Want to start a career in AI but do not know where to begin? This course takes you from your first line of Python to a trained, evaluated and deployed model — with the maths explained instead of skipped. Step by step, in Hindi and simple English, so you never feel lost.
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Who this is for
Machine learning has a reputation for being closed to anyone without a computer science degree. It is not — but it is genuinely harder than most skills, and it rewards patience. At Infonic Training we start from zero and say plainly which students this track suits.
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Students After 12th And In College
B.Tech, BCA, MCA and B.Sc IT students who want AI on the resume before campus placements start. Non-tech students are taken from zero — the first four modules assume you have never written a line of code.
Python Learners Going Further
If you have finished a Python course in Jaipur, this is the natural next step. The language stops being the subject and becomes the tool you use to train models.
Data Analysts And MIS Executives
Analysts, business analysts and MIS staff already living in Excel and dashboards. A data analysis course tells you what happened; machine learning lets you predict what happens next.
Software Engineers And Testers
Developers and QA engineers who want to move into ML roles. You already know code and version control, so most of your time goes into the maths, the modelling and the deployment side.
Career Switchers From Non-IT Backgrounds
B.Com, B.A. and MBA graduates entering IT. Be realistic about the timeline — this is a longer climb than a design or marketing course, and it is worth it only if you enjoy the problem-solving.
Working Professionals In Business Roles
Marketing, HR, finance and operations people who want to read a model, not build one blind. You leave able to judge whether an AI proposal is real or a slide deck.
Business Owners And Founders
Owners who want automation, forecasting and recommendation systems inside their own business instead of paying an agency to guess for them.
Data Science Aspirants
Students planning a data science course in Jaipur often start here, because the modelling half of data science is exactly what this syllabus covers in depth.
Anyone Comparing Technical Tracks
Not sure this is your field? Compare it against everything else on the technical courses in Jaipur list before you pay a rupee. The counsellor will tell you if a different track fits you better.
Using AI is not building AI
An assistant will hand you forty lines that run cleanly and score 99 per cent — because the answer leaked into a feature, and it has no idea. Somebody still has to frame the problem, question the dataset, catch the leakage, pick a metric the business actually cares about, and keep the thing working after launch. That judgement is the job, and it comes from doing the maths and the mistakes yourself. So we teach AI tools as a working module and then teach you to check everything they produce.
See all 20 modules →What you'll master
Eight skill groups, and each one ends in something you built yourself — a cleaned dataset, a tuned model, a network you wrote layer by layer, an endpoint that answers a request. Students who want to put these models behind working automations continue into the AI agents course in Jaipur.
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Python Built For Modelling
The Maths That Actually Matters
Data Wrangling With pandas
Classical Machine Learning
Honest Model Evaluation
Deep Learning
Applied AI Systems
Shipping The Model
Full curriculum
This is not a crash course. Python before maths, maths before models, models before neural networks, and deployment last — because every module hands the next one its foundation. Skip the order and deep learning becomes memorisation.
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How AI And Machine Learning Actually Work
What artificial intelligence is once the marketing is removed, the real difference between rules, machine learning and deep learning, where AI already runs in daily life, and the job roles this course is aimed at.
Python Programming For Machine Learning
Setting up Python, VS Code and Jupyter, then variables, conditions, loops, functions, files and error handling. You write and debug small programs yourself before any dataset appears.
The Maths You Actually Need
Linear algebra for vectors and matrices, probability and Bayes, and the statistics — mean, variance, distribution, correlation, hypothesis testing — that every model quietly depends on. Taught with numbers, not proofs.
NumPy And Vectorised Computation
Arrays, shapes, broadcasting, slicing and matrix operations. This is where the maths of the previous module turns into code that runs in milliseconds instead of minutes.
pandas For Real Datasets
DataFrames, indexing, filtering, groupby, merge and pivot. You work on actual messy files — a sales export, a survey sheet, a scraped table — not a tidy textbook dataset.
Data Cleaning And Preparation
Missing values, duplicates, wrong data types, outliers, inconsistent categories and date formats. Roughly seventy per cent of a real ML job is this module, so it gets the time it deserves.
Exploratory Data Analysis And Visualisation
Matplotlib and Seaborn, distribution plots, correlation heatmaps and pair plots. Learning to look at data properly before modelling it, and to explain what you found to someone non-technical.
Supervised Learning — Regression
Simple and multiple linear regression, polynomial fits, ridge and lasso. Cost functions, gradient descent and residual analysis, built up from a single line of best fit.
Supervised Learning — Classification
Logistic regression, k-nearest neighbours, naive Bayes, support vector machines, decision trees, random forest and gradient boosting including XGBoost. When to reach for which one.
