Lecture Notes on Data Science and Machine Learning Algorithms

Agenda

• Introduction to Data Science (Basics and Fundamentals)
• Statistics and Probability Module
• Basics of Machine Learning
• Supervised Learning Algorithms (Linear Regression, Logistic Regression, Decision Trees, Random Forest, K-Nearest Neighbor, Naive Bayes, Support Vector Machine)
• Unsupervised Learning Module (Clustering, Association Rule Mining)
• Reinforcement Learning Module
• Deep Learning Module
• Data Science Interview Questions Module

Data Science Overview

What is Data Science?

• Deriving insights from data to solve real-world problems.
• Applications include: Walmart uses data to find shopping patterns, Netflix movie recommended by using data viewing patterns.
• Data Scientist: roles include data extraction, cleaning, exploration, modeling, evaluation.

Importance and Need for Data Science

• Increase in data generation: e.g., 2.5 quintillion bytes/day, need to process and derive insights.
• Executes better business decisions: forecasting sales, market analysis, detecting fraud, etc.
• Can handle complex, unstructured data.

Supervised Learning Algorithms

Linear Regression

• Simplest algorithm; used for regression tasks.
• Equation of a straight line: y = mx + c.
• Objective: minimize the error between observed and predicted values.
• Applications: evaluating sales trends, price impact on consumers, risk assessment.

Logistic Regression

• Used for binary classification.
• Uses sigmoid function to map predicted values to a category (0 or 1).
• Equation transformed for probabilities.
• Applications: weather prediction, classification problems, health diagnosis.

Decision Tree

• Graphical representation of all possible solutions to a decision based on certain conditions.
• Splits data recursively based on feature values using impurity measures like Gini index, Information Gain.
• Applications: business decision making, customer behavior prediction.

Random Forest

• Ensemble method using multiple decision trees to improve accuracy and performance.
• Prints multiple trees and each tree votes for the outcome (Majority Voting or averaging regression output).
• Applications: risk assessment in banking, predicting diseases, marketing analysis.

K-Nearest Neighbor (KNN)

• Stores all available data and classifies new data points based on similarity measures (e.g., Euclidean distance).
• Uses majority voting (e.g., k=3 uses three nearest neighbors to vote for prediction).
• Applications: recommendation systems, similarity searches.

Naive Bayes

• Based on Bayes' Theorem; assumes features are independent.
• Commonly used for text classification (e.g., spam detection, news categorization).
• Applications: Document classification, medical diagnosis.

Support Vector Machine (SVM)

• Used for classification and regression tasks.
• Objective: find the hyperplane that best separates data into classes (maximize margin between support vectors).
• Uses kernels for non-linear data separations in higher dimensions.
• Applications: face recognition, bioinformatics.

Unsupervised Learning Algorithms

Clustering

• Dividing data into groups with similar traits.
• Types: K-means (exclusive clustering), Fuzzy C-means (overlapping clustering), Hierarchical clustering (tree-like structure of clusters).
• Applications: Market Basket Analysis, customer segmentation.

Association Rule Mining

• Market Basket Analysis: identifying patterns and associations between different items bought together.
• Apriori Algorithm: used to define frequent itemsets and generate association rules.
• Metrics: Support, Confidence, Lift.
• Applications: product recommendation in retail, cross-selling.

Reinforcement Learning Algorithms

Introduction to Reinforcement Learning

• Learning by interacting with the environment (trial and error).
• Each action results in a reward or punishment; goal is to maximize total reward.
• Agent must discover optimal policy to achieve maximum reward.

Concepts in RL

• Agent: entity making decisions.
• Environment: world through which agent interacts.
• Action: all possible moves the agent can make.
• State: current situation returned by the environment.
• Reward: feedback from the environment.
• Policy: strategy the agent employs to determine actions.
• Value: expected long-term return with discount.
• Action Value (Q-value): similar to Value but considers specific actions.

Examples

• Shortest Path Problem: Using states, actions, rewards to find an optimal path.
• Q-Learning: algorithm to learn the value of actions and states (based on reward maximization and exploration).
• Application: autonomous robots in warehouses.

Deep Learning Module

• What is Deep Learning?
• Types of Neural Networks: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)
• Applications: image recognition, natural language processing.

Data Science Interview Questions

• Important concepts and topics common in data science interviews.
• Tips and strategies to ace the interview.

Summary

• Data Science involves using statistical and machine learning techniques to analyze and predict outcomes from complex and large datasets.
• Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.
• Reinforcement Learning involves an agent interacting with an environment to maximize total reward.
• Various tools and techniques help in processing and analyzing data for meaningful insights.