Coming Soon Machine Learning Fundamentals cover
Home โ€ข Catalog โ€ข Machine Learning Fundamentals
AI & Deep Learning

Machine Learning Fundamentals โ€” From Workflow to Trustworthy Models

A full practical ML track covering scikit-learn, baselines, evaluation, supervised and unsupervised models, pipelines, hyperparameter tuning, and interpretability.

๐Ÿ“š 13 units ๐Ÿชœ 25 steps / lessons โฑ๏ธ Self-paced
This course / diploma is coming soon
This course is coming soon โ€” register your details and we'll notify you when it launches.
Expected launch: Q3 2026
Pricing on launch

Available as part of AI diploma bundles

ุงู„ูƒูˆุฑุณ ุฏู‡ ู‚ูŠุฏ ุงู„ุชุญุถูŠุฑ โ€” ุณุฌู„ ุจูŠุงู†ุงุชูƒ ูˆู‡ู†ุจู„ุบูƒ ุฃูˆู„ ู…ุง ูŠู†ุฒู„.

๐Ÿš€ Enrollment opens soon ๐Ÿ”” Sign up to be notified
Outcomes3
Tools0
Projects0
CertificateVerified

What you will learn

  • โœ… Ship end-to-end classical ML projects
  • โœ… Choose and evaluate models correctly
  • โœ… Debug bias/variance, leakage, and interpretability issues

