LogicMojo
Live instructor-led classes
Learn in real time, ask questions as you go and code along with the mentor.
AI Course for Beginners: Learn AI from Scratch, Build Real Projects
New to AI? Perfect. We start from zero and go step by step, until you can build real AI apps on your own.
- Start from zero — no AI or coding needed
- A real project in every module
- Live classes with doubt-clearing
- Learn from industry mentors
- Program fee
- ₹87,000EMI from ₹7,250/month
- Next batch
- Weekends, live online
- Duration
- 7 monthsAbout 30 weeks
- Eligibility
- BeginnersNo AI or coding needed
- Type of program
- CertificateOn completion
AI course for beginners — overview
Program introduction
Learn AI from zero and build real AI apps in 7 months of live classes.
This live, beginner-friendly course takes you from Python basics to machine learning, deep learning and generative AI: prompting, LLM APIs, RAG and your first AI agent. No coding or AI background needed; school-level maths is enough.
How it works. 8 modules over about 30 weeks, taught live on weekends with recordings and doubt-clearing sessions if you fall behind.
What you build. Every module ends with a project, such as a house price predictor, an image classifier and a document Q&A bot. You finish with eight projects on GitHub, including a deployed capstone.
Who it's for. Students, developers, analysts and managers who want hands-on AI skills, not just theory.
What you'll be able to do after this course
- Write Python programs to load, clean and chart a real dataset
- Train a simple prediction or classification model and explain how well it works
- Explain in plain words how neural networks and large language models work
- Build a small app that calls an LLM API, including a chatbot that answers from your own documents
- Put a working AI app online and show the code on GitHub
That's a solid foundation, not expert level. Production ML and GenAI roles usually also need more projects, work experience or advanced study (see Career outcomes).
8Modules, from Python basics to your first AI agent
8Hands-on builds, including a capstone
180Live instructor-led hours
Key details
Course at a Glance
| Detail | Weekend batch |
|---|---|
| Next start date | |
| Class time (IST) | Sat & Sun, 9:00 AM – 12:00 PM |
| Duration | 7 months (about 30 weeks) · 6 live hours a week · plan for 9–11 hours a week in total |
| Format | Live online classes; every class is recorded so you can rewatch any session |
| Prerequisites | No AI or coding background needed. School-level maths and any laptop with stable internet (no GPU) |
| Projects | 8 hands-on projects, one per module, ending with a deployed capstone on GitHub |
| Support | Mentor doubt-clearing sessions, plus peer feedback in the LogicMojo AI Community |
| Fee | ₹87,000 incl. GST · no-cost EMI from ₹7,250/month |
| Extra costs | LLM API usage (Modules 6–8) may need a small prepaid credit; deployment uses free tiers. No paid software or GPU needed |
| Certificate | Issued after your module projects and capstone are reviewed by mentors |
| Refunds | As per LogicMojo's published policy and your written program terms · Refund policy |
Who should enroll in this AI course?
Who Is This Course For?
Built for beginners and working professionals who want to understand AI and build real AI projects step by step, not just watch videos.
Students & Freshers
Start your AI journey from zero. Learn Python, machine learning and generative AI in a clear, step-by-step path. No coding background needed.
Career Switchers
Moving into tech from another field? Live weekend classes and eight guided projects help you build skills and a GitHub portfolio at a steady pace.
Software Developers
Add AI to the apps you already build: call LLM APIs, create chatbots and make a bot that answers questions from your own documents.
Data & Business Analysts
Go from reports and dashboards to models that predict prices, sales or customer churn, using Python and scikit-learn.
QA, Support & DevOps Engineers
Learn how AI models and LLM apps work inside, so you can test, support and deploy them with confidence.
Product Managers & Tech Leads
Understand what AI can and can't do, so you can plan AI features, talk clearly with engineers and make better decisions.
Compare before you enroll
Is this course right for you?
