Types of Machine Learning

 Machine Learning (ML) is broadly categorized into three main types based on how the model learns from data:

1. Supervised Learning

• The model is trained on labeled data, meaning each input has a corresponding correct output.
• The goal is to learn how to map inputs to outputs.
• Examples:
  • Classification (e.g., spam detection, image recognition)
  • Regression (e.g., predicting house prices, stock price forecasting)
• ✅ How it works: Learns from labeled data (input-output pairs).

• ✅ Goal: Predict outcomes for new inputs based on past data.

• 🔹 Real-World Applications:

  • Email Spam Detection → Classify emails as spam or not spam.
  • Credit Scoring & Fraud Detection → Predict loan default risk.
  • Speech Recognition → Convert spoken words into text (e.g., Siri, Google Assistant).
  • Medical Diagnosis → Identify diseases from medical images (e.g., cancer detection).
  • Stock Market Prediction → Forecast stock prices based on historical data.

• 🔹 Common Algorithms:

  • Regression:
    • Linear Regression
    • Logistic Regression (for classification)
  • Decision Trees & Ensembles:
    • Decision Tree
    • Random Forest
    • Gradient Boosting (XGBoost, LightGBM)
  • Neural Networks:
    • Multi-Layer Perceptrons (MLPs)
    • Convolutional Neural Networks (CNNs) for image recognition

2. Unsupervised Learning

• The model is trained on unlabeled data, which explores the data to find patterns or structures without predefined outputs.
• Examples:
  • Clustering (e.g., customer segmentation, anomaly detection)
  • Dimensionality Reduction (e.g., PCA for feature selection, t-SNE for visualization)
• ✅ How it works: Learns patterns in unlabeled data (no predefined outputs).
• ✅ Goal: Find structure, groupings, or relationships in data.

• 🔹 Real-World Applications:

  • Customer Segmentation → Group customers by behavior for targeted marketing.
  • Anomaly Detection → Detect fraudulent transactions or cybersecurity threats.
  • Topic Modeling → Identify topics in large text datasets (e.g., news categorization).
  • Recommender Systems → Suggest products/movies (e.g., Netflix, Amazon).
  • Dimensionality Reduction → Compress high-dimensional data (e.g., PCA in image processing).

  🔹 Common Algorithms:

  • Clustering:
    • K-Means
    • Hierarchical Clustering
    • DBSCAN (Density-Based Spatial Clustering)
  • Association Rule Learning:
    • Apriori
    • FP-Growth (Frequent Pattern Growth)
  • Dimensionality Reduction:
    • Principal Component Analysis (PCA)
    • t-SNE (t-Distributed Stochastic Neighbor Embedding)

3. Reinforcement Learning (RL)

• The model (agent) learns by interacting with an environment and receiving feedback (rewards or penalties) to optimize long-term goals.
• Used in decision-making tasks requiring sequential actions.
• Examples:
  • Robotics
  • Game playing (e.g., AlphaGo, OpenAI's Dota 2 bot)
  • Self-driving cars
• ✅ How it works: The agent learns by interacting with an environment and receiving rewards/punishments.
• ✅ Goal: Maximize cumulative reward over time.

• 🔹 Real-World Applications:

  • Robotics → Teach robots to perform tasks (e.g., Boston Dynamics robots).
  • Autonomous Vehicles → Self-driving cars (e.g., Tesla’s Autopilot).
  • Game Playing → AlphaGo, OpenAI's Dota 2 bot, DeepMind’s AlphaZero.
  • Trading & Finance → Optimize stock trading strategies.
  • Smart Traffic Control → AI-driven traffic signal optimization.

  🔹 Common Algorithms:

  • Value-Based:
    • Q-Learning
    • Deep Q-Networks (DQN)
  • Policy-Based:
    • REINFORCE
    • Proximal Policy Optimization (PPO)
  • Actor-Critic Methods:
    • Advantage Actor-Critic (A2C)
    • