Vikram Mali
Ahmedabad · Full Stack Development
Before
B.A. (2025)
After
Full Stack Developer
Ahmedabad · Felix ITs
Become a ML Engineer
Master supervised, unsupervised, and reinforcement learning. Build production-ready ML models using Python, Scikit-learn, and TensorFlow.
₹50,000
₹8,334/mo · 0%
New batches starting soon in Ahmedabad. Book a demo to get notified.
Felix ITs' Machine Learning course in Ahmedabad covers ML fundamentals, neural networks, NLP, and AI deployment over 5 months. Ahmedabad's pharmaceutical, logistics, and GIFT City analytics teams are building ML capabilities, creating growing demand for ML engineers in the city. Includes applied business projects and 100% placement support.
Duration
4 months
Placed
1,000+
Mode
Online + Offline
| Experience |
|---|
What You Get
Everything you need to go from beginner to job-ready — not just a certificate.
Real Dataset Model Training
Cloud Deployment (AWS / GCP)
100% Placement Assistance
Industry-Ready Toolkit
Master the exact tools used in top teams. Every tool in the Machine Learning curriculum is live, hands-on, and employer-valued.
Career Outcomes
10,000+ Machine Learning students placed across Pune, Mumbai & Ahmedabad. Over 550 companies actively hire from Felix ITs.
One course. One job. Course fee back in your first 2 months.
10,000+
Students Placed
550+
Hiring Partners
94%
Placement Rate
45 days
Avg Time to Offer
Hiring Companies Include
Industry Recognition
Graduate with a Felix ITs certificate that carries real weight with employers in Ahmedabad and across India. Every certificate includes your name, course, batch date, and a verifiable unique ID — proof that you earned it.
Investment
Flexible batches, 0% EMI, and merit scholarships — making quality Machine Learning training accessible for every learner.
Starting From
₹50,000
or as low as ₹8,334/mo at 0% interest for 6 months
ML engineers command ₹8–20 LPA — demand is growing 40% year on year.
No commitment. Counsellor will walk you through all options.
Weekday Batch
Mon–Fri · Morning or Evening slots
Student Stories
Hear from graduates who transformed their careers with Felix ITs.
Why Felix ITs
See how our course stacks up against generic bootcamps and online platforms.
| Feature | Felix ITsYou’re Here | Bootcamps (e.g. Simplilearn) |
|---|---|---|
| Duration | 4 Months | 6–12 Months |
| 35+ AI & Design Tools | ||
| 100% Placement Assistance |
Training Centre
Centrally located in C G Road, Near Panchvati Circle, Ahmedabad 380006.
FAQ
About Machine Learning in Ahmedabad
Yes, but fewer than in Pune or Mumbai. GIFT City fintech ML (credit scoring, fraud detection), pharma ML (manufacturing quality, clinical data), and SG Highway AI startups are the main demand sources. Many ML graduates from Ahmedabad also target remote roles at Pune and Mumbai companies.
ML engineering freshers start at ₹4.5–8 LPA locally. Remote roles at Pune and Mumbai companies often pay market-rate salaries of ₹6–12 LPA for Ahmedabad-based candidates. Mid-level ML engineers earn ₹10–20 LPA.
Yes. The placement team specifically targets GIFT City ML roles and remote opportunities at Mumbai and Pune product companies, in addition to local Ahmedabad AI startups.
Basic Python programming experience and comfort with basic mathematics are recommended. The course covers ML-specific mathematical concepts from first principles, but students who are completely new to programming are better served starting with the Data Analytics or Data Science course first.
Yes. Weekend batches run Saturday-Sunday. The ML course runs over approximately 4 months in weekend format.
