Traditional Machine Learning

Machine Learning

Supervised learning, classical models, evaluation, and when simple methods win.

Beginner 18 hours · Self-paced 99.0 USD · 100 lessons · ~681 min read

20 topics 100 lessons Start anywhere

What you’ll get out of this course

Build practical skill in “Machine Learning” with text-first lessons and clear checkpoints.
Level: Beginner—follow the syllabus in order or jump to the modules you need.
Reinforce ideas with end-of-topic checks and (where available) hands-on coding tasks.

Trust & quality

Content is designed and maintained by the Deep AI Minds team—structured for working adults, with frequent updates as tooling and best practices evolve.

Content currency: ~100% of lessons on the current curriculum revision

Instructor & outcomes

Deep AI Minds

Curriculum & instruction

Structured, industry-relevant paths with clear checkpoints and refresh cadence.

Satisfaction & billing

30-day satisfaction: if the syllabus or access is not as described, contact support and we will help (refunds for eligible purchases, case by case for integrations).

Common questions

You keep access for the lifetime of the catalog item you purchased, subject to fair use and our terms.

Yes—add your company name at checkout (where available) or contact us for team licensing and PO-based billing.

Review the full syllabus before buying. If something is wrong on our side, reach out and we will make it right.

Syllabus

We structured this course to build your skills step by step

Scroll through each module below—open lessons in place or jump into a topic. Everything runs in order, but you’re free to explore.

Topic 1

Learning module

ML mindset

How supervised learning maps inputs to outputs — and where it breaks.

What is supervised learning? 8 min

Bias and variance (intuition) 7 min

The ML pipeline at a glance 7 min

When NOT to use ML 7 min

The evaluation mindset 7 min

Topic 2

Learning module

Supervised learning deep dive

Loss functions, optimization loop, hyperparameters, gradient descent for ML.

Regression vs classification 6 min

Loss functions 8 min

The optimization loop 7 min

Gradient descent for ML 8 min

Hyperparameters and how to set them 7 min

Topic 3

Learning module

Classical models

Linear models, trees, baselines, and when simple beats deep.

Linear models 9 min

Trees and ensembles 9 min

Naive baselines 6 min

Model comparison done right 7 min

Simplicity as a virtue 6 min

Topic 4

Learning module

Linear regression deep dive

Assumptions, normal equations, polynomial features, regularization, when linear fails.

The four OLS assumptions 7 min

Normal equations and why we don't use them 7 min

Polynomial features 7 min

Regularization effects on linear models 7 min

When linear models fail 7 min

Topic 5

Learning module

Logistic regression and classification

Sigmoid, log-likelihood, softmax, decision boundaries, calibration.

Sigmoid and logits 7 min

Cross-entropy and log-likelihood 7 min

Softmax for multiclass classification 7 min

Decision boundaries 6 min

Calibration 7 min

Topic 6

Learning module

Decision trees deep dive

Entropy/Gini, splitting, pruning, surrogate splits, tree vs rule systems.

Entropy vs Gini impurity 7 min

Splitting strategies 7 min

Pruning 6 min

Missing values and surrogate splits 6 min

Trees vs rule systems 6 min

Topic 7

Learning module

Ensembles and boosting

Bagging, random forest, gradient boosting, XGBoost, ensemble tuning.

Bagging 7 min

Random forests 7 min

Gradient boosting 8 min

XGBoost / LightGBM overview 7 min

Ensemble tuning 7 min

Topic 8

Learning module

SVM and kernel methods

Max-margin, soft-margin, kernel trick, choosing kernels, SVM in practice.

Maximum margin classifier 7 min

Soft-margin SVMs 7 min

The kernel trick 8 min

Choosing kernels 6 min

SVM in practice 7 min

Topic 9

Learning module

Naive Bayes and k-NN

Naive Bayes deep, k-NN mechanics, distance metrics, trade-offs.

Naive Bayes deep dive 7 min

k-NN mechanics 7 min

Distance metrics 6 min

k-NN trade-offs 6 min

When to reach for Naive Bayes vs k-NN 6 min

Topic 10

Learning module

Unsupervised learning

Density estimation, anomaly detection, embeddings, evaluating without labels.

Unsupervised learning overview 7 min

Density estimation 7 min

Anomaly detection 7 min

Embeddings overview 7 min

Evaluating unsupervised models 6 min

Topic 11

Learning module

Clustering methods

K-means, hierarchical, DBSCAN, Gaussian mixtures, choosing k.

k-means 7 min

Hierarchical clustering 7 min

DBSCAN 7 min

Gaussian mixtures 7 min

Choosing the number of clusters 6 min

Topic 12

Learning module

Dimensionality reduction

PCA, t-SNE, UMAP, autoencoders, when to reduce dimensions.

PCA fundamentals 7 min

t-SNE 7 min

UMAP 7 min

Autoencoders as dimensionality reduction 7 min

When to reduce dimensions 6 min

Topic 13

Learning module

Feature engineering

Encoding categoricals, scaling, interactions, time and text features.

Encoding categorical features 7 min

Scaling and binning 7 min

Interaction features 7 min

Time-based features 7 min

Text features 7 min

Topic 14

Learning module

Model evaluation metrics

Confusion matrix, precision/recall, ROC/AUC, regression metrics, choosing right.

Confusion matrix 6 min

Precision, recall, F-scores 7 min

ROC curves and AUC 7 min

Regression metrics 6 min

Choosing the right metric 6 min

Topic 15

Learning module

Cross-validation and hyperparameter tuning

CV strategies, grid/random/Bayesian search, nested CV, early stopping.

Cross-validation strategies 7 min

Hyperparameter search 7 min

Nested cross-validation 7 min

Bayesian optimization 7 min

Early stopping 6 min

Topic 16

Learning module

Regularization and overfitting

L1/L2/elastic net, dropout, data augmentation, label smoothing, learning curves.

L1, L2, and elastic net 7 min

Dropout as regularization 7 min

Data augmentation 7 min

Label smoothing 6 min

Learning curves 6 min

Topic 17

Learning module

Handling imbalanced data

Class weights, resampling, SMOTE, threshold tuning, cost-sensitive learning.

Class weights 6 min

Resampling techniques 6 min

SMOTE and synthetic oversampling 7 min

Threshold tuning 6 min

Cost-sensitive learning 6 min

Topic 18

Learning module

Interpretability and explainability

Feature importance, SHAP, LIME, partial dependence, model cards.

Feature importance 7 min

SHAP values 7 min

LIME overview 6 min

Partial dependence plots 6 min

Model cards and documentation 6 min

Topic 19

Learning module

ML pipelines and workflow

Leakage prevention, train-eval-deploy loop, experiment tracking, reproducibility.

Preventing data leakage 7 min

Train-eval-deploy loop 7 min

Experiment tracking 7 min

Model versioning 6 min

Reproducibility 6 min

Topic 20

Learning module

ML in production

Serving, monitoring, drift, retraining, A/B testing, system design.

Serving models 7 min

Monitoring and drift 7 min

Retraining strategies 7 min

A/B testing models 7 min

ML system design 8 min

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