Machine Learning Classification Course at Wisen IT Solutions, Chennai, India, develops practical skills for the prediction problems businesses actually run — fraud, churn, risk and moderation, where the interesting class is rare. Learn stratified validation, linear and ensemble classifiers, imbalanced learning, threshold selection, probability calibration, tuning, interpretation and error analysis through project-focused, AI-Assisted Learning.
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Wisen IT Solutions, Chennai, India
Think with AI. Don't Depend on AI.
Separate. Calibrate. Decide.
Build practical classification skills for the AI-Era through hands-on training in problem framing and class balance, stratified splitting, linear classifiers, trees and boosting ensembles, kNN, naive Bayes and SVMs, imbalanced learning with resampling and class weights, evaluation metrics in depth, probability calibration, hyperparameter tuning, interpretation and error analysis, and a full classification project. Learn to choose a threshold rather than accept a default, develop project-ready, AI-ready ML skills, and open new career opportunities in data science.
Framing a Classification Problem Topics
Framing a Classification Problem Topics
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Moving Beyond
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Build classification capability through AI-Enabled Learning across stratified validation, ensemble models, imbalanced learning, threshold selection, calibration and error analysis. Wisen’s Classification Training combines AI-Assisted Learning for the concepts with AI-Paired Training for practical modelling, so your team can defend a decision threshold to risk, compliance and the business rather than shipping a default of 0.5.
Develop skills aligned with the fraud, churn, risk and moderation problems teams actually own.
Use AI to accelerate error analysis without replacing statistical judgement.
Structured paths for new hires and for analysts moving into predictive modelling.
Practise resampling, class weights, threshold tuning, calibration and slice analysis.
Align the syllabus with your data, your imbalance ratios and your cost of error.
Evaluate practical progress through AI-assisted assessments that catch metric misuse.
Develop practical classification capability, learning to frame a decision problem, handle the imbalance that makes it hard, choose a metric that reflects real cost, and set a threshold you can justify to the people who act on it.
Define the positive class and the cost of each kind of mistake before modelling.
Split so that rare classes survive in every fold, and groups do not leak across.
Fit, regularise and interpret the model that remains the right answer surprisingly often.
Control depth and splitting, and read what a tree has actually learned.
Tune learning rate, depth and early stopping across the major boosting libraries.
Apply class weights, resampling and SMOTE correctly — inside the fold, never outside.
Use precision, recall, F-beta, ROC AUC and average precision for what each is good at.
Diagnose a model from its errors rather than from a single headline number.
Pick an operating point from a precision-recall curve and the business cost.
Make predicted probabilities mean what they claim, with Platt or isotonic scaling.
Search with custom scorers without leaking the test set into the decision.
Explain a classification with permutation importance, PDP and SHAP.
Inspect misclassified cases and check performance per segment.
Use AI to accelerate failure investigation while validating every claim against your results.
Test whether the model performs differently across groups, and report it.
After completing the training, participants can build a classifier for an imbalanced, real-world problem, defend its metric and threshold, calibrate its probabilities, and explain exactly which cases it gets wrong.
Most training providers set their own test and mark their own paper. We do not. At the end of each stage of the Machine Learning Classification Training you check your own readiness using your own ChatGPT, Claude, Gemini or other AI account. Wisen does not write the questions, does not see your answers, and does not record your score.
The reasoning is straightforward. A score we control proves very little — to an employer, or to you. A score produced by a tool we have no influence over is worth something. You ask the AI to test you on Machine Learning Classification, it decides what to ask, and the result belongs to you alone.
Seventy per cent is the mark we treat as ready. Score seventy or above and you move on to the next stage. Score below it and we work through the gap with you: identify what was missed, teach it again, practise it, then go back to the AI and check. You repeat that loop as many times as it takes.
Classification training aimed at the problems that are actually imbalanced. There is no job guarantee — there is a curriculum built on the cases that break naive models.
Choosing a threshold, reading a precision-recall curve and explaining why accuracy is the wrong metric are two-year-level conversations. You practise all three.
