Professional skills

Machine Learning A-Z

Name: Machine Learning A-Z
Price: 2800.00 USD
Availability: InStock

Six-day (44-hour) course where you build and evaluate machine learning models in Python and R, practise labs daily, and prepare for certification.

44 hours Intermediate Data Science & AI

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THE BIG PICTURE

About this course

This course prepares you for a machine learning professional certification that recognises the ability to frame ML problems, build reliable models, and operate them responsibly. Such credentials are awarded by industry bodies and cloud providers; you should select the provider and credential that matches your role and platform. Across six days, you will work through supervised and unsupervised learning, model evaluation, feature engineering, tuning, and deployment. You will implement algorithms in Python (with optional R equivalents), complete hands-on labs, and apply techniques to realistic datasets, with daily exam-style practice. Exam format, duration, and policies vary by awarding body. The course includes a timed mock exam and guidance on mapping objectives to your chosen certification and booking with the provider’s portal. The awarding body owns the certification scheme. MindClick provides training and exam preparation and is not an accredited or authorised partner.

What you’ll learn

Frame machine learning problems, define success metrics, and assess feasibility
Prepare, cleanse, and engineer features from structured and unstructured data
Build and evaluate supervised models for regression and classification
Apply unsupervised methods for clustering and dimensionality reduction
Tune models with cross-validation, manage bias–variance trade-offs, and interpret results
Package, deploy, and monitor models with basic MLOps practices and governance controls
Address responsible AI concerns, including fairness, bias, and explainability
Practise exam-style questions, sit a mock exam, and create a targeted revision plan

A CLEAR PATH FORWARD

6-day Machine Learning A-Z agenda

6 days · 44 contact hours · 09:00–16:30

6 instructor-led days (44 contact hours) mapped to the exam domains. Each day combines short concept sessions, hands-on practice on a running case study and exam-style questions.

Learning route: 6 recommended days
Practical work: 6 guided activities
Finish with: Mock exam + certification exam

Take a look at each day. Open a section to see the topics and practical work.

Day 1Problem framing, data understanding, and preparationProblem framing; data collection and preparation; feature engineering

What we’ll cover

09:00–09:30 · Course induction, objectives, and success criteria
09:30–10:30 · Problem framing, target definition, and leakage avoidance
10:45–12:30 · Data sources, sampling, quality checks, and exploratory analysis
13:15–14:30 · Data wrangling in Python (pandas); R tidyverse parallels
14:30–15:15 · Feature definition and handling missing, categorical, and skewed data
15:15–16:15 · Exam-style practice questions: problem framing and data prep
16:15–16:30 · Day wrap-up and take-home reading

Put it into practice

Profile a real-world tabular dataset, articulate the business question, define evaluation metrics, and produce a data quality report. Implement a reproducible data cleaning pipeline in Python (with optional R script).

You’ll take away
Problem statement and data audit with a reproducible cleaning notebook

Day 2Supervised learning basics and evaluationSupervised learning; metrics; validation

What we’ll cover

09:00–09:30 · Recap and Q&A
09:30–10:30 · Linear and logistic regression: assumptions and baselines
10:45–12:30 · Train/validation/test splits, cross-validation, and metrics (RMSE, AUC, F1)
13:15–14:00 · Regularisation (Ridge/Lasso) and feature scaling
14:00–15:00 · Naïve Bayes, k-NN, and support vector machines overview
15:00–16:00 · Error analysis and bias–variance trade-offs with learning curves
16:00–16:30 · Exam-style practice questions: metrics and model selection

Put it into practice

Build baseline regression and classification models with proper validation, comparing metrics and learning curves. Document model assumptions and next steps based on error analysis.

You’ll take away
Baseline model notebooks with evaluation reports and learning curves

Day 3Trees, ensembles, and feature engineeringSupervised learning; feature engineering; tuning and validation

What we’ll cover

09:00–09:30 · Recap and Q&A
09:30–10:30 · Decision trees and impurity measures
10:45–12:30 · Ensembles: random forests and gradient boosting (XGBoost/LightGBM concepts)
13:15–14:00 · Feature engineering for tabular data and pipelines
14:00–15:00 · Hyperparameter tuning with grid/random search and early stopping
15:00–16:00 · Model interpretability: permutation importance and partial dependence
16:00–16:30 · Exam-style practice questions: ensembles and tuning

Put it into practice

Engineer features, train tree-based and boosting models, and run a structured tuning experiment. Compare performance and interpretability to select a recommended model.