Unsupervised Learning And Clustering
K-means, hierarchical clustering and DBSCAN, plus dimensionality reduction with PCA and t-SNE. Customer segmentation and anomaly detection built as working examples.
Model Evaluation And Validation
Train, validation and test splits, k-fold cross-validation, the confusion matrix, precision, recall, F1 and ROC-AUC. Bias-variance tradeoff, overfitting, underfitting and data leakage.
Feature Engineering
Encoding categories, scaling and normalisation, binning, date and text features, handling imbalanced classes with SMOTE, and feature selection. Usually the difference between a mediocre model and a good one.
scikit-learn End To End
Estimators, transformers, Pipeline and ColumnTransformer, GridSearchCV and RandomizedSearchCV for tuning, and saving a trained model with joblib so it can be reloaded and served.
Neural Networks From First Principles
Perceptrons, activation functions, hidden layers, loss functions, forward pass and backpropagation. You build a small network by hand before touching a framework, so nothing later feels like magic.
Deep Learning With TensorFlow And PyTorch
The same network written in both frameworks — Keras Sequential and functional models, PyTorch tensors, autograd and training loops. Optimisers, dropout, batch normalisation, callbacks and GPU training on Colab.
Computer Vision With CNNs
Image data handling with OpenCV, convolution and pooling, building a CNN, data augmentation, and transfer learning with ResNet and MobileNet. Image classification and a first look at object detection with YOLO.
NLP From Text Cleaning To Transformers
Tokenisation, stopwords, stemming and lemmatisation with NLTK and spaCy, then TF-IDF, word embeddings, sentiment analysis, and the attention and transformer architecture that everything modern is built on.
LLMs And Fine-Tuning Basics
How a large language model is actually trained, prompt engineering that holds up in production, embeddings and vector databases, retrieval-augmented generation, and fine-tuning a small open model with LoRA on your own data.
MLOps, Deployment And Monitoring
Wrapping a model in FastAPI, a Streamlit front end, Docker basics, Git and GitHub workflow, experiment tracking with MLflow, deploying to a cloud instance, and watching for data drift once it is live.
Capstone Project And Interview Preparation
One end-to-end problem taken from raw data to a deployed, documented model on GitHub — plus resume work, the maths and ML questions interviewers actually ask, and mock interviews.
Your portfolio
Nobody is hired in this field for a certificate. They are hired for a GitHub profile an interviewer can open. Every project below is a real dataset, a real evaluation and a repository with a README explaining what you tried and what failed.
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Project 01
House Price Prediction Model
A regression model on real Jaipur-style property data — area, locality, age, amenities. You clean the data, engineer features, compare four algorithms and explain why one won.
pandas · scikit-learn · Regression
Project 02
Customer Churn Classifier
A telecom or subscription dataset where you predict who leaves next month. Imbalanced classes, SMOTE, threshold tuning and a confusion matrix you can defend in an interview.
Classification · XGBoost · ROC-AUC
Project 03
Customer Segmentation Engine
Unsupervised clustering on retail transaction data to find genuine buyer groups, reduced with PCA and plotted so a business owner understands it without a data background.
K-means · PCA · Seaborn
Project 04
Handwritten Digit And Image Classifier
A convolutional neural network trained from scratch, then the same task solved again with transfer learning so you see exactly what a pre-trained model saves you.
TensorFlow · CNN · Transfer learning
Project 05
Sentiment Analysis On Live Reviews
Scraped product or restaurant reviews cleaned, vectorised and classified, then re-run through a transformer model to compare a classical pipeline against a modern one.
NLTK · spaCy · Transformers
Project 06
Movie Or Product Recommendation System
Collaborative filtering and content-based recommendation built side by side, evaluated properly, and served through a small interface a non-technical person can click.
Embeddings · Cosine similarity
Project 07
Fine-Tuned LLM Assistant With RAG
A small open model given your own document set through embeddings and a vector store, then lightly fine-tuned with LoRA. The project most students end up talking about at interview.
LLM · RAG · LoRA
Project 08
Deployed Capstone With A Live API
Your chosen problem taken all the way — cleaned data, tuned model, FastAPI endpoint, Streamlit front end, Dockerfile and a README on GitHub. Deployment is covered further in the cloud computing and DevOps course.
FastAPI · Docker · GitHub
Want these projects reviewed under a working brief with a college-format report? Continue into the computer science internship in Jaipur after the course.
Fees & tracks
Three tracks, one syllabus philosophy — the difference is how far into deep learning and deployment you go, and how much project review you get. Fees are printed here rather than saved for the counselling table. No exam fee, no material charge, no certificate charge.
