Artificial Intelligence (AI) Training
Learn AI from the basics in 60 days of live classes: Python, data analysis, Machine Learning, Deep Learning, Generative AI, RAG and AI agents, with 10 hands-on projects and a capstone.
Course highlights
Sujeet Sir
21 Years Exp.
Excel | VBA | Power BI | SQL | Python | AI
Data Analytics Expert & Corporate Trainer
- Sujeet Kumar Singh (MCA), founder of IPT Excel School since 2007
- Worked as MIS Executive and VBA Developer in 10+ companies
- Report consultant for 40+ organizations
What you will learn
AI, ML, DL and GenAI, real-world use cases and demos of popular AI tools.
Python basics, data structures, functions, files and NumPy.
Pandas, data cleaning, Matplotlib, Seaborn and EDA on real data.
Regression, classification, clustering and model evaluation.
Neural networks, Keras/TensorFlow and a CNN image classifier.
Prompt engineering, AI APIs and a Python chatbot with memory.
Embeddings, vector databases, Chat with your PDF and function-calling agents.
AI in Excel and Power BI, Streamlit apps, Git, ethics and a capstone project.
BENEFITS
- Build real AI and Machine Learning projects from scratch
- Learn Python, data analysis and ML step by step
- Use LLMs, RAG and AI agents in real apps
- Finish with a capstone project and certificate
YOUR GOAL
Become job-ready in AI: build, deploy and present your own AI projects with confidence.
WHO SHOULD LEARN
Students, working professionals, MIS and data staff and business owners who want to learn AI from the basics.
COURSE OUTLINE
⬇ Download PDFWeek 1 - AI Foundations
- Day 1 : What is AI, ML, DL, and GenAI? History and real-world examples
- Day 2 : AI use cases in business, education, and healthcare. Demo of popular AI tools
- Day 3 : Setting up the environment (Google Colab, Jupyter, VS Code)
- Day 4 : Python basics 1: variables, data types, operators, input/output
- Day 5 : Python basics 2: if/else, loops. Practice: 10 small programs
Week 2 - Python for AI
- Day 6 : Lists, tuples, dictionaries, sets
- Day 7 : Functions, modules, and error handling
- Day 8 : File handling (CSV, JSON, text)
- Day 9 : NumPy: arrays, indexing, operations
- Day 10 : NumPy practice and mini quiz. Assignment: a basic statistics calculator
Week 3 - Data Analysis
- Day 11 : Pandas: Series and DataFrames
- Day 12 : Loading, filtering, sorting, and grouping data
- Day 13 : Data cleaning: missing values, duplicates, outliers
- Day 14 : Matplotlib and Seaborn: charts and visualization
- Day 15 : Exploratory Data Analysis (EDA) on a real dataset. Mini project: Sales Data Analysis
Week 4 - Machine Learning Basics
- Day 16 : ML concepts: supervised vs. unsupervised, train/test split
- Day 17 : Math intuition: mean, variance, correlation, probability (no heavy formulas)
- Day 18 : Linear Regression
- Day 19 : Regression practice. Project: House Price Prediction
- Day 20 : Model evaluation: MAE, MSE, R²
Week 5 - Classification
- Day 21 : Logistic Regression
- Day 22 : Decision Trees and Random Forest
- Day 23 : KNN and Naive Bayes
- Day 24 : Confusion matrix, precision, recall, F1-score
- Day 25 : Project: Customer Churn Prediction
Week 6 - Advanced ML
- Day 26 : Overfitting, underfitting, and cross-validation
- Day 27 : Feature engineering and encoding
- Day 28 : Hyperparameter tuning (GridSearchCV)
- Day 29 : Clustering: K-Means
- Day 30 : Saving models (pickle/joblib). Mini project: Customer Segmentation
Week 7 - Deep Learning
- Day 31 : Neural networks: neurons, layers, activation functions
- Day 32 : Forward pass, loss, backpropagation (intuitive)
- Day 33 : Building a first neural network with Keras/TensorFlow