What the course covers
- Python from the very basics, then cleaning data and making charts
- Machine learning: teaching a computer to predict prices and sort data
- Deep learning basics: neural networks that understand images and text
- Generative AI: prompting, LLM APIs, RAG and your first AI agent
- Putting a working AI app online, with all your code on GitHub
What it does not cover
- Advanced maths or research-level AI theory. Ideas are taught with visuals and code
- Training huge models like ChatGPT from scratch
- No-code tools only. You will write Python in every module, with help
- A guaranteed job, placement or salary
Consider another option if
- You can't give about 9–11 hours a week (weekend live class plus practice) for 7 months
- You only want to use AI tools like ChatGPT at work, without learning to code
- You already build ML models at work and want only advanced topics
- You want a quick certificate in a few weeks rather than hands-on skills
All you need to start: school-level maths, any laptop with stable internet (no GPU) and free GitHub and Hugging Face accounts. An LLM API account is needed from Module 6 and may cost a small prepaid credit; see extra costs.
AI course highlights
Course Highlights
Python from the basics
No coding background assumed. You'll write real Python from the very first week.
A build in every module
From an exploratory data analysis report to an image classifier, a document Q&A bot and a deployed capstone.
ML & deep learning intuition
Understand why models work — regression, trees, neural networks — not just which function to call.
Generative AI, hands-on
Prompting, LLM APIs, embeddings and RAG — the skills behind today's AI assistants.
Your first AI agent
Give an LLM tools to use and see how agentic AI works, as a launchpad for advanced study.
Learning methodology
How you'll learn
A simple, repeatable loop for every module: understand the idea, code it live, practise it alone, then build something real with it.
Live concept classes
Mentors explain each idea with visuals and live code you follow along with.
Guided practice
Short notebook exercises after each class so concepts stick before you move on.
Module projects
Every module ends with a small project you push to your own GitHub.
Doubt-clearing sessions
Dedicated sessions to unblock you — no question is too basic.
Class recordings
Missed a class or want to revisit a topic? Rewatch the recording.
Community & reviews
Share builds with the LogicMojo AI Community and get feedback from peers and engineers.
Upcoming batch and course logistics
Schedules
One batch is open for enrolment right now. Dates and seat availability are confirmed by an advisor before you pay.
Morning · Weekend
Starts
Course logistics
Total duration7 months (about 30 weeks), 180 live hours
Weekly commitment6 hours of live class + 3–5 hours of practice and projects
RecordingsEvery class is recorded and shared, so you can rewatch any session
If you fall behindCatch up from recordings, then bring questions to the doubt-clearing sessions. Talk to an advisor if you need a longer break.
Time zoneClasses run on Indian Standard Time; recordings help if you're in another time zone
CertificateAfter your module projects and capstone are reviewed by mentors
AI course for beginners — fees
Fees & EMI
₹87,000
Total course fee, inclusive of GST. This is the full price; there are no separate registration or exam fees.
Early-enrolment offer: the first 15 learners in a batch get 15% off, bringing the fee to ₹73,950. An advisor confirms whether a discounted seat is still available.
No Cost EMIInterest-free plans of 3, 6, 9 or 12 months on any credit card. The 12-month plan works out to ₹7,250/month on the full fee.
What the fee includes
- Live online classes across all 8 modules
- Access to class recordings
- A hands-on project in every module, plus a capstone
- Dedicated doubt-clearing sessions with mentors
- AI Course for Beginners certificate
Possible extra costs
- LLM API usage (Modules 6–8): some providers have a free tier; others need a small prepaid credit before their API works. Any usage charges are billed by the provider directly, not by LogicMojo.
- Cloud deployment (Module 8): the course uses free tiers. Paid hosting is optional.
- Software and hardware: none. All course tools are free and no GPU is needed.
AI course for beginners — curriculum
Curriculum
Set up your AI development environment, understand what AI, machine learning, deep learning and generative AI really mean, and write your first Python programs.
Topics
- What is AI? AI vs ML vs deep learning vs generative AI
- Setting up Python, VS Code and Jupyter notebooks
- Variables, data types and operators
- Conditions, loops and functions
- Lists, dictionaries, sets and tuples
- Reading and writing files, working with JSON
- Installing packages and virtual environments
- Git and GitHub basics
ProjectPython Quiz & Expense Tracker
A command-line app that stores data in JSON — your first program on GitHub.
Learn to load, clean, analyse and visualise data — and build the intuition for the statistics and linear algebra that power AI, without heavy theory.