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3-Month AI Foundation
₹14,000
Modules 1–10
6-Month ML Career Track
₹32,000
EMI from ₹5,400/mo · Modules 1–20
12-Month AI Engineering
₹52,000
EMI from ₹4,400/mo
Not sure this is the right depth for you? Compare it with the data analysis course, the data science course or the wider job oriented courses in Jaipur before you decide. Every programme above ₹15,000 can be paid through no-cost EMI.
Compare before you enrol
Visit two or three centres before you pay anyone. Ask each of them these five questions and watch how quickly the answers get vague.
| What to check | Typical institutes | Infonic Training |
|---|---|---|
| Fee for a full AI and ML track | ₹60,000 – ₹1,50,000+ | From ₹14,000 |
| The maths behind the models | Skipped, or one slide on 'don't worry about it' | A full module, taught with numbers you run |
| What you leave with | A certificate and a recorded notebook | 8 trained models and a GitHub profile |
| Batch size | 40–60 students, or a recorded cohort | 12–15 students, trainer at your screen |
| Deployment and MLOps | Not covered — the course ends at model.fit() | FastAPI, Docker and a live endpoint |
Tools you'll use
The same stack a working ML team opens every day — for exploring data, training and tuning models, handling images and text, tracking experiments and getting a model out of the notebook and into production.
Why students choose us
The complaint we hear most from students who join us after another AI course is always the same: the syllabus finished, the certificate arrived, and they still could not build a model without copying one. Our training is built to close exactly that gap.
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Maths Taught, Not Skipped
Most institutes hide the maths because it scares admissions away. We teach it with numbers and code you can run, because a model you cannot explain is a model you cannot debug.
You Type Every Line
No screen-sharing a finished notebook. Each concept is built in class on your own machine, errors included, because reading somebody else's working code teaches you almost nothing.
Small Batch Learning
Twelve to fifteen students per batch, so the trainer can reach every screen during a practical and actually see where your model is going wrong.
Assignments After Every Topic
Regular practice tasks with review, exactly as the source syllabus promises. Progress is checked module by module, and extra support is arranged before the batch moves on.
Paid Internship On Real Tasks
Students who clear their assessments move into a paid internship on real work. Students needing a college-format report take the computer science internship instead.
Placement Support, Honestly Framed
Resume work, GitHub review, mock interviews and referrals to hiring companies. We open the door — the interview, and the maths question inside it, is yours to clear.
Meet your trainer
Ankit Sharma
Lead Trainer, AI & Machine Learning · 9+ years
Ankit builds and maintains production models alongside teaching, so the examples in class come from a project that shipped this month — including the run where accuracy collapsed on live data. Students watch a model get debugged, not a slide showing the finished score.
Bring a dataset or a problem from your own work to the demo class and ask him whether machine learning is even the right tool for it. If it is not, he will say so.
Inside the campus
Jobs & salary
Start with the honest part. Jaipur has far more openings for data analysts and Python developers than for pure machine learning engineers, so most students here take a data role first and move across once the portfolio is strong. The bigger ML salaries are remote or in Bangalore, Pune, Hyderabad and Gurgaon — and remote hiring in this field is genuinely open to Jaipur candidates who can show working code. Students who want the analyst route mapped separately should look at the data analysis course in Jaipur.
| Role and experience | Jaipur monthly | Remote / metro monthly | What the role is judged on |
|---|---|---|---|
| ML / AI trainee or intern (0–6 months) | ₹8,000 – ₹15,000 stipend | ₹15,000 – ₹30,000 stipend | A working notebook and a GitHub profile. |
| Data Analyst or Python Developer (fresher) | ₹15,000 – ₹25,000 | ₹25,000 – ₹45,000 | Clean pandas work and honest reporting. |
| Machine Learning Engineer (1–2 years) | ₹30,000 – ₹55,000 | ₹55,000 – ₹95,000 | Models that survive real, unseen data. |
| Data Scientist (2–4 years) | ₹50,000 – ₹90,000 | ₹85,000 – ₹1,70,000 | Framing the problem, not just fitting it. |
| AI / LLM Engineer (3+ years) | ₹70,000 – ₹1,30,000 | ₹1,20,000 – ₹2,50,000+ | Systems in production that stay up. |
Read those bands honestly. They are ranges from the market, not promises from us, and nobody walks out of a three-month course into ₹1 lakh a month. What this field rewards is a maths-and-portfolio grind: a year of consistent practice, four or five models you can defend line by line, and a GitHub profile that shows the failures as well as the wins. The certificate opens no doors on its own. Anyone promising a guaranteed package to a fresher is not being straight with you. Students planning to take freelance model and automation work alongside a job should add the freelancing course in Jaipur.