Curriculum & units

๐Ÿ“š 13 units ๐Ÿชœ 25 steps โฑ๏ธ Flexible
Introduction 1 topics ยท Flexible pace
  • Lesson 1: AI vs ML vs DL + ML Workflow โ€” Definitions, where classical ML fits, typical ML pipeline; baseline-first mindset; mini-workshop: map 5 problems to ML type + outline pipeline
Unit 1: Practical ML Setup 2 topics ยท Flexible pace
  • Lesson 1: scikit-learn + Problem Types โ€” Estimator API (fit/predict/score), datasets, regression vs classification vs clustering; labels/features/targets; hands-on: train first model end-to-end
  • Lesson 2: Baselines + Reproducibility โ€” Dummy baselines + heuristics; sanity checks; random_state; repeatable splits; comparing runs fairly; mini-lab: beat baseline and explain variance
Unit 2: Evaluation Fundamentals 5 topics ยท Flexible pace
  • Lesson 1: Splits + CV + Leakage โ€” Quick recap: train/val/test, stratification, time split concept; K-fold/Stratified K-fold; leakage patterns; mini-lab: choose split strategy + fix leakage
  • Lesson 2: Regression Metrics + Residuals โ€” RMSE/MAE/R2; compute on small example; outlier sensitivity; residual plots; business metric selection; mini-lab: evaluate 2 models + interpret residuals
  • Lesson 3: Classification Metrics + Thresholds
  • Lesson 4: ROC/PR + Calibration โ€” ROC-AUC vs PR-AUC (imbalanced focus); baseline PR; curve interpretation; calibration intuition; mini-lab: compare ROC/PR + calibrated vs uncalibrated outputs
  • Lesson 5: Learning Curves + Bias/Variance Plan
Unit 3: Regression Models 3 topics ยท Flexible pace
  • Lesson 1: Linear Regression Deep Dive โ€” Intuition + geometry; assumptions; coefficient interpretation; multicollinearity intuition; mini-lab: train + interpret coefficients responsibly
  • Lesson 2: Polynomial + Regularization โ€” Polynomial features; overfitting diagnosis; Ridge/Lasso/ElasticNet (shrinkage vs sparsity); scaling reminders; mini-lab: compare Ridge/Lasso/EN and justify choice
  • Lesson 3: Regression Debugging Workshop โ€” Outliers + leverage points; heteroscedasticity symptoms; residual patterns; quick mitigation strategies; mini-lab: diagnose broken regression and improve
Unit 4: Classification Models 2 topics ยท Flexible pace
  • Lesson 1: Logistic Regression + Multiclass โ€” Decision boundaries; probabilities; log loss intuition (no heavy math); One-vs-Rest vs Multinomial; mini-lab: multiclass evaluation + confusion analysis
  • Lesson 2: Classification Error Analysis โ€” Slice analysis; false positive/negative review; threshold recap; mini-lab: find weakest subgroup + propose fixes
Unit 5: Distance & Probabilistic ML 2 topics ยท Flexible pace
  • Lesson 1: kNN End-to-End โ€” Distance intuition; scaling requirement; choosing k; distance metrics; curse of dimensionality; failure modes; mini-lab: tune kNN + measure latency
  • Lesson 2: Naive Bayes (Variants + Smoothing)
Unit 6: Support Vector Machines 1 topics ยท Flexible pace
  • Lesson 1: SVM + Kernels + Tuning โ€” Margin/support vectors; RBF/poly kernels intuition; scaling + compute warnings; C/gamma tuning; mini-lab: visualize kernel boundary + tune SVM
Unit 7: Trees & Bagging 2 topics ยท Flexible pace
  • Lesson 1: Decision Trees (Core โ†’ Pitfalls) โ€” Splits/impurity; interpretability; pruning/overfitting; high-cardinality pitfalls; mini-lab: build tree, prune, and re-evaluate
  • Lesson 2: Bagging + Random Forests โ€” Bootstrap averaging; feature subsampling; tuning knobs; feature importance caveats; mini-lab: tune RF + analyze errors
Unit 8: Boosting & Ensembles 2 topics ยท Flexible pace
  • Lesson 1: Boosting + Gradient Boosting โ€” Boosting intuition; learning_rate/estimators/depth; early stopping concept; mini-lab: tune GBDT and justify choices
  • Lesson 2: Voting + Stacking + Model Choice โ€” Voting vs stacking; leakage risks; safe stacking pattern; model selection cheat sheet; workshop: pick models for 5 tabular scenarios
Unit 9: Pipelines & Tuning 2 topics ยท Flexible pace
  • Lesson 1: Pipelines + Safe Feature Selection
  • Lesson 2: Hyperparameter Search โ€” Grid vs random search; budget thinking; GridSearchCV scoring/refit/cv; nested CV concept; mini-lab: correct vs incorrect tuning comparison
Unit 10: Unsupervised ML 2 topics ยท Flexible pace
  • Lesson 1: Clustering (Overview + k-Means) โ€” When clustering is meaningful; similarity choice; k-means init; choosing k; inertia vs silhouette; mini-lab: elbow vs silhouette comparison
  • Lesson 2: Hierarchical + DBSCAN + Evaluation
Unit 11: Dimensionality Reduction 2 topics ยท Flexible pace
  • Lesson 1: PCA + TruncatedSVD โ€” Variance directions; scaling reminder; PCA for visualization; TruncatedSVD for sparse data; mini-lab: explained variance + component interpretation
  • Lesson 2: t-SNE/UMAP + Pitfalls โ€” Visualization purpose; parameter sensitivity; why not for modeling claims; pitfalls (leakage/over-interpretation); mini-lab: run and compare settings
Unit 12: Practical ML Problems 1 topics ยท Flexible pace
  • Lesson 1: Imbalanced + Missing + Outliers
Unit 13: Interpretability & Trust 2 topics ยท Flexible pace
  • Lesson 1: Feature Importance + SHAP idea โ€” Permutation vs built-in importance; stability; SHAP intuition; communicating responsibly; mini-lab: compare importance methods + explain 3 cases
  • Lesson 2: Bias & Fairness Basics โ€” Bias sources; subgroup evaluation; reporting responsibly; mini-lab: evaluate subgroup slice and discuss tradeoffs
Project 1 topics ยท Flexible pace
  • End-to-End ML Project โ€” Framing โ†’ EDA (Course 2) โ†’ baseline โ†’ compare models โ†’ tuning โ†’ interpretation โ†’ final report + error analysis

Projects you will build

Tools & platforms

Target audience

  • Aspiring ML engineers
  • Data analysts moving deeper into modeling
  • Engineers building their first serious ML portfolio

Career paths

What you receive after finishing

Verification-ready certificates and HR-friendly training letters.

๐Ÿ†

Verified Certificate

Official Learn in Depth completion certificate with QR verification.

Verifiable on the public verification page.

๐Ÿ‡ฌ๐Ÿ‡ง

English Training Letter

For international companies and overseas employment.

On official Learn in Depth letterhead, signed by the instructor.

๐Ÿ‡ช๐Ÿ‡ฌ

Arabic Training Letter

For local employers in MENA and university coordination.