Topics
- NumPy arrays and vectorised operations
- Pandas DataFrames: filtering, grouping, joining
- Handling missing and messy data
- Visualisation with Matplotlib
- Mean, median, variance and distributions
- Probability basics for machine learning
- Vectors, matrices and dot products — intuitively
- Exploratory data analysis (EDA) workflow
ProjectExploratory Data Analysis Report
Analyse a real-world dataset and present your findings as a visual notebook report.
Understand how machines learn from data and train your first predictive models with scikit-learn.
Topics
- Supervised vs unsupervised learning
- Train/test split and why it matters
- Linear regression and gradient descent intuition
- Logistic regression for classification
- Evaluation: MAE, RMSE, accuracy, precision, recall
- Overfitting, underfitting and the bias–variance trade-off
- The scikit-learn workflow
ProjectHouse Price Predictor
Train, evaluate and explain a regression model on real housing data.
Go beyond the basics with the algorithms used most often in industry, and learn how to make models reliable.
Topics
- Feature engineering and encoding
- Decision trees and random forests
- Gradient boosting — an introduction
- K-means clustering
- Cross-validation and hyperparameter tuning
- Handling imbalanced data
- ML pipelines in scikit-learn
ProjectCustomer Churn Classifier
Predict which customers are likely to leave and explain the key drivers.
Learn how neural networks learn, build them in PyTorch and apply them to images and text.
Topics
- Neurons, layers and activation functions
- Loss functions and backpropagation intuition
- PyTorch tensors and training loops
- Convolutional neural networks (CNNs) for images
- Transfer learning with pre-trained models
- Introduction to NLP: text as numbers
ProjectImage Classifier
Build and fine-tune a CNN that recognises everyday objects in photos.
Understand how large language models work under the hood and learn to use them effectively and safely in your own programs.
Topics
- Transformers and attention — the intuition
- Tokens, context windows and embeddings
- How LLMs generate text: next-token prediction
- Prompt engineering patterns and structured outputs
- Calling LLM APIs from Python
- Open-source models with Hugging Face
- Hallucinations, cost and responsible use
ProjectAI Study Assistant
A chat assistant that explains topics, quizzes you and returns structured answers.
Ground LLMs in your own data with retrieval-augmented generation, then give them tools to build your first simple AI agent.
Topics
- Why LLMs need retrieval
- Chunking documents and creating embeddings
- Vector databases with ChromaDB
- Building a RAG pipeline with LangChain
- Answers with citations
- Function / tool calling
- What makes an AI agent — plan, act, observe
ProjectDocument Q&A Bot
Upload PDFs and get answers grounded in them, with source citations.
Turn your models and LLM features into apps people can use, follow responsible-AI practices and ship your capstone.
Topics
- Building AI web apps with Streamlit
- Serving models with FastAPI — the basics
- Environment variables and API-key safety
- Deploying to a free cloud tier
- Responsible AI: bias, privacy and safety
- Documenting projects and building a GitHub portfolio
ProjectCapstone: End-to-End AI Application
Design, build and deploy an AI app of your choice, with mentor review.
AI & ML experts
Who Will Mentor You in This Course?
Classes are led by working AI and data professionals. Mentors are assigned to each batch based on availability, and an advisor can tell you who is teaching yours.
Founder, LogicMojo · Former AI Architect, Amazon & WalmartLabs · 15+ years in IT
Ravi is a Data Science and AI expert with more than 15 years in the IT industry. As an AI Architect at Amazon and WalmartLabs, he built large-scale machine learning and deep learning systems. He founded LogicMojo to teach AI the way it is used at work: clear explanations, real code and projects that connect each idea to a real-world application.
Reviewed by our expert team
Instructor & mentor (AI & ML) — LogicMojo AI Candidate cohort guidance. Senior AI Architect at Samsung R&D Division with deep expertise in building production-grade AI systems and mentoring aspiring AI professionals.
Ex-Goldman Sachs & BITS Pilani alum. Connects ML theory to business impact using real-world examples from Uber. Mentors students on A/B testing, causal inference, and industry readiness.
IIT Kharagpur graduate specializing in Computer Vision & LLMs. Built virtual try-on platforms and AI APIs. Mentored 2100+ students in ML, statistics, and real-world projects.
8+ years architecting scalable AI systems. Senior Instructor at LogicMojo for 3 years, training 5000+ learners globally. Expert in delivering practical, industry-aligned AI training.