An honest note: the placement cell handles resume building, GitHub review, mock interviews and referrals to hiring companies in Jaipur. It works for students who finish their projects and keep practising after the batch ends. We open the door — the interview, and the maths question inside it, is yours to clear.
Student reviews
Before joining any institute, most learners want to hear from someone who already sat in that classroom. These are words from students who finished the training.
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No reviews found.
Areas we serve
Our campus is on Ajmer Road at Heera Nagar Mode — 20 to 30 minutes from these localities, with city buses stopping close by. Students from nearby Rajasthan cities attend the live online batches with the same syllabus and the same trainer.
Nearby cities we serve
FAQ
Serious learners think carefully before giving months to any training — especially this one. These are the questions people actually ask at the counselling table.
You need to be willing to learn it, not already good at it. Module 3 rebuilds the linear algebra, probability and statistics you need from school level, taught with numbers and code rather than proofs. Be honest with yourself though: this is the one field on our list where the maths is not optional, and students who refuse to sit with it stall around the deep learning modules.
The foundation track runs about three months, the career track six months and the mastery track a full year. Classes are five days a week for roughly two hours, or a longer weekend batch for working people. Add one to two hours of practice at home every day — machine learning is a skill you lose fast if you only watch it being done.
The 3-month AI and Python foundation is ₹14,000, the 6-month machine learning career track is ₹32,000, and the 12-month AI engineering mastery track is ₹52,000. No-cost EMI is available above ₹15,000, and there is no separate exam, material or certificate charge.
Yes. Students from B.Com, B.A., MBA and other non-IT backgrounds finish this course every batch, and classes run in Hindi and simple English. The first four modules assume zero coding. What matters more than your degree is whether you will do the practice work between classes.
No. Everything up to deep learning runs on any normal laptop with 8GB of RAM, and the heavy training is done on Google Colab, which gives you a free GPU in the browser. Bring your own laptop if you have one; if not, tell the counsellor at admission so a machine can be reserved for your batch timing.
You train models. Prompting and AI tools get one part of one module because they are useful, but the rest of the syllabus is you writing the code — cleaning data, fitting regressions, building a CNN layer by layer, fine-tuning a small language model and deploying the result. Anyone selling an AI course that is only tool demonstrations is selling you a workshop, not a career.
Yes. The online batches are live, not recorded, with the same syllabus, same assignments and the same trainer. Students from Ajmer, Sikar, Kota, Alwar, Jodhpur and Jhunjhunu attend regularly, and screen sharing works well for debugging a notebook.
In Jaipur a fresher usually starts as a data analyst or Python developer at ₹15,000 to ₹25,000 a month, because pure ML roles for freshers are still few here. Remote and metro roles start higher, ₹25,000 to ₹45,000. With one to two years of proven model work, ₹30,000 to ₹55,000 in Jaipur and ₹55,000 to ₹95,000 remote is realistic. Anyone promising a guaranteed package to a fresher is not being straight with you.
Yes to both. The certificate is issued once you complete the training and the required practice work. The paid internship is for students who clear their assessments and puts you on real tasks. Be clear about one thing though: in this field the GitHub repository you can show gets you the interview, and the certificate supports it, not the other way round.
Yes, and the reason is simple. An AI assistant will happily produce a model that scores 99 per cent because the target leaked into the features, and it will not tell you. Somebody has to frame the problem, question the data, catch the leakage, judge whether the metric matters to the business and keep the thing running after launch. That judgement is the job, and it is what this syllabus is built to produce.
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Python Course
The language every model in this course is written in.
Start here if newData Science
Statistics, analytics and business framing around the models.
Closest neighbourAI Agents Course
Build agents and automations on top of the models you train.
Natural next stepData Analysis
Excel, SQL and dashboards — the fastest route to a first data job.
Quicker to a jobCloud & DevOps
Docker, CI/CD and cloud — how a model actually stays deployed.
For MLOpsCS Internship
Paid internship with a university-format report and certificate.
Rolling admissionsJob Oriented Courses
Every programme we run that is built around hiring, in one list.
Compare optionsAll Technical Courses
Development, data, cloud and design tracks side by side.
Browse the hubVisit the centre
Come to the Heera Nagar centre, meet the trainer, and watch a model get trained, scored and then broken on purpose in front of you. No pressure, and no charge for the demo.
Do not wait more. If you are willing to sit with the maths and put a year of honest practice behind you, this is where to start. The next batch begins on 1 September, in morning and evening slots.
Our placement team is available Mon–Fri, 9 AM to 6 PM
