Machine learning- Full Course

Source: Dev.to

Machine Learning — Blog Series Contents

PART 0: Before ML (Mindset & Big Picture)

• What is Machine Learning? (Without buzzwords)
• ML vs AI vs DL vs Statistics
• Why ML models fail in the real world
• The ML lifecycle: Data → Model → Deployment → Decay
• When NOT to use Machine Learning

PART 1: Mathematical Foundations (Intuition First)

• (No heavy proofs initially — geometry + visuals)
• Linear Algebra for ML
  • Vectors as points, directions, and features
  • Dot product = similarity (why cosine works)
  • Matrix multiplication as a transformation
  • Eigenvectors as “stable directions”
  • Why high‑dimensional space is weird
• Probability & Statistics
  • Random variables as uncertainty containers
  • Expectation as long‑term behaviour
  • Variance, bias, and noise (real meaning)
  • Bayes theorem without formulas
  • Maximum Likelihood vs MAP
• Optimization Basics
  • Loss functions: measuring regret
  • Gradient descent geometrically
  • Local minima, saddle points, flat regions
  • Learning rate as step‑size in physics
  • Convex vs non‑convex problems

PART 2: Classical Machine Learning (Core)

• Supervised Learning
• Linear Regression from scratch
• Overfitting vs underfitting (bias–variance tradeoff)
• Regularization: L1, L2, Elastic Net
• Logistic Regression as a probabilistic model
• Decision Trees: splitting chaos into order
• Random Forests: wisdom of crowds
• Gradient Boosting intuitively
• XGBoost explained simply
• Model Evaluation
  • Train/Validation/Test split myths
  • Accuracy is a lie (precision, recall, F1)
  • ROC vs PR curves
  • Cross‑validation done right
  • Data leakage horror stories

PART 3: Unsupervised Learning

• Clustering as structure discovery
• K‑Means geometrical intuition
• Hierarchical clustering
• DBSCAN and density‑based thinking
• Dimensionality reduction vs feature selection
• PCA as variance maximisation
• When PCA amplifies bias (fairness angle)

PART 4: Feature Engineering (Underrated Superpower)

• Why features matter more than models
• Encoding categorical variables
• Scaling and normalisation myths
• Feature interactions
• Time‑based features
• Feature leakage patterns
• Domain‑driven feature design

PART 5: Neural Networks (Deep Learning)

Basics

• Perceptron: the neuron myth
• Why linear models fail
• Activation functions geometrically
• Backpropagation explained visually
• Vanishing & exploding gradients
• Architectures
  • Fully connected networks
  • CNNs: local connectivity intuition
  • Pooling: information compression
  • RNNs and sequence memory
  • LSTM & GRU demystified
  • Transformers at a high level

PART 6: Training Deep Models

• Initialization matters more than you think
• Batch vs mini‑batch vs stochastic GD
• Optimizers: SGD, Adam, RMSProp
• Regularization in deep learning
  • Dropout as ensemble trick
  • BatchNorm explained
• Early stopping intuition

PART 7: Model Interpretability & Fairness

• Black‑box vs glass‑box models
• Feature importance myths
• SHAP and LIME intuitively
• Fairness in ML: what does it mean?
• Bias in data vs bias in models
• Fair PCA and representation learning
• Trade‑offs: accuracy vs fairness

PART 8: ML Systems & Production

• Training vs inference pipelines
• Offline vs online learning
• Model versioning
• Data drift vs concept drift
• Monitoring ML in production
• Retraining strategies
• ML technical debt

PART 9: Applied Machine Learning

• ML for recommendation systems
• ML in search engines
• ML for fraud detection
• ML in healthcare (risks & ethics)
• ML in finance
• ML in sports analytics
• ML for NLP tasks
• ML for computer vision

PART 10: Research Thinking in ML

• How to read ML research papers
• Empirical vs theoretical papers
• Reproducibility crisis in ML
• Baselines nobody respects
• Ablation studies explained
• Writing a good ML paper
• Common research mistakes

PART 11: Advanced & Emerging Topics

• Self‑supervised learning
• Contrastive learning
• Representation learning
• Meta‑learning
• Online learning
• Causal ML
• Reinforcement Learning intuition
• LLMs and foundation models
• ML alignment & safety

PART 12: ML Career & Learning Path

• How to learn ML without drowning
• Math vs intuition — what to prioritise?
• ML interviews vs real ML
• Building impactful ML projects
• From engineer to ML researcher
• How to choose a research problem