Samridh Vikhas - Machine learning tutor - Madurai
1st lesson belanja
Samridh Vikhas - Machine learning tutor - Madurai

Samridh Vikhas's profile, qualifications and contact details have been verified by our experts

Samridh Vikhas

  • Rate RM51
  • Response 1h
  • Students

    Number of students accompanied by Samridh Vikhas since their arrival at Superprof

    12

    Number of students accompanied by Samridh Vikhas since their arrival at Superprof

Samridh Vikhas - Machine learning tutor - Madurai
  • 5 (5 reviews)

RM51/h

1st lesson belanja

Contact

1st lesson belanja

1st lesson belanja

  • Machine learning

Machine Learning from the foundations up — for students who want to understand their models, not just run them

  • Machine learning

Lesson location

Recommended

Samridh Vikhas is a respected tutor in our community. They are highly recommended for their commitment and the quality of their lessons. An excellent choice to progress with confidence.

About Samridh Vikhas

I'm a data analyst who works with real data every day — building pipelines, cleaning things that arrive broken, and producing analysis that people make decisions on. That's the ground machine learning actually stands on, and it's the part most ML courses skip past in the rush to fit a model.

Before this I was a Chegg subject matter expert in probability and statistics. That matters here more than it sounds: almost every genuine difficulty in machine learning is a statistics difficulty wearing different clothes. Overfitting is a variance problem. Train-test splitting is a sampling problem. Every metric you'll be asked to justify in a viva or an interview is a conditional probability. Students who struggle with ML are usually struggling with the statistics they were never taught properly.

I'll be straight about scope: I teach foundations and applied fundamentals well. I'm not a deep learning researcher, and if you need cutting-edge work in that area I'll tell you so rather than take your money.

I've tutored math, statistics and programming since my undergraduate years, in English, Hindi and Tamil.

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About the lesson

  • Bachelor
  • Masters
  • Diploma
  • +4
  • levels :

    Bachelor

    Masters

    Diploma

    Beginner

    Intermediate

    Advanced

    Professional

  • English

All languages in which the lesson is available :

English

Most people learn machine learning backwards — they can call model.fit() within a week, and still can't say why their accuracy of 94% is meaningless. I teach it the other way round.

What we cover:

The foundations — what a model actually is, bias and variance, overfitting, train/validation/test splits, cross-validation, and why data leakage quietly ruins more projects than bad algorithms do
Supervised learning — linear and logistic regression, decision trees, random forests, gradient boosting, k-nearest neighbours
Unsupervised learning — k-means clustering, hierarchical clustering, PCA and dimensionality reduction
Evaluation — precision, recall, F1, ROC-AUC, confusion matrices, and choosing the metric that matches the actual decision being made
The unglamorous 80% — feature engineering, handling missing data, encoding categoricals, scaling, and dealing with imbalanced classes
Tools — Python, pandas, scikit-learn, matplotlib; R where your syllabus requires it

How I teach: every algorithm gets the intuition first, then the mathematics, then an implementation on real data. We work on messy datasets rather than the clean teaching ones, because clean data is the single most misleading thing about most ML courses.

Who this suits: BTech, MCA, MSc and MBA students with machine learning or data science papers; final-year students building ML projects; and working professionals who want foundations solid enough to build on rather than a certificate.

Also available for: project and capstone supervision, dissertation modelling chapters, and viva preparation — where the question is almost never "does it run" but "why did you choose that, and what would you do differently."

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Rates

Rate

  • RM51

Pack rates

  • 5h: RM243
  • 10h: RM464

free lessons

This first lesson is free to allow you to get to know your teacher so that they can best meet your needs.

  • 1hr

online

  • RM51/h

Details

My standard rate is ₹1,200 per hour for online classes. No webcam fee, no travel fee, no materials charge. All code and notebooks from every session are yours to keep.

Packages:
5 hours — ₹5,750 (₹1,150 per hour)
10 hours — ₹11,000 (₹1,100 per hour)

For machine learning specifically I'd suggest the 10-hour package. Five hours is enough to cover a syllabus or fix a specific project; it isn't enough to build foundations from scratch, and I'd rather say that now than halfway through.

A note before you book: if your statistics is shaky, we'll spend the first session or two there. It's not a detour — it's the only thing that makes the rest stick, and skipping it is why so many people finish an ML course unable to explain their own results.

Free first class:
The first hour is free with no obligation. Send your syllabus, project brief or a description of what you're trying to build, and I'll come having read it. You'll get an honest assessment of how much time it will realistically take.

Payment:
After each session or at the start of a package, by UPI or bank transfer. Reschedules are free with a day's notice.

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