You’ll take away
Feature-engineered dataset and tuned ensemble model with interpretation

Day 4Unsupervised learning, dimensionality reduction, and anomaly detectionUnsupervised learning; dimensionality reduction; evaluation

What we’ll cover

09:00–09:30 · Recap and Q&A
09:30–10:30 · Clustering methods: k-means and hierarchical clustering
10:45–12:30 · Dimensionality reduction: PCA and t-SNE/UMAP concepts
13:15–14:00 · Similarity measures and feature scaling for unsupervised tasks
14:00–15:00 · Anomaly detection approaches and practical thresholds
15:00–16:00 · Evaluating unsupervised results and visualising clusters
16:00–16:30 · Exam-style practice questions: clustering and reduction

Put it into practice

Apply clustering and PCA to a multi-feature dataset to discover segments, then validate findings with internal metrics and visualisations. Propose actions for two segments based on insights.

You’ll take away
Cluster analysis report and dimensionality reduction notebook

Day 5Deployment, MLOps, monitoring, and responsible AIDeployment and MLOps; monitoring; governance and responsible AI

What we’ll cover

09:00–09:30 · Recap and Q&A
09:30–10:30 · Packaging models with pipelines and reproducible environments
10:45–12:30 · Serving basics: REST endpoints, batch vs real-time scoring
13:15–14:00 · Monitoring performance, drift detection, and retraining triggers
14:00–15:00 · Data and model governance: versioning, lineage, and documentation
15:00–16:00 · Responsible AI: fairness checks, bias mitigation, and explainability (SHAP/LIME)
16:00–16:30 · Exam-style practice questions: deployment, monitoring, and ethics

Put it into practice

Containerise a trained model and expose a local REST endpoint for scoring with logging. Implement a simple drift check and document governance controls and risk mitigations.

You’ll take away
Packaged model service, monitoring checklist, and governance notes

Day 6Neural networks, revision, mock exam, and next stepsSupervised learning; evaluation; exam preparation and booking

What we’ll cover

09:00–09:30 · Recap and Q&A
09:30–10:30 · Neural network basics: architectures, activation, and optimisation
10:45–12:30 · Build and evaluate a simple deep learning model for classification
13:15–14:00 · Consolidated review: objectives, pitfalls, and strategy
14:00–14:30 · Exam techniques: timing, elimination, and question patterns
14:30–16:00 · Timed mock exam and group debrief
16:00–16:30 · Exam booking guidance and personal revision plan

Put it into practice

Implement a small feed-forward network on a tabular or image dataset, comparing its performance with classical models. Sit a timed mock exam and refine your personal study plan using the debrief.

You’ll take away
Neural network lab, mock exam score report, and exam booking checklist

Bring it all together

Certification exam: Machine Learning A-Z. Confirm the current exam format with the awarding body when you book. Every attendee also receives a MindClick certificate of completion for 44 contact hours.

Sample MindClick certificate of completion for Machine Learning A-Z, marked SAMPLE — What you receive
You earn two separate credentials. The sample shows the MindClick training certificate.
MindClick certificate of completion: 6 days, 44 contact hours, issued to every attendee who completes the course.
Machine Learning A-Z: issued by the awarding body when you meet its requirements and pass the exam.

Before you choose your learning route

Session timings are indicative; the trainer may adjust them for the group. Exam details reflect the awarding body’s published information at the time of writing; confirm the current version when you book.

The awarding body owns this certification scheme. MindClick provides training and exam preparation and is not an accredited or authorised partner of the awarding body.

Fees

Fee per learner outside India. Exam fee included.
Delivery format	Fee per learner	Exam fee
Live online	$2,800 USD	Included
Classroom	$4,500 USD	Included

One awarding-body certification exam attempt is included in the training fee. Confirm the current fee, any applicable tax and what the fee covers in writing before you pay.

Prerequisites

Recommended: basic Python programming, core statistics (distributions, hypothesis tests), linear algebra concepts, and familiarity with Jupyter or RStudio. Bring a laptop with admin rights, Python 3.11 (or Anaconda), a modern browser, Git installed, and optionally R (≥4.3) with RStudio.

FIND YOUR START

Confirmed batch starts

Choose a published start, then review your offer in the same enrollment flow. A start date is not the full timetable or a seat reservation.

Choose your country & month

Oct252026

Confirmed by MindClickLive onlineWeekday start