Senior Lead at Walmart Global Tech, ex-Informatica. Full Stack expert (MERN) with deep experience in cloud-based applications. Passionate mentor bridging the gap between coding and corporate impact.
Learner reviews
What LogicMojo learners say
We're collecting stories from AI Course for Beginners learners and will add them here, with the projects they built, once they're verified. Until then, these reviews come from learners on LogicMojo's Data Science Course, which covers some of the same machine-learning and deep-learning topics. Each one is labelled with the course taken.
I highly appreciated Logicmojo's data science course for its outstanding lectures and the expert team's readiness to address technical queries, which played a crucial role in helping me secure a job in Data Scientist roles, especially GenAI development.
Course taken: LogicMojo Data Science Course
I liked Logicmojo's data science course for its amazing lectures and the always-helpful expert team, which really helped me land a job as a Data Scientist at Invent Health Inc. It's the best data science course currently available online.
Course taken: LogicMojo Data Science Course
It was a rewarding 7-month journey. The instructor covered Advanced Python, Machine Learning, Deep Learning and Computer Vision in the classes. I built a strong profile as a Data Scientist and completed 5 projects during the classes.
Course taken: LogicMojo Data Science Course
Looking for reviews of other LogicMojo programs, such as DSA, system design and software engineering? See all LogicMojo reviews
Your learning path
AI Course Roadmap
Month 1
AI foundations & Python for AI
Month 2
Working with data & maths intuition
Month 3
Machine learning fundamentals
Month 4
Practical machine learning
Month 5
Deep learning & neural networks
Month 6
Generative AI, LLMs & prompt engineering
Month 7
RAG & your first AI agent
Capstone & Certificate
Deploy your capstone, with 8 projects on GitHub and your course certificate
Which Tools Will You Master in This Course?
Hands-on with the beginner-friendly tools used to build, train and ship AI.
Programming & data
PYPython
JUJupyter
NPNumPy
PDPandas
MPMatplotlib
Machine & deep learning
SKscikit-learn
PTPyTorch
HFHugging Face
Generative AI
AILLM APIs
LCLangChain
VDChromaDB
Build & ship
STStreamlit
FAFastAPI
GHGit & GitHub
VSVS Code
Tool names identify technologies taught in the course and do not imply endorsement or partnership.
AI course projects
What Kind of Project Will You Build in This Course?
Every project ends up as a public GitHub repository with your code, a README that explains the problem, your approach and results, and screenshots or a demo. Here is what you hand in for four of the eight projects.
- Modules 1–7: guided. Each project builds on the code from that module's live classes. You complete it yourself, can adapt it to a dataset or topic you choose, and get help in doubt-clearing sessions.
- Module 8 capstone: individual. You pick the idea, build and deploy it yourself, and a mentor reviews it before your certificate is issued.
House Price Predictor
Your first end-to-end ML model.
You hand in: a Jupyter notebook that cleans the data, compares two regression models and reports their error, plus a short write-up of which features matter most and why.
Pandasscikit-learnModel evaluation
Image Classifier
Teach a neural network to see.
You hand in: training code, accuracy before and after transfer learning, and a set of example photos the model gets right and wrong, with your notes on why.
PyTorchCNNsTransfer learning
AI Study Assistant
Your first LLM-powered app.
You hand in: a small chat app with your prompts in version control, sample conversations, and a note on how you handled wrong or made-up answers.
LLM APIsPrompt engineeringStructured outputs
Document Q&A Bot
Answers grounded in your documents, with citations.
You hand in: a working app that answers questions about a set of PDFs you choose and shows the source passage for every answer, plus a test list of questions and how well it did.
RAGLangChainChromaDB
Skills you gain, and where they can lead
Career outcomes: what to expect
This is a foundation course. You finish with practical AI skills and a portfolio you can show. It is a strong first step toward GenAI developer or ML engineer roles, but those jobs usually need more training and experience. Use the tabs to see which skills come from this course and what each path needs next.
For professionals in any field, such as marketing, HR, operations, sales or teaching, who want to use AI to work faster and better.
Realistic next step: using AI tools with confidence in the job you already have, and becoming the go-to AI person on your team.
