• Format — 1,450+ MCQs with instant explanations after every wrong answer
• Duration — about 16 hours of focused practice; 3–6 weeks depending on daily pace
• Level — beginner to intermediate; no prior ML knowledge required
• Cost — free with a completion certificate
• Audience — CS/engineering students, placement prep candidates, developers switching into ML or data science roles
• Series — Module 1 of 4 in the Machine Learning series; next: Supervised Learning Mastery

This course is designed for three audiences. First, CS and engineering students preparing for campus placements — ML questions are a staple of written tests and technical interviews at product companies, and this course covers the concepts examiners test most: algorithm selection criteria, metric trade-offs under class imbalance, bias-variance diagnosis, and the mechanics of core supervised and unsupervised methods. Second, working developers transitioning into data science or machine learning engineering — if you understand how to write code but have never formally studied ML, this course fills the conceptual gaps in roughly four to six weeks of daily practice. Third, self-taught learners who have picked up ML from blog posts and YouTube but have never tested their recall systematically — the MCQ format reveals exactly which concepts feel familiar but are not recalled accurately under pressure.

Foundations

• Foundations and Workflow — what machine learning is, how it differs from traditional rule-based programming, the six learning paradigms (supervised, unsupervised, semi-supervised, self-supervised, online, and reinforcement learning), inductive bias, ML applicability criteria, the ML project lifecycle, and the iteration cycle from problem framing to deployment
• Data Splits and Leakage Prevention — how to partition data correctly using different split strategies, what causes data leakage and how to prevent it, and why the validation set exists separately from the test set

Core Algorithms

• Supervised Learning — simple and multiple linear regression, polynomial regression, the closed-form normal equation, residual analysis, Ridge regression (L2), Lasso regression (L1), Elastic Net, regularization strength selection, logistic regression (sigmoid function, decision boundary, log-odds, MLE, multinomial), k-Nearest Neighbors (Euclidean and cosine distance, k selection, curse of dimensionality), decision trees (Gini impurity, entropy and information gain, pruning), random forests, introductory gradient boosting, and support vector machines (maximum margin classifier, soft margin, kernel trick, RBF kernel, polynomial kernel)
• Unsupervised Learning — k-Means clustering (elbow method, silhouette score), hierarchical clustering, DBSCAN, Gaussian mixture models, Principal Component Analysis (eigenvalues and eigenvectors, explained variance ratio), Singular Value Decomposition, t-SNE, UMAP, anomaly detection (Isolation Forest, Local Outlier Factor, one-class SVM, statistical thresholds), and association rule learning (Apriori algorithm, FP-Growth, support, confidence, and lift)

Evaluation, Engineering, and Optimization

• Model Evaluation — classification metrics (accuracy, precision, recall, F1 score, confusion matrix, precision-recall curve, ROC curve and AUC), regression metrics (MAE, MSE, RMSE, R², adjusted R², MAPE), cross-validation variants, k-fold cross-validation, model calibration techniques, and class imbalance strategies (class weights, resampling over/under, SMOTE, threshold tuning)
• Bias-Variance and Regularization — the bias-variance tradeoff, overfitting and underfitting, bias-variance decomposition, learning curves, validation curves, train-test gap, L1 regularization, L2 regularization, early stopping, and weight decay
• Feature Engineering — missing value strategies, outlier detection (IQR and z-score), duplicate handling, data type cleanup, one-hot encoding, label encoding, ordinal encoding, target encoding, high-cardinality feature strategies, min-max scaling, standardization, robust scaling, scaling sensitivity by algorithm, filter methods (correlation, chi-square), wrapper methods (RFE), embedded methods (Lasso), mutual information, and derived features (interaction features, polynomial features, binning, datetime decomposition, domain-specific aggregates)
• Optimization — loss functions for different task types (MSE, MAE, Huber loss, hinge loss, cross-entropy loss, custom loss functions), vanilla gradient descent, stochastic gradient descent, mini-batch gradient descent, learning rate selection, momentum, and convexity (local vs global minima, convex vs non-convex problems, optimization tractability)

Deep learning is a specific subset of machine learning that uses multi-layer neural networks trained by gradient descent. Before deep learning makes sense, you need to understand the foundational vocabulary this course builds: what a loss function is and why it matters, what overfitting means and how regularization fixes it, how cross-validation prevents tuning on the test set, why feature engineering changes model behaviour, and how gradient descent finds model parameters. Deep learning does not replace any of this — it operates within the same evaluation and optimization framework. Starting with neural networks before understanding bias-variance, regularization, and model evaluation is like learning calculus before understanding functions. Completing this course first makes every deep learning concept easier to learn and harder to confuse.

The typical advice is to work through a textbook or watch a lecture series. The problem is that passive consumption creates an illusion of understanding — the material feels familiar while you're reading it but doesn't transfer to recall under pressure. Research on retrieval practice consistently shows that testing yourself shortly after learning something produces roughly double the long-term retention compared to re-reading the same material. The MCQ format on Abekus enforces this automatically: each question is a forced recall event where you must produce the answer, not just recognise it. The explanation on a wrong answer corrects your mental model at exactly the moment you're most receptive. A practical schedule: 40–60 questions per day, with the AI guide's weak-subtopic flags driving your review sessions twice a week.

Video courses on platforms like Udemy and Coursera are structured around lectures — you watch, take notes, and revisit slides when you forget something. That format works well for initial exposure to a topic. Where it falls short is retention: passive watching produces recognition, not recall. Most learners who finish a 20-hour ML video course still blank on "what is the difference between L1 and L2 regularization?" when asked cold a week later, because they never retrieved the answer under pressure. MCQ practice flips this: every question is a retrieval event, and the explanation on a wrong answer corrects your mental model at exactly the moment you are most receptive. If you have already watched a lecture series and want to convert that knowledge into recalled, interview-ready facts, this course is the right next step.

Each question covers one label from the curriculum — a specific concept like "decision boundary in logistic regression", "silhouette score interpretation", or "SMOTE for class imbalance" — so there is no ambiguity about what is being tested. You answer, and if you are wrong, you see an explanation immediately. The AI guide tracks your accuracy across all 145 labels and flags the subtopics where you are making the most mistakes, so each practice session becomes more focused over time rather than grinding through topics you have already mastered. Questions span four difficulty levels, from definitional recall to tricky edge cases that trip up learners who have only read descriptions.

The course has 1,450 questions. At a realistic pace of 40 seconds per question — reading the stem, answering, skimming the explanation — that is about 16 hours of focused practice. Spread over a daily habit:

• 40 questions/day → 5–6 weeks
• 60 questions/day → 3–4 weeks
• 80 questions/day → 2–3 weeks

These are honest numbers computed from the actual question count, not rounded estimates. Most learners who complete the course do so in 4–5 weeks at a moderate daily pace.

This course gives you a broad foundation across the full ML landscape. The natural next steps are the deeper-dive modules in the Machine Learning series on Abekus:

• Machine Learning: Supervised Learning Mastery — a deep dive into supervised algorithms with significantly more labels per method, covering advanced regression and classification techniques, ensemble methods at depth, and hyperparameter tuning strategies
• Machine Learning: Unsupervised Learning and Clustering — focused practice on clustering algorithms, density estimation, dimensionality reduction techniques, and anomaly detection at depth
• Machine Learning: Ensembles and Boosting — dedicated coverage of Random Forests, AdaBoost, Gradient Boosted Trees, XGBoost, LightGBM, and CatBoost

If your goal is AI engineering and working with language models, GenAI Engineering: LLM Foundations is the companion track that builds on this ML base to cover transformers, large language models, and modern AI systems.