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6-Week Intensive Course • 40% Concepts + 60% Hands-on

Artificial Intelligence & Machine Learning

Master Python foundations, NumPy, Pandas, 8 core Supervised ML Algorithms, Unsupervised Clustering, PCA, Deep Learning with TensorFlow & Keras, Generative AI, and Flask/Streamlit Model Deployment.

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6 Weeks
30 Total Live Sessions (Mon-Fri)
12+ Projects
Real-World Capstones & Case Studies
AI & ML Logo
AI & Machine Learning
Live Interactive Online Training with 1-on-1 Mentorship & Industry Portfolio
Python NumPy & Pandas Scikit-Learn TensorFlow Generative AI Streamlit
Why This Course?

Designed for Next-Gen Tech Careers

A balanced 40% conceptual and 60% hands-on project-driven approach that prepares you for real-world AI engineering roles.

Python & Math Foundations
Start with OOP, Linear Algebra, Vectors, Matrices, Statistics, Probability, and NumPy numerical computing.
Pandas & Data Viz
Clean raw datasets, handle missing values, and build interactive dashboards using Matplotlib and Plotly.
8 Supervised ML Models
Linear & Multiple Regression, Logistic Regression, KNN, Decision Trees, Random Forest, Naive Bayes, and SVM.
Unsupervised & Deep Learning
K-Means, Hierarchical Clustering, DBSCAN, PCA, Neural Networks, Perceptrons, and TensorFlow Keras models.
Generative AI & Prompting
LLMs, Prompt Engineering, ChatGPT, AI Coding Assistants, Productivity Workflows, and Responsible AI.
Streamlit & Flask Deployment
Save models using Pickle/Joblib, build live web applications, and showcase capstone projects on GitHub.
Comprehensive Syllabus

6-Week Day-by-Day Curriculum

Explore the complete 30-session roadmap structured week by week.

