🎯 MemoLearning Classification Algorithms

Master machine learning algorithms for predicting categorical outcomes and class labels

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Classification Algorithms Curriculum

12
Core Units
~70
ML Concepts
15+
Algorithms
20+
Evaluation Metrics
1

Classification Fundamentals

Understand the basics of classification problems and how they differ from regression tasks.

  • What is classification
  • Binary vs multiclass classification
  • Supervised learning overview
  • Features and target variables
  • Training and test data
  • Decision boundaries
  • Classification vs regression
  • Real-world applications
2

Logistic Regression

Learn logistic regression as a fundamental classification algorithm for binary and multiclass problems.

  • Linear to logistic regression
  • Sigmoid function
  • Odds and log-odds
  • Maximum likelihood estimation
  • Gradient descent optimization
  • Regularization (L1/L2)
  • Multinomial logistic regression
  • Coefficient interpretation
3

Decision Trees

Master decision tree algorithms for intuitive and interpretable classification models.

  • Tree structure and nodes
  • Information gain and entropy
  • Gini impurity
  • Splitting criteria
  • Pruning techniques
  • Handling categorical features
  • Tree visualization
  • Advantages and limitations
4

Random Forest

Learn ensemble methods with Random Forest for improved accuracy and reduced overfitting.

  • Ensemble learning concepts
  • Bootstrap aggregating (bagging)
  • Random feature selection
  • Voting mechanisms
  • Out-of-bag error
  • Feature importance
  • Hyperparameter tuning
  • Bias-variance tradeoff
5

Support Vector Machines

Understand SVM algorithms for finding optimal decision boundaries and handling non-linear data.

  • Maximum margin classifier
  • Support vectors
  • Soft margin and C parameter
  • Kernel trick
  • RBF, polynomial, linear kernels
  • Gamma parameter
  • Multiclass SVM
  • SVM for non-linear problems
6

Naive Bayes

Apply probabilistic classification using Naive Bayes algorithms for text and categorical data.

  • Bayes' theorem
  • Naive independence assumption
  • Gaussian Naive Bayes
  • Multinomial Naive Bayes
  • Bernoulli Naive Bayes
  • Laplace smoothing
  • Text classification applications
  • Handling continuous features
7

K-Nearest Neighbors

Learn instance-based learning with KNN for simple yet effective classification.

  • Distance-based classification
  • Choosing optimal K
  • Distance metrics
  • Weighted voting
  • Curse of dimensionality
  • Feature scaling importance
  • Computational efficiency
  • Local vs global patterns
8

Gradient Boosting

Master advanced ensemble methods including XGBoost, LightGBM, and CatBoost.

  • Boosting vs bagging
  • AdaBoost algorithm
  • Gradient boosting machines
  • XGBoost implementation
  • LightGBM features
  • CatBoost for categorical data
  • Regularization in boosting
  • Early stopping
9

Neural Networks

Introduction to neural networks and deep learning for classification tasks.

  • Perceptron model
  • Multi-layer perceptrons
  • Activation functions
  • Backpropagation
  • Hidden layers and neurons
  • Regularization techniques
  • Deep learning frameworks
  • When to use neural networks
10

Model Evaluation

Learn comprehensive methods to evaluate and compare classification model performance.

  • Confusion matrix
  • Accuracy, precision, recall
  • F1-score and F-beta
  • ROC curves and AUC
  • Precision-recall curves
  • Cross-validation
  • Classification reports
  • Multiclass evaluation metrics
11

Imbalanced Data

Handle imbalanced datasets with specialized techniques and evaluation strategies.

  • Identifying class imbalance
  • Sampling techniques (SMOTE, undersampling)
  • Cost-sensitive learning
  • Threshold tuning
  • Ensemble methods for imbalance
  • Evaluation metrics for imbalanced data
  • Business cost considerations
  • Real-world imbalanced problems
12

Model Selection and Deployment

Choose optimal algorithms, tune hyperparameters, and deploy classification models in production.

  • Algorithm selection criteria
  • Hyperparameter optimization
  • Grid search and random search
  • Bayesian optimization
  • Model pipelines
  • Feature engineering integration
  • Model deployment strategies
  • Monitoring and maintenance

Unit 1: Classification Fundamentals

Understand the basics of classification problems and how they differ from regression tasks.

What is Classification

Learn classification as the task of predicting discrete class labels for input instances.

Supervised Learning Discrete Outcomes Pattern Recognition
Classification algorithms learn to map input features to discrete output categories, enabling automated decision-making and pattern recognition.

Binary vs Multiclass Classification

Distinguish between binary classification (two classes) and multiclass classification (multiple classes).

# Binary classification example
# Predict: Spam or Not Spam
y_binary = [0, 1, 1, 0, 1] # 0=Not Spam, 1=Spam

# Multiclass classification example
# Predict: Flower species
y_multiclass = ['setosa', 'versicolor', 'virginica']

Supervised Learning Overview

Understand how classification fits into the supervised learning paradigm with labeled training data.

Supervised learning uses labeled examples to learn a mapping from inputs to outputs, enabling predictions on new, unseen data.
from sklearn.model_selection import train_test_split
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
  X, y, test_size=0.2, random_state=42)

Features and Target Variables

Learn to identify and prepare features (inputs) and target variables (outputs) for classification.

import pandas as pd
# Features (independent variables)
X = df[['age', 'income', 'education_level']]

# Target variable (dependent variable)
y = df['purchased'] # 0 or 1

Training and Test Data

Understand the importance of separating data for training models and evaluating their performance.

Training data: Used to learn model parameters
Validation data: Used for hyperparameter tuning
Test data: Used for final performance evaluation
# Common split ratios
# 70% training, 15% validation, 15% test
# or 80% training, 20% test (with cross-validation)

Decision Boundaries

Visualize how classification algorithms create decision boundaries to separate different classes.

import matplotlib.pyplot as plt
import numpy as np
# Visualize decision boundary
def plot_decision_boundary(model, X, y):
  # Create mesh grid
  h = 0.02
  x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
  y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1

Classification vs Regression

Compare classification (predicting categories) with regression (predicting continuous values).

Classification: Predicts discrete classes (cat, dog, bird)
Regression: Predicts continuous values (price, temperature, age)
# Classification output
predicted_class = model.predict(X_new) # ['cat', 'dog']

# Regression output
predicted_value = model.predict(X_new) # [25.7, 30.2]

Real-world Applications

Explore common real-world applications where classification algorithms solve business problems.

• Email spam detection
• Medical diagnosis
• Image recognition
• Fraud detection
• Customer segmentation
• Sentiment analysis

Unit 2: Logistic Regression

Learn logistic regression as a fundamental classification algorithm for binary and multiclass problems.

Linear to Logistic Regression

Understand how logistic regression extends linear regression for classification problems.

Linear regression: ŷ = β₀ + β₁x₁ + β₂x₂ + ... + βₚxₚ
Logistic regression: P(y=1) = 1 / (1 + e^(-z)) where z = β₀ + β₁x₁ + ...
from sklearn.linear_model import LogisticRegression
# Create and train logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)

# Get probability predictions
probabilities = model.predict_proba(X_test)

Sigmoid Function

Learn how the sigmoid function transforms linear outputs into probabilities between 0 and 1.

import numpy as np
import matplotlib.pyplot as plt

def sigmoid(z):
  return 1 / (1 + np.exp(-z))