Curriculum
Built for people who want to specialise deeply in machine learning, not survey it
5
Modules
150
Hours of content
6
Live projects
35+
Tools covered
100%
Hands-on from Day 1
Types of Machine Learning
Vectors, matrices, and matrix operations are the language of every ML algorithm — covered with the specific operations used in real models, not abstract linear algebra
Python for ML
Gradient descent and optimisation is the single most commonly asked "explain it from scratch" question in ML interviews — you will be able to derive and code it
Linear Algebra & Calculus Review
Cost functions and loss functions (MSE, cross-entropy) are asked about constantly — understanding why each is used for its respective problem type is the expected standard
Data Preprocessing
Building a model from scratch with NumPy before using a library is what makes the library use meaningful rather than magical
What you will build
A from-scratch implementation of gradient descent and a simple linear regression model using only NumPy — no scikit-learn — to internalise what the library is actually doing
ML interviews at product companies frequently ask candidates to explain gradient descent or derive a cost function — candidates who only know the scikit-learn API struggle here
Types of Machine Learning
Vectors, matrices, and matrix operations are the language of every ML algorithm — covered with the specific operations used in real models, not abstract linear algebra
Python for ML
Gradient descent and optimisation is the single most commonly asked "explain it from scratch" question in ML interviews — you will be able to derive and code it
Linear Algebra & Calculus Review
Cost functions and loss functions (MSE, cross-entropy) are asked about constantly — understanding why each is used for its respective problem type is the expected standard
Data Preprocessing
Building a model from scratch with NumPy before using a library is what makes the library use meaningful rather than magical
What you will build
A from-scratch implementation of gradient descent and a simple linear regression model using only NumPy — no scikit-learn — to internalise what the library is actually doing
ML interviews at product companies frequently ask candidates to explain gradient descent or derive a cost function — candidates who only know the scikit-learn API struggle here
Regression Algorithms
Support Vector Machines and kernel methods are a step beyond the generalist data science curriculum — covered here with the math behind kernel tricks
Classification Algorithms
XGBoost and gradient boosting are the most widely used production algorithms in real ML systems — covered with hyperparameter tuning, not just default settings
Ensemble Methods
Bias-variance tradeoff is asked about in essentially every ML interview — you will be able to diagnose it from a learning curve, not just define it
Model Tuning
Hyperparameter tuning (GridSearch, RandomSearch, Bayesian optimisation) is what separates a notebook model from a competition-grade one
What you will build
A model comparison study on a single dataset using Linear/Logistic Regression, SVM, Decision Trees, Random Forest, and Gradient Boosting (XGBoost) — with a documented analysis of which performed best and why
ML-specialist roles expect deeper algorithm knowledge than generalist data analyst roles — interviewers test trade-offs (kernel choice in SVM, tree depth in Random Forest) in detail
Clustering
Overfitting vs underfitting diagnosis via learning curves and validation curves is tested with real plots in interviews, not just definitions
Dimensionality Reduction
Data leakage is one of the most common silent failures in real ML projects — recognising the patterns that cause it is a skill experienced practitioners are specifically hired for
Anomaly Detection
Class imbalance techniques (SMOTE, class weighting, resampling) are asked about whenever a dataset resembles fraud detection or churn prediction — extremely common interview scenario
Q-Learning Basics
Cross-validation strategies (k-fold, stratified, time-series split) chosen correctly for the problem type is a detail that distinguishes ML specialists from generalists
What you will build
A documented model debugging case study — deliberately introducing overfitting, data leakage, and class imbalance into a project, then diagnosing and correcting each one with the appropriate technique
"Your model is performing poorly in production — what do you check?" is one of the most common senior ML interview questions — most candidates have never practiced this diagnostic process
Neural Network Architecture
Forward propagation, backpropagation, and activation functions are asked about conceptually in nearly every ML specialist interview — covered here with worked examples, not just diagrams
Backpropagation
CNNs and their use in image tasks are the most common practical deep learning project in interviews and portfolios — built here on a real dataset
CNNs & RNNs
Overfitting in deep learning (dropout, batch normalisation, early stopping) is a distinct skill from classical ML regularisation — covered specifically for neural networks
