🤖 Machine Learning Foundations

Master the fundamental concepts, algorithms, and techniques that power modern AI systems

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Machine Learning Foundations Curriculum

12
Core Units
~100
ML Concepts
20+
Algorithms
40+
Practical Examples
1

Introduction to Machine Learning

Understand what machine learning is, its types, and fundamental concepts.

  • What is machine learning
  • Types of machine learning
  • Supervised vs unsupervised learning
  • Key terminology and concepts
  • ML workflow and pipeline
  • Applications and use cases
  • History and evolution
  • Ethics in machine learning
2

Data and Feature Engineering

Learn how to prepare, clean, and transform data for machine learning models.

  • Data collection and sources
  • Data cleaning and preprocessing
  • Handling missing values
  • Feature engineering techniques
  • Feature selection methods
  • Data transformation and scaling
  • Encoding categorical variables
  • Data quality assessment
3

Linear Regression

Master the foundational algorithm for predicting continuous values.

  • Simple linear regression
  • Multiple linear regression
  • Least squares method
  • Gradient descent optimization
  • Cost functions and loss
  • Regularization techniques
  • Polynomial regression
  • Model interpretation
4

Logistic Regression

Learn classification using logistic regression and probability-based models.

  • Binary classification
  • Sigmoid function
  • Maximum likelihood estimation
  • Decision boundaries
  • Multiclass classification
  • Regularized logistic regression
  • Odds ratios and interpretation
  • Logistic regression assumptions
5

Decision Trees

Understand tree-based models for both classification and regression tasks.

  • Decision tree structure
  • Splitting criteria and metrics
  • Information gain and entropy
  • Gini impurity
  • Tree pruning techniques
  • Handling overfitting
  • Feature importance
  • Tree visualization
6

Ensemble Methods

Combine multiple models to create more powerful and robust predictions.

  • Ensemble learning principles
  • Bagging and bootstrap
  • Random Forest algorithm
  • Boosting techniques
  • AdaBoost algorithm
  • Gradient boosting
  • XGBoost and LightGBM
  • Voting classifiers
7

Support Vector Machines

Learn about SVMs for classification and regression with kernel methods.

  • SVM fundamentals
  • Maximum margin classifier
  • Support vectors
  • Kernel trick
  • Linear and non-linear kernels
  • Soft margin classification
  • SVM for regression
  • Parameter tuning
8

Clustering Algorithms

Discover patterns and group similar data points using unsupervised learning.

  • Unsupervised learning concepts
  • K-means clustering
  • Hierarchical clustering
  • DBSCAN algorithm
  • Gaussian mixture models
  • Cluster evaluation metrics
  • Choosing optimal clusters
  • Clustering applications
9

Dimensionality Reduction

Reduce feature dimensions while preserving important information.

  • Curse of dimensionality
  • Principal Component Analysis
  • Eigenvalues and eigenvectors
  • Variance explained
  • Linear Discriminant Analysis
  • t-SNE visualization
  • UMAP algorithm
  • Feature selection vs extraction
10

Model Evaluation

Assess model performance using various metrics and validation techniques.

  • Train-validation-test splits
  • Cross-validation techniques
  • Classification metrics
  • Regression metrics
  • Confusion matrices
  • ROC curves and AUC
  • Precision-recall curves
  • Statistical significance
11

Bias, Variance, and Overfitting

Understand the bias-variance tradeoff and techniques to prevent overfitting.

  • Bias-variance decomposition
  • Underfitting vs overfitting
  • Learning curves
  • Regularization techniques
  • Early stopping
  • Dropout methods
  • Model complexity
  • Generalization strategies
12

ML in Practice

Apply machine learning in real-world scenarios and production environments.

  • ML project lifecycle
  • Problem formulation
  • Data collection strategies
  • Model selection process
  • Hyperparameter tuning
  • Model deployment
  • Monitoring and maintenance
  • A/B testing for ML

Unit 1: Introduction to Machine Learning

Understand what machine learning is, its types, and fundamental concepts.

What is Machine Learning

Learn the fundamental definition and core concepts of machine learning as a subset of artificial intelligence.

AI Algorithms Data
Machine learning is the science of getting computers to learn and act like humans do, and improve their learning over time in an autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.

Types of Machine Learning

Explore the three main categories of machine learning approaches and their applications.

Supervised Learning: Learn from labeled examples
Unsupervised Learning: Find patterns in unlabeled data
Reinforcement Learning: Learn through interaction and rewards
# Types of ML problems
ml_types = {
  "Supervised": {
    "Classification": "Predict categories",
    "Regression": "Predict continuous values"
  },
  "Unsupervised": {
    "Clustering": "Group similar data",
    "Dimensionality Reduction": "Reduce features"
  },
  "Reinforcement": {
    "Agent-Environment": "Learn optimal actions"
  }
}

Supervised vs Unsupervised Learning

Understand the key differences between learning with and without labeled data.

Supervised: Input-output pairs (X, y) → Learn mapping function f(X) = y
Unsupervised: Only inputs (X) → Discover hidden patterns and structure
# Supervised learning example
from sklearn.linear_model import LinearRegression

# We have both X (features) and y (target)
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]

model = LinearRegression()
model.fit(X, y) # Learn from labeled data
prediction = model.predict([[5]]) # Predict new value

# Unsupervised learning example
from sklearn.cluster import KMeans

# We only have X (features), no labels
X = [[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]]

kmeans = KMeans(n_clusters=2)
kmeans.fit(X) # Find patterns without labels
clusters = kmeans.predict(X) # Assign cluster labels

Key Terminology and Concepts

Master the essential vocabulary and concepts used throughout machine learning.

Features Labels Training Testing
# Essential ML terminology
terminology = {
  "Dataset": "Collection of data points",
  "Features": "Input variables (X)",
  "Target/Labels": "Output variable (y)",
  "Training": "Learning from data",
  "Testing": "Evaluating performance",
  "Model": "Mathematical representation",
  "Algorithm": "Learning procedure",
  "Prediction": "Model output for new data",
  "Hyperparameters": "Algorithm settings",
  "Overfitting": "Too complex for data",
  "Underfitting": "Too simple for data"
}

ML Workflow and Pipeline

Understand the typical steps involved in a machine learning project.

Problem Definition → Data Collection → Data Preprocessing → Model Selection → Training → Evaluation → Deployment → Monitoring
# Complete ML workflow
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 1. Load and explore data
data = pd.read_csv('dataset.csv')
print(data.info())

# 2. Prepare features and target
X = data.drop('target', axis=1)
y = data['target']

# 3. Split data
X_train, X_test, y_train, y_test = train_test_split(
  X, y, test_size=0.2, random_state=42)

# 4. Preprocess
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# 5. Train model
model = RandomForestClassifier()
model.fit(X_train_scaled, y_train)

# 6. Evaluate
predictions = model.predict(X_test_scaled)
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.3f}")

Applications and Use Cases

Explore the wide range of real-world applications where machine learning excels.

Healthcare Finance Technology Business
# ML applications by industry
applications = {
  "Healthcare": [
    "Medical diagnosis and imaging",
    "Drug discovery and development",