Bilingual stamped letter ready for HR submission.

๐Ÿข

Company-Stamped Certificate

Company-stamped, for academic credit. Request it by contacting +20 155 876 5064 via WhatsApp or phone.

Issued upon request after successful completion.

Course FAQ

The current target is Q3 2026. Register via the form and we will send launch and pricing updates first.

ุงู„ู…ุณุชู‡ุฏู ุงู„ุญุงู„ูŠ ู‡ูˆ Q3 2026. ุณุฌู‘ู„ ุจูŠุงู†ุงุชูƒ ููŠ ุงู„ููˆุฑู… ูˆุณู†ุฑุณู„ ู„ูƒ ุฃูˆู„ ุชุญุฏูŠุซุงุช ุงู„ุฅุทู„ุงู‚ ูˆุงู„ุฃุณุนุงุฑ.

These tracks are currently marked Coming Soon. You can browse the curriculum and leave your details to be notified when registration opens.

ุญุงู„ูŠู‹ุง ุงู„ู…ุณุงุฑุงุช ููŠ ุญุงู„ุฉ Coming Soon. ุชู‚ุฏุฑ ุชุชุตูุญ ุงู„ู…ุญุชูˆู‰ ูˆุชุณุฌู„ ุงู‡ุชู…ุงู…ูƒ ู„ู†ุจู„ุบูƒ ุนู†ุฏ ูุชุญ ุงู„ุชุณุฌูŠู„.

Yes โ€” every track is designed around a verified certificate and hands-on project review.

ุฃูŠูˆุฉ โ€” ูƒู„ ู…ุณุงุฑ ู…ุตู…ู… ุจุดู‡ุงุฏุฉ ู…ูˆุซู‚ุฉ ูˆู…ุฑุงุฌุนุฉ ุนู…ู„ูŠุฉ ู„ู„ู…ุดุงุฑูŠุน ุงู„ุฑุฆูŠุณูŠุฉ.

Yes. The positioning, support, and project design target engineers in the Egyptian and Gulf markets with a strong practical hiring focus.

ุฃูŠูˆุฉุŒ ุงู„ุชุณูˆูŠู‚ ูˆุงู„ุฏุนู… ูˆุจู†ุงุก ุงู„ู…ุดุงุฑูŠุน ู…ุนู…ูˆู„ูŠู† ุฎุตูŠุตู‹ุง ู„ู…ู‡ู†ุฏุณูŠู† ุงู„ุณูˆู‚ ุงู„ู…ุตุฑูŠ ูˆุงู„ุฎู„ูŠุฌูŠ ู…ุน ุชุฑูƒูŠุฒ ุนู„ู‰ ุงู„ุชูˆุธูŠู ุงู„ุนู…ู„ูŠ.

Create your account, add the course to cart, and follow the payment steps.

ุณุฌู„ ุญุณุงุจูƒ ูˆุฃุถู ุงู„ูƒูˆุฑุณ ู„ู„ุณู„ุฉ ูˆุงุชุจุน ุฎุทูˆุงุช ุงู„ุฏูุน.

Yes โ€” students get an automatic discount shown at checkout.

ุฃูŠูˆู‡ โ€” ุงู„ุทู„ุจุฉ ู„ูŠู‡ู… ุฎุตู… ุฎุงุต ุจูŠุธู‡ุฑ ุฃูˆุชูˆู…ุงุชูŠูƒ.

All courses are recorded so you can learn at your own pace.

ูƒู„ ุงู„ูƒูˆุฑุณุงุช ู…ุณุฌู„ุฉ ุนุดุงู† ุชุชุนู„ู… ููŠ ุฃูŠ ูˆู‚ุช ูŠู†ุงุณุจูƒ.

Yes โ€” all courses are free for people from Palestine.

ุฃูŠูˆู‡ โ€” ูƒู„ ุงู„ูƒูˆุฑุณุงุช ู…ุฌุงู†ูŠุฉ ู„ุฃู‡ู„ ูู„ุณุทูŠู†.

Bank transfer, Vodafone Cash, InstaPay.

ุชุญูˆูŠู„ ุจู†ูƒูŠุŒ ููˆุฏุงููˆู† ูƒุงุดุŒ ุฅู†ุณุชุงุจุงูŠ.

Related courses