Unit-I Introduction What is Machine Learning, Unsupervised
Learning, Reinforcement Learning Machine Learning
Use-Cases, Machine Learning Process Flow, Machine
Learning Categories, Linear regression and Gradient descent.
Machine Learning
1. Introduction to Machine Learning
Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables computers to learn from
data and make predictions or decisions without being explicitly programmed. ML models are trained
using algorithms that allow them to find patterns in data and improve their performance over time.
Why is Machine Learning Important?
● Automates decision-making processes.
● Enhances efficiency and accuracy in various industries.
● Powers AI-driven applications such as recommendation systems, fraud detection, and
self-driving cars.
2. Types of Machine Learning
Machine Learning is broadly classified into three categories:
1. Supervised Learning
● The model is trained on labeled data (i.e., each input is paired with the correct output).
● The goal is to learn a mapping function from inputs to outputs.
Supervised Learning Examples:
● Email Spam Detection (Spam vs. Not Spam)
● Handwritten Digit Recognition (0-9 digits)
● Disease Diagnosis (Healthy vs. Diseased)
Supervised Learning Algorithms:
, 1. Linear Regression – Predicts continuous values (e.g., predicting house prices).
2. Logistic Regression – Used for binary classification (e.g., fraud detection).
3. Decision Trees – A tree-based model for decision-making.
4. Random Forest – An ensemble of decision trees.
5. Support Vector Machines (SVM) – Finds the best boundary for classification.
6. Neural Networks – Used in deep learning for complex tasks like image recognition.
2. Unsupervised Learning
● The model learns from unlabeled data, finding hidden patterns.
● It is used when we don’t have predefined categories.
Unsupervised Learning Examples:
● Customer Segmentation (Grouping customers based on purchasing behavior).
● Anomaly Detection (Detecting fraudulent transactions).
● Market Basket Analysis (Identifying product purchase patterns).
Unsupervised Learning Algorithms:
1. K-Means Clustering – Groups data points into clusters.
2. Hierarchical Clustering – Builds a hierarchy of clusters.
3. Principal Component Analysis (PCA) – Reduces dimensionality of data.
4. Autoencoders – Used in deep learning for anomaly detection.
3. Reinforcement Learning (RL)
● The model learns by interacting with an environment and receiving rewards or penalties.
● The goal is to find the optimal policy to maximize cumulative rewards.
Reinforcement Learning Examples:
● Game AI (AlphaGo, OpenAI’s Dota bot).
● Robotics (Teaching robots how to walk).
● Self-Driving Cars (Learning to navigate roads).
Key Reinforcement Learning Concepts:
● Agent – The AI system making decisions.
● Environment – The world in which the agent operates.
● Actions – The choices the agent can make.
● Rewards – Feedback based on the agent’s actions.
, 3. Machine Learning Workflow
The process of building an ML model involves several steps:
1. Data Collection
● Gathering relevant and high-quality data from different sources (databases, APIs, sensors).
2. Data Preprocessing
● Handling Missing Values – Filling missing data with mean/median values.
● Data Normalization – Scaling data for better model performance.
● Feature Engineering – Selecting and transforming relevant features.
3. Model Selection
● Choosing the best algorithm based on the problem type (classification, regression, clustering).
4. Training the Model
● The model learns from the training dataset by optimizing parameters.
5. Model Evaluation
● Testing the model using a separate dataset to check performance.
● Common evaluation metrics:
○ Accuracy (for classification tasks).
○ Mean Squared Error (MSE) (for regression tasks).
○ F1-Score (for imbalanced datasets).
6. Hyperparameter Tuning
● Optimizing model parameters to improve performance.
● Techniques include Grid Search and Random Search.
7. Deployment
● Deploying the trained model to a real-world application (e.g., web app, mobile app, or cloud
service).