Transfer Learning
TensorFlow/Keras is the most widely deployed deep learning framework in Indian industry roles — hands-on fluency, not just familiarity, is the goal here
What you will build
An image classification model built with a Convolutional Neural Network (CNN) using TensorFlow/Keras, trained and evaluated on a real image dataset — with a from-scratch single-layer network built earlier for comparison
Deep learning fundamentals are now expected baseline knowledge for ML specialist roles even outside pure AI teams — companies want to know you understand neural networks beyond the buzzword
Model Versioning with MLflow
Model serialisation and serving via REST API is the minimum production skill expected from an ML specialist in 2025
Feature Stores
Concept drift and data drift are increasingly asked about in ML interviews as companies have been burned by silently degrading models in production
Model Monitoring
Docker and containerisation for ML models is now a common requirement in job postings, even at companies that previously only needed it for software engineers
Deployment Pipelines
Experiment tracking (even basic logging of metrics and parameters) is a discipline that distinguishes a hobby project from an industry-grade one
What you will build
A deployed ML model served via FastAPI, containerised with Docker, with basic logging and a simple drift-monitoring check comparing live input distributions to training data
MLOps awareness is now a standard expectation even for "pure" ML roles — interviewers ask how you would detect a model degrading in production, not just how you trained it
By the end of this course
You will understand the mathematical intuition (linear algebra, calculus, optimisation) behind machine learning algorithms — not just how to call .fit() on them
You will be able to build, tune, and evaluate models across regression, classification, clustering, and an introduction to neural networks
You will know how to diagnose and fix common ML failure modes — overfitting, underfitting, data leakage, class imbalance — the practical skills that separate working models from broken ones
You will have hands-on experience with deep learning fundamentals (neural networks, backpropagation) using TensorFlow/Keras or PyTorch
You will be able to take a model from experimentation to a deployed, monitored service — and explain every step of that pipeline in an interview
What our graduates say about the curriculum
“I wanted to specialise in ML specifically, not just "data science in general". The deep learning module — actually building a neural network from scratch before using Keras — gave me an understanding most candidates in my interviews clearly did not have.”
“The model evaluation and debugging module was the most valuable part. Knowing how to explain why a model was overfitting, and what I did to fix it, was the exact question that came up in my technical interview.”
| Salary Range |
|---|
| ML Engineer Fresher | ₹5–8 LPA |
| ML Engineer (2–4 yr) | ₹10–18 LPA |
| Senior ML Engineer (4–6 yr) | ₹18–30 LPA |
| ML Architect / Lead (6+ yr) | ₹28–50 LPA |
Industry-Recognised Certificate
Kaggle & Live Competition Projects
ML Engineer Mock Interviews
50+
Live sessions
35+
AI tools
6
Month program
Train models on real datasets and deploy to AWS SageMaker or GCP — go from theory to production.
Work with OpenAI, Hugging Face, and LangChain — the fastest-growing specialisation in tech right now.
Benchmark, evaluate, and improve models — the difference between a hobbyist and a professional ML engineer.
Bias, fairness, and interpretability — every ML engineer at a serious company is expected to understand this.
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Certificate awarded on successful course completion and project submission.
This certifies that
Your Name Here
has successfully completed
Machine Learning
Issued by
Felix ITs
Batch
2026–27
City
Ahmedabad
Weekend Batch
Sat–Sun · Full Day
Working ProfessionalsFast-Track Batch
Mon–Sat · Intensive
Quick Career Switch0% EMI — ₹8,334/mo for 6 months
Easy monthly installments through Razorpay, HDFC & Bajaj Finserv. No hidden charges.
Merit Scholarships — Up to 20% Off
Early enrollment and aptitude test toppers qualify. Ask our counsellors for details.
Seats are limited per batch. Fee confirmed at the time of enrollment. Cancellation policy: full refund within 7 days of enrollment if the batch has not started.
Salary range after this course: ₹8 – ₹20 LPA
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M.Com / Graphic Designer
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“Best Software Testing course in Ahmedabad. The automation module with Selenium is extremely hands-on. Got placed within 45 days.”
| Varies |
| Offline Batches in Pune / Mumbai |
| Weekend Batches Available |
| Max 15 Students per Batch |
| 3 Live Industry Projects | 1–2 |
| 0% EMI Financing | Sometimes |
| Dedicated Mentor Access |
| Lifetime Alumni Network |
Centre Hours
Mon–Fri 8am–8pm, Sat–Sun 9am–7pm