- Day 34 : CNNs for image data
- Day 35 : Project: Handwritten Digit or Cat-vs-Dog Classifier
Week 8 - Generative AI and LLMs
- Day 36 : How LLMs work: tokens, transformers, context window (simple)
- Day 37 : Prompt engineering 1: zero-shot, few-shot, role prompts
- Day 38 : Prompt engineering 2: structured outputs, chain-of-thought, prompt templates
- Day 39 : Using AI APIs from Python (keys, requests, responses)
- Day 40 : Mini project: a Python chatbot with conversation memory
Week 9 - Applied GenAI
- Day 41 : Embeddings and vector databases
- Day 42 : RAG concepts and architecture
- Day 43 : Building RAG, part 1: loading and chunking PDFs
- Day 44 : Building RAG, part 2: retrieval and answering
- Day 45 : Project: “Chat with your PDF” app
Week 10 - Agents and Automation
- Day 46 : AI agents: concept, tools, function calling
- Day 47 : Building a simple agent (calculator, web search, file reader)
- Day 48 : Workflow automation: email, reports, and spreadsheet automation with Python and AI
- Day 49 : No-code and low-code AI tools
- Day 50 : Image, audio, and video generation tools
Week 11 - AI for Productivity and Deployment
- Day 51 : AI in Excel and Power BI (formulas, insights, report summaries)
- Day 52 : Data analysis with AI assistants
- Day 53 : Building a UI with Streamlit
- Day 54 : Deploying an app (Streamlit Cloud/Hugging Face Spaces)
- Day 55 : Git and GitHub basics, and portfolio setup
Week 12 - Ethics, Capstone, and Careers
- Day 56 : AI ethics: bias, privacy, hallucinations, responsible use
- Day 57 : Capstone planning, dataset selection, and architecture
- Day 58 : Capstone building
- Day 59 : Capstone testing, documentation, and presentation preparation
- Day 60 : Project presentations, career guidance (roles, resume, interviews), and certificates
Capstone project ideas
- Document Q&A chatbot (RAG)
- Sales forecasting dashboard
- AI resume screener
- Customer support bot
- Student performance predictor
Daily class format (about 2 hours)
- 10 min : Recap of the previous day
- 50 min : New concept with live demo
- 45 min : Hands-on exercise
- 15 min : Assignment and Q&A
COURSE DETAILS
| Duration | 60 days (12 weeks, 5 days a week) |
|---|---|
| Level | Beginner to Advanced |
| Class time | About 2 hours daily: 1 hour concept and 1 hour hands-on practice (about 120 hours in total) |
| Batch size | 1 to 3 students (one-to-one or small group) |
| Mode | Live online on Google Meet |
| Language | Hindi or English |
| Projects | 10 projects including a capstone, plus a final presentation |
| Certificate | Yes, with real project work |
| Support | Phone and Google Meet doubt support |
| Demo | Free 3-4 day demo class |
| Fees | Contact Sujeet Sir: +91 88025 79388 |
HOW IT IS TAUGHT
Live on Google Meet by Sujeet Sir in Hindi or English, with real examples, hands-on projects, a certificate and doubt support on phone after the course.
Join a free demo
Questions about this course
What is the fee of the Artificial Intelligence (AI) Training course?
Fees depend on one-to-one or group batch. Contact Sujeet Sir on +91 88025 79388 or WhatsApp for the current fee.
How long is the AI Training course?
60 days over 12 weeks, 5 days a week, with about 2 hours of live class each day.
Do I need to know programming before joining?
No. The course starts with AI basics and Python basics, then moves step by step to Machine Learning, Deep Learning, Generative AI and AI agents.
Is there a free demo?
Yes. Join the 3-4 day free demo before you pay.
Which language is it taught in?
Hindi or English, as you prefer.