4. Popular Machine Learning Algorithms
A. Regression Algorithms (Predicting Continuous Values)
1. Linear Regression – Predicts a dependent variable based on independent variables.
Learning, Reinforcement Learning Machine Learning
Use-Cases, Machine Learning Process Flow, Machine
Learning Categories, Linear regression and Gradient descent.
Machine Learning
1. Introduction to Machine Learning
Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables computers to learn from
data and make predictions or decisions without being explicitly programmed. ML models are trained
using algorithms that allow them to find patterns in data and improve their performance over time.
Why is Machine Learning Important?
● Automates decision-making processes.
● Enhances efficiency and accuracy in various industries.
● Powers AI-driven applications such as recommendation systems, fraud detection, and
self-driving cars.
2. Types of Machine Learning
Machine Learning is broadly classified into three categories:
1. Supervised Learning
● The model is trained on labeled data (i.e., each input is paired with the correct output).
● The goal is to learn a mapping function from inputs to outputs.
Supervised Learning Examples:
● Email Spam Detection (Spam vs. Not Spam)
● Handwritten Digit Recognition (0-9 digits)
● Disease Diagnosis (Healthy vs. Diseased)
Supervised Learning Algorithms:
, 1. Linear Regression – Predicts continuous values (e.g., predicting house prices).
2. Logistic Regression – Used for binary classification (e.g., fraud detection).
3. Decision Trees – A tree-based model for decision-making.
4. Random Forest – An ensemble of decision trees.
5. Support Vector Machines (SVM) – Finds the best boundary for classification.
6. Neural Networks – Used in deep learning for complex tasks like image recognition.
2. Unsupervised Learning
● The model learns from unlabeled data, finding hidden patterns.
● It is used when we don’t have predefined categories.
Unsupervised Learning Examples:
● Customer Segmentation (Grouping customers based on purchasing behavior).
● Anomaly Detection (Detecting fraudulent transactions).
● Market Basket Analysis (Identifying product purchase patterns).
Unsupervised Learning Algorithms:
1. K-Means Clustering – Groups data points into clusters.
2. Hierarchical Clustering – Builds a hierarchy of clusters.
3. Principal Component Analysis (PCA) – Reduces dimensionality of data.
4. Autoencoders – Used in deep learning for anomaly detection.
3. Reinforcement Learning (RL)
● The model learns by interacting with an environment and receiving rewards or penalties.
● The goal is to find the optimal policy to maximize cumulative rewards.
Reinforcement Learning Examples:
● Game AI (AlphaGo, OpenAI’s Dota bot).
● Robotics (Teaching robots how to walk).
● Self-Driving Cars (Learning to navigate roads).
Key Reinforcement Learning Concepts:
● Agent – The AI system making decisions.
● Environment – The world in which the agent operates.
● Actions – The choices the agent can make.
● Rewards – Feedback based on the agent’s actions.
, 3. Machine Learning Workflow
The process of building an ML model involves several steps:
1. Data Collection
● Gathering relevant and high-quality data from different sources (databases, APIs, sensors).
2. Data Preprocessing
● Handling Missing Values – Filling missing data with mean/median values.
● Data Normalization – Scaling data for better model performance.
● Feature Engineering – Selecting and transforming relevant features.
3. Model Selection
● Choosing the best algorithm based on the problem type (classification, regression, clustering).
4. Training the Model
● The model learns from the training dataset by optimizing parameters.
5. Model Evaluation
● Testing the model using a separate dataset to check performance.
● Common evaluation metrics:
○ Accuracy (for classification tasks).
○ Mean Squared Error (MSE) (for regression tasks).
○ F1-Score (for imbalanced datasets).
6. Hyperparameter Tuning
● Optimizing model parameters to improve performance.
● Techniques include Grid Search and Random Search.
7. Deployment
● Deploying the trained model to a real-world application (e.g., web app, mobile app, or cloud
service).
4. Popular Machine Learning Algorithms
A. Regression Algorithms (Predicting Continuous Values)
1. Linear Regression – Predicts a dependent variable based on independent variables.
