From Python basics to a deployed ML app.
Learn AI & Machine Learning with Python in 72 live classes — Python and SQL, statistics, machine learning, a Streamlit app deployed publicly, deep learning, text, Generative AI and RAG — taught live on Zoom. You build inside every hour, and one customer-churn capstone runs from data prep to a live app on your GitHub. We are collecting interest for the next batch — tell us the days and times that suit you.
- 72live classes · 1 hour each
- 9modules · Python to GenAI
- 1deployed capstone app
- Liveon Zoom · every class recorded
How it works now
Register interest — we’ll contact you about suitable batch options.Tell us the days and times that suit you. An enquiry does not register you on Zoom, reserve a seat or confirm a batch.- Python → ML → deployed app, one journey
- Free tools · no paid API · no GPU
- Capstone deployed publicly + on your GitHub
- GenAI, embeddings & a RAG Q&A app
- Revision recordings · 1 year from full fee payment
Register your interest
Next batch date to be confirmed. Tell us how to reach you and we’ll contact you about suitable batch options.
This form saves your interest. It does not register you on Zoom, reserve a paid seat or confirm a batch.
- 72live classes · 72 hours
- 9modules · Python to GenAI
- 1capstone app deployed publicly
- 1 yearrevision recordings from full fee payment
- 2-dayrefund window after paid classes start
Not just notebooks. A model you can show — live on the web, documented on GitHub.
Most ML courses end with a notebook on a laptop. Here every class is concept and demo, then guided practice inside the same hour — and from session 43 one customer-churn project runs through modelling, a Streamlit app, public deployment, GitHub documentation, presentation and a mock interview. You finish with something an interviewer can click.
Live, one hour per class
Concept and demo for about 35 minutes, then guided practice in the same hour, with the last minutes reserved for doubts. Practical and project classes are student-built with the mentor guiding.
Python to Generative AI, in the right order
Python and Git → data analysis and SQL → statistics → machine learning → deployment → deep learning → text → GenAI and RAG → capstone. Nine modules, 72 sessions, each reused by the next.
One capstone you carry to the end
Telco customer-churn prediction: problem framing and EDA, a full pipeline with tuning and error analysis, a Streamlit app validated and deployed publicly, a README on GitHub, then presentation and code review.
Free tools only — no hidden costs
Python, Colab (free GPU for the CNN sessions), scikit-learn, XGBoost, Keras, Streamlit Community Cloud, FAISS/Chroma, LangChain and free-tier LLM access. No paid API subscription and no GPU purchase.
Revision recordings
Every class is recorded. Paid students get the recordings of their batch for revision at no extra charge, for one year from the date their fee is paid in full. Assignments after most classes are reviewed by the mentor, with soft-copy materials for revision.
Assessment, mock interview and certificate
Capstone presentation and code review, a final assessment, a technical mock interview based on your capstone, interview preparation on common ML/AI questions and a JavitaTech Certificate of Completion.
What you will learn, module by module
Python and data first, then statistics and machine learning, then deployment, deep learning, text and Generative AI — finishing with the capstone, code review and interview preparation.
- Module 1 · Sessions 1–10 · 10 hrs
Python Foundations & Git
Python basics, collections, functions, OOP, files, CSV/JSON, debugging, packages and Git/GitHub — a contact-book app and your first GitHub repo.
- Module 2 · Sessions 11–20 · 10 hrs
Data Analysis & Essential SQL
NumPy, pandas, cleaning, grouping and merging, Matplotlib and Seaborn EDA, SQL on SQLite — Titanic and Superstore EDA and a mini EDA project.
- Module 3 · Sessions 21–25 · 5 hrs
Applied Statistics
Descriptive statistics, probability, sampling and confidence intervals, correlation vs causation, hypothesis testing — a t-test on real data.
- Module 4 · Sessions 26–43 · 18 hrs
Machine Learning
Regression, classification, decision trees, random forests, boosting, KNN, SVM, k-means, PCA, pipelines, evaluation, tuning, imbalanced data and error analysis — ten model types and the churn pipeline.
- Module 5 · Sessions 44–48 · 5 hrs
ML Application & Deployment
Saving the pipeline, a Streamlit prediction app, validation and testing, public deployment on Streamlit Community Cloud, GitHub documentation, versioning and error tracking.
- Module 6 · Sessions 49–54 · 6 hrs
Deep Learning & Image Applications
Neural networks, backpropagation, Keras, CNNs, transfer learning with MobileNetV2 — a guided image-classification project.
