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After 12th · 1 Year Certificate · TechCadd Amritsar
Machine Learning & Deep Learning course in Amritsar
Machine Learning & Deep Learning course in AmritsarClassical ML first, neural networks second, with vision and language projects trained on hardware you have access to.
1 yearBeginner to advancedClassroom & live online
4.7/5
Student rating
9
Structured modules
3+
Portfolio projects
12th pass
Entry requirement
Course Overview
TechCadd's Machine Learning & Deep Learning in Amritsar is aimed at students who have just finished 12th — whatever they have or have not done before. The early modules cover Core Machine Learning Concepts, Model Evaluation and Core Algorithms, so nothing later assumes knowledge you were never given. From there you take on Artificial Neural Networks and Convolutional Neural Networks. The second half is where it turns practical: you work in Python 3, NumPy, pandas, scikit-learn, TensorFlow and Jupyter the way a team actually does, against briefs that carry a real deadline. Each module ends in something you have built and a trainer has reviewed, so you finish with a portfolio, a CV built around it and interview practice for Junior ML Engineer, Deep Learning Associate and Computer Vision Trainee roles.
Programme facts
- LevelBeginner to advanced
- ModeClassroom & live online
- Eligibility12th pass, any stream
- BatchesMorning, evening & weekend
- CertificationTechCadd industry certificate
- InternshipIncluded — second semester
Not sure this is the right track?
A counselling call maps your background against the syllabus before you commit to anything. It takes about twenty minutes.
Book a counselling callWhat the Machine Learning & Deep Learning programme actually covers
Deep learning is taught after classical models on purpose — knowing when a simpler model wins is the judgement that separates a practitioner from a tutorial follower.
The machine learning & deep learning programme runs across 1 year at TechCadd Amritsar and is built for students who have just finished school — no degree, no prior experience and no coding background assumed. Two hours a day, five days a week, in batches small enough that your work is looked at individually.
You can take it as a standalone career course, run it alongside a degree in the evening or weekend batch, or use it to fill a drop year with something a recruiter will actually read. Whichever route you take, the finish line is the same: a certificate, 3 reviewed projects and a portfolio you can talk through in an interview.
- 12th pass, any stream, and no entrance test
- Every concept is built before it is examined
- Mentors are working practitioners, not career trainers
- Lab access and doubt sessions outside batch hours
Module by module
Open any module and see exactly what happens inside it
Module by module curriculum9 modules, each ending in something reviewable that goes into your portfolio.
Module 01 of 09AI & Data
Core Machine Learning Concepts
Core Machine Learning Concepts is where the Machine Learning & Deep Learning track gets its footing. You work the concepts in the lab first, then carry them straight into the running project rather than leaving them as isolated exercises.
Skills you build
- ML against traditional programming
- Supervised, unsupervised, reinforcement
- Data preprocessing and feature engineering
Tools in this module
Python 3NumPypandasscikit-learn
You finish with
A reviewed piece of work demonstrating ml against traditional programming and supervised, unsupervised, reinforcement.
How the programme is paced
Three stages, in the order a working practitioner learns them
Programme stagesStage one
Foundations
Fundamentals, vocabulary and the first working artefacts. Nothing is assumed; everything is implemented.
Modules 01 – 03
- 01Core Machine Learning ConceptsML against traditional programming · Supervised, unsupervised, reinforcement · Data preprocessing and feature engineering
- 02Model EvaluationAccuracy, precision and recall · Train and test splits · Choosing the right metric for the problem
- 03Core AlgorithmsLinear and logistic regression · Decision trees and random forests · SVM, KNN and k-means clustering
Stage two
Core practice
The professional middle of the course. Real tooling, realistic inputs, and mentor review on every submission.
Modules 04 – 06
- 04Artificial Neural NetworksHow ANNs mimic learning · Activation functions · Backpropagation and optimisation
- 05Convolutional Neural NetworksImage and video processing · Layers and filters · Building an image classifier
- 06Recurrent Neural NetworksSequential and time-series data · Sequence modelling · Where RNNs beat CNNs
Stage three
Advanced & capstone
Depth, deployment and the capstone build that becomes the centre of your portfolio and your interview answers.
Modules 07 – 09
- 07Transfer Learning & NLPUsing pre-trained models · Fine-tuning for a new problem · Text-based AI applications
- 08Deployment & PresentationModel deployment basics · Structured and unstructured datasets · Presenting project work in an interview
- 09Capstone ProjectA predictive model · An image recognition system · Sentiment analysis with NLP
Finish this machine learning & deep learning programme and you leave Amritsar with more than a certificate from your 12th year: a reviewed portfolio, an internship letter where the track applies, and a first role that is realistic rather than aspirational.
Tools you will actually use
The working stack for Machine Learning & Deep Learning
Tools and technologies coveredEvery tool below is installed, configured and used by you during the course — not demonstrated on a slide. You leave able to set up your own environment from scratch.
8tools covered
Core
- Python 3
- NumPy
- pandas
Working set
- scikit-learn
- TensorFlow
- Jupyter
Shipping
- Google Colab
- Git
Eligibility
Four kinds of people join this batch. You are probably one of them.
Who this course is forThere is no entrance test and no prerequisite degree. What the programme does assume is that you will show up for the full 1 year and do the project work.
01No experience needed
Straight out of 12th
The programme assumes nothing beyond a 12th pass and comfort with a computer. You start at the actual beginning of machine learning & deep learning, and by month three you are working on a brief rather than an exercise.
02Evening & weekend
Studying alongside a degree
Two hours a day fits either side of a college timetable. You graduate with a machine learning & deep learning portfolio already built instead of starting one after your final semester.