Week 1 — Python + Mathematics + NumPy Foundations
Days 1 to 5 • Establishing programming, math, statistics, and array processing basics.
Day 1 Python Refresher for AI
Topics Covered
  • Variables & Data Types
  • Operators & Conditional Statements
  • Loops & Functions
Hands-on Practical
  • Basic Python programs
  • Student Marks System
Day 2 Python Programming for AI
Topics Covered
  • Object-Oriented Programming (OOP) Concepts
  • File Handling (Read/Write)
  • Exception Handling
Hands-on Practical
  • Employee Management System
  • Banking Application
Day 3 Mathematics for AI & ML
Linear Algebra
  • Linear Algebra Basics
  • Vectors & Matrices
Statistics
  • Mean, Median, Mode
  • Variance & Standard Deviation
Day 4 Probability + NumPy Mathematics
Topics Covered
  • Probability fundamentals
  • Statistical & Matrix calculations
  • Vector calculations
Practical
  • NumPy-based mathematical operations
  • NumPy calculations
Day 5 NumPy Fundamentals & Mini Project
NumPy Concepts
  • NumPy Arrays, Indexing & Slicing
  • Reshaping, Broadcasting, Universal Functions
Mini Project
  • Numerical Data Analysis using NumPy
Week 2 — Pandas + Data Visualization + ML Foundations
Days 6 to 10 • Master dataset cleaning, visualization dashboards, and EDA.
Day 6 Pandas Basics
Topics Covered
  • Series & DataFrames
  • Reading CSV files & Inspection
  • Indexing & Selecting rows/columns
Hands-on
  • Data manipulation exercise
Day 7 Data Cleaning with Pandas
Topics Covered
  • Cleaning Data & Missing Values
  • Filtering, GroupBy
  • Merge & Join operations
Practical
  • Real-world dataset cleaning pipeline
Day 8 Sales Data Analysis Project
Project Activities
  • Load sales CSV & handle missing values
  • Filter & group records
  • Merge datasets to generate business insights
Deliverable
  • Complete Sales Analysis Report Notebook
Day 9 Matplotlib Visualizations
Charts Covered
  • Line Chart & Bar Chart
  • Histogram, Pie Chart, Scatter Plot
Practical
  • Comparison charts & distribution analysis
Day 10 Plotly + COVID Dashboard Project
Plotly Topics
  • Interactive Visualizations with Plotly
  • Interactive Dashboard Design
Project
  • COVID-19 Interactive Data Dashboard
Week 3 — Machine Learning + Data Preprocessing
Days 11 to 15 • ML concepts, feature engineering, scaling, and preprocessing pipelines.
Day 11 AI & Machine Learning Fundamentals
Core Concepts
  • Artificial Intelligence vs Machine Learning vs Deep Learning
  • Supervised, Unsupervised & Reinforcement Learning
Day 12 Machine Learning Workflow
End-to-End Workflow
  • Problem Definition → Data Collection → Data Analysis
  • Preprocessing → Feature Engineering → Model Training
  • Prediction → Evaluation → Deployment
Key Concepts
  • Features, Labels, Training & Testing sets, Models
Day 13 Data Preprocessing
Topics Covered
  • Train-Test Split & Feature Engineering
  • Handling Missing Values
  • Label Encoding & One Hot Encoding
Practical
  • Titanic Dataset Preprocessing
Day 14 Feature Scaling & Outliers
Topics Covered
  • StandardScaler vs MinMaxScaler
  • Outlier Detection & Scaling decisions
Practical
  • Titanic feature scaling pipeline
Day 15 Complete Preprocessing Pipeline
Pipeline Steps
  • Dataset → Cleaning → Missing Values → Encoding
  • Feature Engineering → Train-Test Split → Feature Scaling
Mini Project
  • Titanic Survival Prediction — Data Preparation
Week 4 — Supervised Machine Learning
Days 16 to 20 • Intensive coverage of 8 Core Supervised Learning Algorithms.
Day 16 Linear Regression
Topics
  • Simple Linear Regression concept
  • Training, Prediction, Coefficients & Intercept
Practical Project
  • House Price Prediction Model
Day 17 Multiple Regression
Topics
  • Multiple features & feature relationships
  • Model training & prediction
Dataset
  • California Housing Dataset
Day 18 Classification & Logistic Regression
Topics
  • Binary classification & decision boundaries
  • Prediction probabilities
Practical Project
  • Student Performance Prediction
Day 19 KNN + Decision Tree
K-Nearest Neighbors
  • Distance metrics, Neighbors & K value
Decision Tree
  • Nodes, Splits, Gini Impurity, Tree prediction
Day 20 Random Forest + Naive Bayes + SVM
Algorithms
  • Random Forest (Ensemble Learning)
  • Naive Bayes (Probability Classification)
  • Support Vector Machine (Hyperplane & Boundary)
Practical Dataset
  • Diabetes Prediction System
Week 5 — Unsupervised ML + Evaluation + Deep Learning + GenAI
Days 21 to 25 • Clustering, PCA, Neural Networks, TensorFlow/Keras, and Generative AI.
Day 21 Unsupervised Learning (K-Means)
Topics
  • Clustering concepts, K-Means & Centroids
  • Choosing K (Elbow method) & Cluster visualization
Practical
  • Customer Segmentation Project
Day 22 Advanced Clustering & PCA
Algorithms
  • Hierarchical Clustering & Dendrograms
  • DBSCAN (Density-Based Clustering)
  • PCA (Dimensionality Reduction)
Day 23 Model Evaluation & Validation
Classification Metrics
  • Accuracy, Precision, Recall, F1 Score
  • Confusion Matrix & ROC Curve
Validation
  • Cross Validation
  • Bias & Variance Trade-off
Day 24 Deep Learning with TensorFlow & Keras
Deep Learning
  • Neural Networks & Perceptrons
  • Activation Functions & Neural Architecture
  • TensorFlow Basics & Keras Sequential Model
Practical
  • Handwritten Digit Recognition
Day 25 Generative AI & LLMs
GenAI Concepts
  • Large Language Models (LLMs) & ChatGPT
  • Prompt Engineering principles
  • AI Coding Assistants & Responsible AI
Hands-on
  • Prompt engineering exercises
  • AI-assisted coding workflows
Week 6 — Model Deployment + Capstone Project
Days 26 to 30 • Build Streamlit/Flask apps, deploy models, and present capstones.
Day 26 Model Deployment (Flask & Streamlit)
Deployment Workflow
  • Saving Models with Pickle & Joblib
  • Flask & Streamlit Web UI Basics
  • Deploying Live Prediction APIs
Day 27 Capstone Planning & Dataset Selection
Activities
  • Problem Definition & Dataset Selection
  • EDA, Feature Selection & Project Architecture
Day 28 Capstone Development
Development Steps
  • Data Cleaning → Preprocessing → Model Training
Day 29 Model Evaluation & Web Application
Tasks
  • Model improvement & evaluation metrics
  • Building Streamlit interface for live predictions
Day 30 Final Capstone Presentation & Deliverables
Deliverables
  • Jupyter Notebook / Google Colab code
  • Dataset & Trained Model files
  • Streamlit/Flask Web App & GitHub Repository
Hands-On Experience

12 Industry Capstone Projects

Students select and build real-world machine learning applications during the course.

1
House Price Prediction
2
Employee Salary Prediction
3
Fake News Detection System
4
Email Spam Filter Classifier
5
Loan Approval Prediction
6
Customer Churn Prediction
7
Student Performance Predictor
8
Movie Recommendation System
9
Sales Demand Forecasting
10
Face Mask Detection App
11
Smart Crop Recommendation
12
Heart Disease Risk Predictor
Tech Stack

Tools & Libraries You Will Master

Gain mastery in industry-standard libraries, frameworks, and deployment environments.

Python
Jupyter Notebook
Google Colab
NumPy
Pandas
Matplotlib
Plotly
Scikit-Learn
TensorFlow
Keras
Streamlit
Flask
GitHub

Ready to Transform Your Career with AI & ML?

Join live interactive classes, work on real datasets, and launch your AI portfolio in 6 weeks.

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