- Module 7 · Sessions 55–58 · 4 hrs
Text Applications
Text cleaning and tokenization, bag of words and TF-IDF, sentiment classification, sentence embeddings — a mini sentiment-analysis project.
- Module 8 · Sessions 59–65 · 7 hrs
Generative AI & Document Q&A
LLM fundamentals, prompt engineering, document loading and chunking, embeddings and vector stores, RAG — a working document Q&A app, tested for reliability.
- Module 9 · Sessions 66–72 · 7 hrs
Capstone, Review & Interview Preparation
Final churn model and Streamlit app, testing, documentation and public deployment, presentation, code review, final assessment and a mock interview.
What you will build in class
- Module 1 — a contact-book app, OOP classes, file/CSV/JSON scripts and your first GitHub repo
- Module 2 — Titanic and Superstore EDA in pandas, SQL on a SQLite sales database, a mini EDA project
- Module 4 — ten model types on real datasets, then a full churn pipeline with tuning and error analysis
- Module 5 — a Streamlit churn-prediction app, validated, deployed publicly and documented on GitHub
- Module 6 — a Keras neural network, a trained CNN and a MobileNetV2 transfer-learning image classifier
- Module 7 — a sentiment classifier with TF-IDF, then with sentence embeddings
- Module 8 — a document Q&A app with embeddings, a vector store and RAG, tested for reliability
- Module 9 — the final churn model and app, presented, code-reviewed and used for your mock interview
- All 72 sessions with topics, subtopics and the practical you build in each
- The 9 modules, the customer-churn capstone plan and how a one-hour class runs
- Tools, datasets, free accounts you create and the assessment structure
Enter your details and the PDF downloads right here.
Your curriculum is ready
The PDF is downloading. If it did not start, use the button below.
Download the curriculum PDF Ask a question on WhatsAppName, email and WhatsApp number are required so that we can send you the curriculum.
Concept and demo first — then you build it in the same hour
Your mentor
The mentor for the next batch is confirmed together with the batch dates.
- Concept and demo first, then you build the same thing in the same hour
- Assignments are reviewed after most classes and doubts are cleared inside every session
- The customer-churn capstone is guided from EDA to a deployed app and a GitHub README
- Code review, final assessment and a mock interview based on your own project
This is not a lecture series. Each 60-minute class is a concept, a demo and a hands-on build — so that by the end of the course you have trained, evaluated and deployed models yourself, built a working GenAI Q&A app, and can explain every decision in an interview.
- 01Build inside the hourEvery session ends with something you made: a script, a cleaned dataset, a trained model, a deployed app.
- 02One capstone, carried to the endTelco customer churn from session 43 to 69 — modelled, deployed publicly, documented on GitHub, presented and reviewed.
- 03Interview preparation built inFinal assessment, technical mock interview, interview preparation on common ML/AI questions and a JavitaTech Certificate of Completion.
The fee is confirmed with the batch
We are collecting interest for the next batch. The fee and payment options are confirmed together with the batch dates and shared with you before you decide. Registering interest is free.
Fee to be confirmed
Collecting interest — next batch date to be confirmed.- Registering interest is free — no payment is taken on this page
- We share the fee and payment options with the batch details before you decide
- GST included · no hidden charges
An enquiry does not reserve a seat or confirm a batch.
Everything included in the fee
- 72 live classes on Zoom, one hour each
- Hands-on practice inside every hour · assignments reviewed by the mentor
- Revision recordings (1 year from full fee payment) and soft-copy materials
- Customer-churn capstone deployed publicly and documented on GitHub
- Capstone presentation, code review and final assessment
- Technical mock interview and interview preparation
- GenAI / RAG document Q&A app and image-classification mini-project
- JavitaTech Certificate of Completion
Refund policy, in short
After enrollment, 15% of the total fee is non-refundable if you withdraw. Withdraw before paid classes begin and the rest is refunded. Once paid classes begin, refund requests are accepted within 2 days of your first paid session and processed within 15 business days. If JavitaTech cancels a batch, all fees paid are refunded in full, including the 15%. Read the full Cancellation & Refund Policy.
Collecting interest — next batch date to be confirmed.
Tell us the days and times that suit you. When a batch is being planned, we contact the people who registered interest with the dates, timing, mentor and fee — and you decide then. Every class is recorded.