03Fills the year
Taking a drop or gap year
An empty year reads badly on a CV. A certificate, 3 reviewed projects and an internship letter turn it into something you can explain in an interview.
04Applies immediately
Joining a family business
Bring the business you already know and layer machine learning & deep learning onto it. Students in this group usually apply the first two modules inside their own shop or unit before the course has ended.
Where it leads
The roles this portfolio opens
Job titles vary by company, but the underlying expectations do not. Each destination below maps to work you will have already done during the programme.
What you build
Projects that answer the interview question for you
Portfolio projectsEach project is reviewed individually, and the review notes go into your portfolio documentation.
Flagship project Text Classification Service
A language model fine-tuned and served behind an API with measured latency.
- TensorFlow
- Jupyter
- Google Colab
Deployed Capstone Demo
A trained model wrapped in an interface someone outside the class can use unaided.
- Google Colab
- Git
- Python 3
Future scope
Where machine learning & deep learning is headed from here
Where machine learning & deep learning is headed from hereA certificate answers what you can do today. This is the honest answer to what machine learning & deep learning looks like three to five years out, and why the fundamentals this course spends real time on are what carry you there.
- Demand is structural, not seasonalEvery company sitting on years of data is now under pressure to turn it into forecasts, automation and decisions — and that pressure is still climbing, not levelling off.
- The skill ladder keeps climbingJunior ML Engineer is the entry rung, not the ceiling. Once that portfolio is in place, deep learning associate is the realistic next step — a promotion earned on the job, not a second course you have to go back and pay for.
- Tooling changes; fundamentals compoundPython 3 and the rest of the stack will look different in five years — they always do. What does not expire is the fundamentals this course is built around, which is why the syllabus is reviewed each intake instead of frozen once and reused.
- Remote and hybrid widen the marketA machine learning & deep learning portfolio built in Amritsar competes for the same remote and hybrid roles as anywhere else. Companies hiring for this work are increasingly indifferent to which city the offer letter is posted to.
The 1 year programme is built around the part that is genuinely in your hands — and you leave holding all of it.
A reviewed portfolio, an industry-recognised certificate and a fundamentals-first foundation you can keep building on. Markets move, as they always have; that foundation is exactly what lets you move with them as AI and data work around you keeps shifting.
Why choose Machine Learning & Deep Learning
Why students choose techcadd
Why students choose techcaddTwo decades of training in Amritsar, in a format that has not changed since: small batches, real projects, mentors who still work in the field.
- 01
Practitioner-led teaching
Your mentor works in machine learning & deep learning for a living. The examples in class come from that work, and so do the shortcuts.
- 02
Small, fixed batches
Batch sizes are capped so a raised hand gets answered in the session it was raised in, not weeks later.
- 03
Reviewed, not marked
Every submission comes back annotated, line by line. The notes are the point — they are what you carry into the interview.
- 04
Current tooling
We teach Python 3, NumPy, pandas and refresh the list each intake, because a stale stack is worse than no stack.
- 05
Flexible batches
Morning, evening and weekend tracks, classroom or live online, with recordings either way for revision.
- 06
Support that continues
Resume and portfolio review, mock interviews and hiring-drive access — and it does not stop the day the course ends.
Student reviews
What the last few batches actually said
Machine Learning & Deep Learning course reviewsCollected from students who completed the machine learning & deep learning programme at the Amritsar campus, across morning, evening and weekend batches. Hover a card to slow the row right down and read it.
4.7
out of 5
179 verified reviews from Amritsar students
- 5★
- 85%
- 4★
- 7%
- 3★
- 5%
- 2★
- 2%
- 1★
- 1%
I joined the Machine Learning & Deep Learning batch with almost no background, and what made the difference was that core machine learning concepts was taught by building rather than by slides. By the third week I was debugging my own code instead of copying someone else's.
The project reviews are the real value. My text classification service was picked apart line by line, and those notes are exactly what I ended up talking through in the interview that got me a deep learning associate offer.
Weekend batches meant I kept my job through the whole 1 year. Anything I missed got re-explained without fuss, and lab access outside batch hours was never a problem.
pandas and the rest of the stack were set up on day one, so no week went into environment issues. Batches are small enough that a doubt gets answered the same day instead of piling up.
I had tried learning machine learning & deep learning on my own twice and stalled both times. A fixed batch in Amritsar, a mentor who checks your work and a deadline on every module is the only reason I finished.
Placement support was not just a line on the brochure — resume and portfolio review, two mock interviews with people who do the job, and a referral into one of the hiring drives.
The syllabus is current. We worked in NumPy rather than the older tooling most machine learning & deep learning syllabi around here still teach, and that came up directly in my first interview.
The balance of theory to lab time is about right: enough to understand why something works, then straight into building. I left with 3 projects I can demo, not just a certificate.
Questions we get asked at the admissions desk
Frequently asked questionsIf something here is not covered, the Amritsar desk will answer it directly — no call-back queue.
Yes. The entry requirement is 12th pass, any stream, and there is no entrance test. Admissions run through the year rather than in a single intake window, so you can start in the gap between your result and college admissions closing.
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Next batch, Amritsar campus
Start the Machine Learning & Deep Learning programme this intake.
Enquire about the Machine Learning & Deep Learning courseSend your question and a counsellor will call you back about batch timings, fees, EMI options, placement record, or whether this course fits your background.
- Free career counselling
- No registration fee
- Placement support included
Request a call back
About the Machine Learning & Deep Learning course · 1 year