What this course teaches
- Prompt engineering and structured outputs
- Using LLMs to speed up everyday work
- Python and Pandas basics for working with data
- Spotting hallucinations and managing cost
Portfolio work from this course
- AI Study Assistant
- Document Q&A Bot
What the role usually also needs
- Practice applying AI to real tasks in your own field
- Knowing your company's data and privacy rules
For analysts and business professionals who want to add predictive models and AI tools to their work.
Realistic next step: bringing predictive models and AI-assisted analysis into your current analyst work, or moving into an analyst role that uses them.
What this course teaches
- Pandas, data cleaning and EDA
- Charts and telling stories with data
- Statistics intuition
- Simple predictive models
- Using LLMs to speed up analysis
Portfolio work from this course
- Exploratory Data Analysis Report
- Customer Churn Classifier
What the role usually also needs
- SQL and a BI tool such as Power BI or Tableau
- Knowledge of your industry or domain
For people who already work as software developers and want to add AI features to the products they build.
Realistic next step: adding LLM-powered features in your current role. This path assumes you already have professional coding experience; the course does not replace it.
What this course teaches
- Calling LLM APIs from Python
- Simple apps with Streamlit and FastAPI
- API-key safety and cost awareness
- Git-based workflow and cloud free-tier deployment
Portfolio work from this course
- AI Study Assistant
- Document Q&A Bot
- Capstone application
What the role usually also needs
- Existing software engineering experience
- Testing, monitoring and security for AI features in production
GenAI developers build products on large language models: assistants, document search and agents. This course gives you a working introduction.
Realistic next step: an advanced course such as Agentic AI, plus larger projects, before applying for GenAI developer roles.
What this course teaches
- Prompt engineering and structured outputs
- Embeddings and RAG basics
- Tool calling and a first simple agent
- Handling hallucinations and cost
Portfolio work from this course
- AI Study Assistant
- Document Q&A Bot
- Capstone application
What the role usually also needs
- Strong general programming skills
- Evaluating and testing LLM apps
- Production agents, guardrails and deployment
Junior ML engineers train, evaluate and ship machine-learning models. This course covers the first steps of that path.
Realistic next step: more projects, internships or an advanced program before applying for ML engineer roles. Most of these roles also ask for work experience or a related degree.
What this course teaches
- Data cleaning and feature engineering
- Regression, classification and decision trees
- Model evaluation and cross-validation
- Neural network basics in PyTorch
Portfolio work from this course
- House Price Predictor
- Customer Churn Classifier
- Image Classifier
What the role usually also needs
- Deeper ML and statistics
- SQL and data pipelines
- Deploying and monitoring models (MLOps)
- Larger, real-world projects
Skill mapping is based on the curriculum. Completing the course is not a guarantee of a job title, employer or salary.
Certification
A credential that demonstrates capability
On successful completion, receive a verifiable AI Course for Beginners certificate you can showcase on LinkedIn and to recruiters.
Verifiable & shareable
Unique credential ID and a public verification link to embed anywhere.
LinkedIn-ready
One click to add it to your LinkedIn profile under Licenses & Certifications.
Issued on capability
Earned only after your hands-on projects and capstone are reviewed by mentors.
Backed by your portfolio
Pairs with the eight projects on your GitHub, so recruiters can see the work behind it.
LogicMojo AI Community
Where real learners ship real AI projects — reviewed by working engineers.
Explore student profiles, GitHub repositories, and live AI/ML/GenAI/Agentic AI projects built by the LogicMojo community. Every project is peer-reviewed and portfolio-ready.
1,200+ active builders
500+ shipped projects
8,400+ GitHub commits
Explore the AI Community
+1,200
AI course for beginners — FAQs
Frequently Asked Questions
AI Course Training
A live, mentor-led online course from LogicMojo that takes you from no AI background to building real AI projects. You learn Python, machine learning, deep learning, generative AI and RAG, with a hands-on project in every module.
AI is the broad goal of making machines act intelligently, and machine learning is the main way to do it by learning from data. Deep learning is ML with multi-layer neural networks, and generative AI uses those models to create text, images or code.
You start with Python, Jupyter, NumPy, Pandas and Matplotlib, then move to scikit-learn, PyTorch and Hugging Face. For generative AI you use LLM APIs, LangChain and ChromaDB, and you ship apps with Streamlit, FastAPI and Git.