- Start dateTo be confirmedWe are collecting interest for the next batch
- Days and timingTo be confirmedTell us your preferred days and time ranges when you register interest
- Course length72 classes · 72 hoursLive on Zoom · one hour per class
- Register interestFill the short form — about 30 seconds. No payment.
- Add your scheduleOptionally tell us your preferred days, times and start window.
- We contact youWith suitable batch options, on WhatsApp, phone or email.
- Batch confirmedYou get the dates, timing, mentor and fee in a separate confirmation.
- Deploy your capstone72 classes, a live ML app on your GitHub, mock interview and certificate.
Beginner or working analyst — both get value
- Freshers and graduatesSession 1 installs Python with you. No coding or maths background needed.
- Professionals moving into data and MLFrom spreadsheets to pandas, SQL, trained models and a deployed app you can show.
- Analysts, Excel and SQL usersTwo full modules on data analysis and SQL before the modelling starts.
- Developers who want GenAI skillsLLMs, prompt engineering, embeddings, vector stores and a working RAG Q&A app.
Questions learners ask
Is this suitable for complete beginners?
Yes. Session 1 installs Python, VS Code and Google Colab with you and runs your first script; sessions 1–10 build Python from the ground up before any data or ML topic. No coding or maths background is needed — statistics is taught in Module 3.
When does the next batch start?
We are collecting interest — the next batch date is to be confirmed. Register your interest on this page and tell us the days and times that suit you; we will contact you about suitable batch options. Registering interest does not reserve a seat or confirm a batch, and a demo registration is confirmed only when you receive a separate confirmation for that session.
Do I need a GPU or paid tools?
No. Everything is free and open-source: Python, Google Colab (its free GPU covers the CNN sessions), scikit-learn, XGBoost, TensorFlow/Keras, Streamlit Community Cloud, FAISS/Chroma, LangChain and free-tier LLM access via Google AI Studio or Groq. Any recent laptop works.
What exactly will I build?
A contact-book app and your first GitHub repo, EDA on Titanic and Superstore data, SQL queries on a sales database, ten model types on real datasets, a Streamlit churn-prediction app deployed publicly, a CNN and a MobileNetV2 image classifier, a sentiment classifier, a document Q&A app with RAG — and the Telco customer-churn capstone, presented and code-reviewed.
What are the timings and duration?
72 live classes of one hour each, on Zoom. The days, timing and start date of the next batch are to be confirmed — tell us your preferred schedule when you register interest. Every class is recorded. Paid students get the recordings of their batch for revision at no extra charge, for one year from the date their fee is paid in full.
What is the fee and how do I pay?
The fee and payment options for the next batch are confirmed together with the batch dates and shared with you before you decide. Registering interest is free, and no payment is taken on this page.
What is the refund policy?
After enrollment, 15% of the total fee is non-refundable if you withdraw. If you withdraw before paid classes begin, the rest is refunded. Once paid classes begin, refund requests are accepted within 2 days of your first paid session and approved refunds are processed within 15 business days. If JavitaTech cancels a batch, all fees paid are refunded in full, including the 15%. Full policy: Cancellation & Refund Policy.
How are assessments and the certificate handled?
Assignments after most classes, module consolidations in sessions 10 and 20, mini-projects in sessions 54, 58 and 64, the capstone build in sessions 43 and 66–69 with a presentation in 70, code review in 71, and the final assessment and mock interview in 72 — then the JavitaTech Certificate of Completion (a training certificate, not a vendor certification).
Do you provide placement support?
You get a technical mock interview based on your capstone, interview preparation on common ML/AI questions, a deployed project and a GitHub portfolio to show. Placement referral is a separate, optional registration — no job guarantee. We prepare you for interviews; we do not promise a job.
I am outside India. Can I join?
Yes. Everything is live online on Zoom in IST, in English, and every class is recorded, so you can attend live, and paid students can also watch the recordings in their own time zone. Academic questions are answered live in the sessions.
Tell us you’re interested in the next batch.
Collecting interest — next batch date to be confirmed. Register your interest with the days and times that suit you, and we’ll contact you about suitable batch options.
- No payment to register interest
- A deployed ML app and a RAG Q&A app on your GitHub
- Revision recordings and materials
- Mock interview, code review and certificate
Register your interest
Next batch date to be confirmed. We’ll contact you about suitable batch options.
This form saves your interest. It does not register you on Zoom, reserve a paid seat or confirm a batch.