Get expert guidance to build a strong, professional resume that highlights your skills, projects and achievements.
Build real-world projects and a strong portfolio that demonstrates your practical skills to potential employers.
You will be given a fraud-shaped problem where the positive class is one per cent. We rehearse the metric argument and the resampling one.
Classification underpins risk, fraud, churn and moderation roles. We help you identify which of those hire from your background and how to approach them.
We assist you in identifying relevant opportunities and connecting with potential employers.
Your learning, projects and skills are verified by our Independent AI Verification System to ensure objective and unbiased evaluation.
Boosting libraries change; calibration, thresholding and cost-sensitive evaluation do not. Learn those and the next algorithm is a drop-in.
Employment is not guaranteed here. Your result depends on your practice, your assessments and your interviews. We stay in your corner throughout.
The learning materials for this Classification Course are developed from 27+ years of Python and data training experience, refined across classroom batches, corporate Machine Learning Training programs, learner questions, lab reviews, and models built for real client projects.
Every chapter, notebook, lab activity and exercise in the Classification Online Course is built on genuinely imbalanced data — a fraud set where the positive class is under one per cent, a churn set where the obvious feature is recorded after the customer left — so the metric argument and the threshold argument are real ones.
Structured explanations of metrics, imbalance handling and calibration, written for study after each session.
Walkthrough labs that take one imbalanced dataset from baseline to a defended operating point.
Independent tasks on resampling, threshold tuning, calibration and slice analysis.
Fraud, churn and risk-shaped data where accuracy is actively misleading.
Topics sequenced from framing to deployment, so each session builds on the one before it.
Quick reference for metric formulas, averaging modes and resampler options.
Written by trainers who taught statistical modelling long before ML was a job title.
Created and maintained by practising trainers, not assembled from generated text.
Not copied from library documentation, books, or generic online ML tutorials.
Every concept arrives with a dataset to model and a threshold to defend.
Updated as the library evolves, covering current APIs and the modern boosting libraries.
AI supports grammar, readability and presentation; the teaching content stays human.
Our Commitment
The classification topics listed on this website reflect the actual learning journey delivered in our live instructor-led online sessions. We follow the published sequence and enrich it with extra imbalance case studies, threshold arguments and error post-mortems whenever they help the batch. Whether you join a Machine Learning Training in Chennai batch or attend from elsewhere, the published order is what gets taught.
Classification Training is evaluated twice over, independently. An experienced trainer assesses how you reason about cost and evidence, and an independent AI evaluation reviews the code you write, so you learn where your methodology is wrong as well as where your syntax is.
Classification is unusually easy to fake: on a dataset with one per cent positives, predicting "no" every time scores ninety-nine per cent accuracy. This course puts both evaluations to work on exactly those failures.
Our experienced trainers evaluate your ability to:
State what a false positive and a false negative each cost, before modelling.
Defend a metric choice rather than reporting whichever looks best.
Apply weights or resampling correctly, and only inside the training fold.
Choose an operating point from evidence and explain the trade-off it makes.
Recognise when a probability needs to be trustworthy, not just ranked.
Diagnose a model from its errors, per class and per segment.
Inspect misclassifications and form a hypothesis about the cause.
Check and report performance differences across groups.
Explain a classification decision to the person who has to act on it.
Our independent AI evaluation reviews your classification code to assess:
Verify correct use of estimators, scorers and resampling pipelines.
Analyse whether the experiment supports the conclusion drawn.
Evaluate adherence to current conventions taught in the course.
Review readability, reproducibility and random-state handling.
Identify resampling before splitting, leaked features and unstratified folds.
Flag oversized searches and needless refits.
Recommend improvements based on modern classification standards.
Evaluate whether the notebook would survive a peer review.
Human trainers evaluate how you reason about cost and defend your operating point.
AI independently reviews the code for leakage, resampling errors and reproducibility.
Together they separate a high score from a model that will actually be useful.
