Verified Certificate of Completion from The Geek Institute of Cyber Security upon successful completion of the course curriculum, practical labs, and 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.

Ready to start your tech journey?

Enroll now in Applied AI and Machine Learning and gain practical skills, live industry mentorship, and verified certification.

Price:₹25,000₹28,000

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