Yes. Every module ends with a project, such as a house price predictor, an image classifier and a document Q&A bot. The course finishes with an end-to-end capstone project that you build and deploy.
Yes. Module 6 covers how large language models work, prompt engineering and LLM APIs, and Module 7 covers RAG and tool calling, where you build a simple AI agent. For deeper agent work, LogicMojo's Agentic AI Course is the next step.
Eligibility
No. Python is taught from the basics in Module 1, so complete beginners can start from zero. If you already code, you will simply move faster through the early modules.
No. School-level maths is enough to start. The course builds intuition for the statistics, probability and linear algebra behind AI using visuals and code rather than heavy theory.
Anyone who wants a structured, hands-on start in AI: students and freshers, software developers, QA, support and DevOps engineers. It also suits data and business analysts, product managers and professionals switching into AI.
Any Windows, macOS or Linux laptop or desktop with a stable internet connection. You do not need to buy any software, and heavy deep learning runs in free cloud notebooks, so you do not need a powerful GPU.
Format
Both. Classes are taught live online by an instructor, so you can ask questions in real time. Recordings are shared afterwards so you can revise or catch up on anything you missed.
The course runs for 7 months (about 30 weeks), with live weekend classes on Saturday and Sunday, 9:00 AM – 12:00 PM IST. The next batch starts on 14 November 2026. See Schedules for details.
About 6 hours of live class on weekends, plus 3–5 hours of practice and project work during the week. If you fall behind, catch up from the recordings and bring questions to the doubt-clearing sessions.
Every class is recorded, so you can watch the recording and catch up at your own pace. Bring any questions to the next doubt-clearing session and a mentor will help you.
You can ask questions directly during live classes while the instructor is teaching. There are also dedicated doubt-clearing sessions where mentors work through your questions with you.
Fees
The total fee is ₹87,000, inclusive of GST, with no separate registration or exam fees. The first 15 learners in a batch get 15% off, which brings it to ₹73,950. See Fees & EMI for everything that is included.
Yes. No Cost EMI is interest-free on any credit card, with 3, 6, 9 or 12-month plans. The 12-month plan works out to ₹7,250/month on the full fee. Talk to an advisor for the options available for your batch.
Possibly small ones. From Module 6 you need an LLM API account: some providers have a free tier, while others need a small prepaid credit, billed by the provider directly. Cloud deployment in Module 8 uses free tiers. No paid software or special hardware is needed.
Refunds follow LogicMojo's published refund policy and the written program terms you receive at enrolment. Please read both, or ask an advisor, before you pay.
Career
No course can guarantee a job, and this beginner course alone is not enough for most AI engineering roles. It gives you hands-on AI skills, an eight-project portfolio and a certificate to build on. Results depend on your effort, prior experience and market conditions.
It is a foundation. It fits best if you want to add AI to your current work, whatever your role, including analysts and software developers. ML engineer and GenAI developer roles usually also need more projects, work experience or an advanced program such as Agentic AI. See Career outcomes for what each path needs.
Yes. You earn a LogicMojo AI Course for Beginners certificate when you complete the course projects and the capstone, and you can add it to your LinkedIn profile.
You can go deeper into data science and machine learning, or build production AI agents with LogicMojo's Agentic AI Course. Not sure which? An advisor can help you choose.
No questions match your search. Ask an advisor instead
Ready to start?
Start your AI journey — even if you're starting from zero
Learn the fundamentals, build eight real projects and finish with a deployed capstone. Talk to an advisor to find the batch that fits your schedule.
Recommended LogicMojo courses
Continue Your Learning
Finished the beginner course? These programs take you further.
Disclaimer
At LogicMojo, we're committed to helping every learner grow through live classes, hands-on projects and expert mentorship. Because career outcomes such as placements, job titles and salaries are shaped by each learner's effort, experience and the job market, we don't guarantee them, but we support you every step of the way. Every testimonial is labelled with the course that learner took, so you can see results from the exact program you're considering. Tool names reflect the technologies you'll learn and don't imply endorsement or partnership. Fees, schedules and syllabus are clearly confirmed in your written program terms at enrolment, so you always know exactly what you're signing up for.