By combining Human Evaluation with Independent AI Evaluation across our Machine Learning Training in Chennai and online, you will:
Machine Learning Training in Chennai and online is delivered in live batches with two pace options, so the schedule bends around your commitments instead of competing with them.
Total Learning Hours
50 - 55 HoursInstructor-led concept sessionsImbalance and resampling labsMetric, threshold and calibration labsEnd-to-end classification project
A balanced rhythm that leaves time to rerun every experiment between sessions.
A concentrated schedule for learners who want the Classification Online Course finished sooner.
Live Instructor-Led Training
Concept Deep Dives
Hands-on Modelling
Imbalance Lab Activities
Threshold Tuning Sessions
AI-Assisted Learning
Independent Code Evaluation
Doubt Clarification
ML Project Guidance
The Classification Course content is identical on both tracks — the course you join in Chennai or online differs only in how quickly the sessions arrive.
Every session is taught live by a practising engineer — never a pre-recorded video. Batches run to Indian Standard Time, and the timing is adjusted to suit your time zone wherever you are.
You write code during the session, ask questions as they come up, and have that code reviewed.
Weekday and weekend slots in IST. If none of them suit where you live, we schedule a batch that does.
International debit and credit cards, PayPal and direct bank transfer are all accepted.
A metric argument only lands once a useless model has scored ninety-nine per cent. This Classification Course therefore runs on a 40:60 Lecture-Practical Ratio, so every concept is immediately tested against data where the default choices fail.
Across the Classification Training you resample, tune, calibrate and diagnose yourself. Nothing in the course is left as something you only watched someone else type.
Understand what each metric rewards and what it hides.
Apply every concept to imbalanced data in the same session.
Learn what recall costs in precision before you are asked to choose.
Each concept is applied to imbalanced data in class.
A ninety-nine per cent useless model is met in the lab, not in a review.
Build the habit of inspecting the confusion matrix, not the headline score.
Leave able to deliver and defend a classifier end to end.
Every classification concept is followed by a threshold you have to defend.Wisen IT Solutions, Chennai runs Machine Learning Training in Chennai and online on the belief that classification is learned by arguing about cost, not by maximising accuracy.
This Classification Course assumes working Python, comfort with pandas, and the modelling fundamentals — pipelines, cross-validation and leakage awareness. If you have those, this course goes straight to the problems that make classification genuinely difficult.
The Classification Training is delivered live online, so the Machine Learning Training in Chennai batch and the online batch start from exactly the same imbalanced dataset.
Data Analysts & Scientists
Python Developers
Risk & Fraud Analysts
Anyone who has finished our Machine Learning Fundamentals course
If pipelines and cross-validation are unfamiliar, start with our Machine Learning Fundamentals Course first. We will tell you honestly which of the two suits you rather than enrolling you in the harder one — this course moves quickly through the basics because it assumes you already have them.
All you need is working Python, the modelling fundamentals and a problem where one class is rare.
We’ll take care of the rest!This Classification Course works through the model families and the evaluation machinery in depth: the classifiers, the resamplers, the scorers, the calibration wrappers and the inspection tools most tutorials never open.
The Classification Training works on data where the default settings actively mislead, which is what makes it an advanced course. Training in Chennai and the online batches run identical labs.
LogisticRegression
L1 & L2 Regularisation
LDA & QDA
Naive Bayes
SGDClassifier
DecisionTreeClassifier
RandomForest & ExtraTrees
HistGradientBoosting
XGBoost & LightGBM
CatBoost
Voting & Stacking
class_weight
SMOTE & Resamplers
Threshold Moving
CalibratedClassifierCV
Calibration Curves
Confusion Matrix
classification_report
ROC & PR Curves
Permutation Importance
SHAP & PDP
Search with Custom Scorers
By the end of this Classification Course you can build a classifier for a one-per-cent positive class and defend every choice behind it — the difference an advanced classification course is meant to make.
Handle Imbalance
Choose the Metric
Set the Threshold
Calibrate Probabilities
Every metric the course asks you to justify — precision, recall, ROC AUC, average precision — is defined in the library’s scoring reference, which is linked here.
The reference for estimators, pipelines, model selection and the evaluation metrics.
The definitions behind every score the course asks you to justify.
You can learn classification by first securing the modelling fundamentals — pipelines, cross-validation and leakage awareness — and then working almost entirely on imbalanced data, because that is where classification is actually difficult and where the default settings fail.
For most learners, a structured Classification Training course provides a clearer path than assembling knowledge from scattered tutorials, because the metric and threshold arguments have to be taught against real data.
At Wisen, the Machine Learning Fundamentals Course is the recommended prerequisite for Classification Training.
A practical learning progression can include:
Because accuracy treats every mistake as equal, and in the problems worth solving they never are. Missing a fraudulent transaction and flagging a legitimate one have completely different costs.
It also collapses under imbalance. If one per cent of transactions are fraud, a model that predicts "not fraud" every single time is ninety-nine per cent accurate and entirely worthless. Precision, recall and average precision expose that immediately; accuracy hides it.
The course teaches you to choose the metric from the cost structure before training, so the number you report is the number that matters.
Sometimes, and less often than its popularity suggests. Class weights are simpler, cheaper and frequently work as well, and threshold moving addresses the actual problem more directly than generating synthetic minority examples.
What matters far more than the choice is the discipline: any resampling must happen inside the training fold only. Resampling before splitting leaks synthetic rows into validation and produces excellent scores that mean nothing. That mistake is extremely common, and the course drills it until you cannot make it.
A calibrated model is one whose predicted probabilities mean what they say — of the cases it scores at 0.7, roughly seventy per cent really are positive. Many strong classifiers, boosted trees especially, rank cases well while producing probabilities that are badly wrong.
You need calibration when the probability itself is used — to price risk, to rank a review queue by expected value, or to feed a downstream decision rule. If you only need the ordering, you can skip it. The course covers how to tell which situation you are in.
Yes. A course completion certificate is issued once you finish the assessments and the classification project.
It records that you completed the course and the project. It is not a vendor or university certification, and we would not suggest otherwise. What carries weight in an interview is the project and your ability to explain why you chose the threshold you did.
Product companies, global capability centres and services firms in Chennai all run teams on this stack, which keeps steady demand behind the Machine Learning Classification Course. Because the scikit-learn Classification Course is used both for new builds and for long-lived systems already in production, that demand spans startups and established engineering organisations.
Yes. Freshers and graduates from non-IT streams join regularly, and Machine Learning Classification Online Training starts from the fundamentals before moving into advanced work. If your programming basics are thin we will say so before you enrol, and suggest the order in which to take Classification Training and its prerequisites.
We run a one-week evaluation instead of a one-hour demo class. A ₹2,000 registration fee reserves your seat in the Machine Learning Classification Course; attend the first week of live sessions and, if the training is not the right fit, that registration fee is refunded in full. If you register but cannot attend, it converts to Training Credit valid for 12 months. Once the course fee is paid, Machine Learning Classification Online Training fees are transferable once, within a month, if you leave before two weeks, and are neither refundable nor transferable after two weeks of attendance.
We do not promise a job, and you should be wary of anyone who does. What Classification Training includes is preparation: we help you prepare your biodata, build a portfolio from work you have actually done, and identify the knowledge gaps that would cost you an interview. That review covers the the Machine Learning Classification Course skills interviewers ask about today and the ones worth learning next, so you can keep preparing after the course ends.
Corporate Training Clients
The software industry is evolving with AI—not disappearing. Wisen's AI-Enabled Learning helps you master modern technologies, build strong engineering fundamentals, and collaborate effectively with AI tools like ChatGPT and Claude. Develop the practical skills, critical thinking, and real-world experience needed to build software with confidence and remain valuable throughout your career.
Talk to our AI Learning Advisor
+91 900 31 31 555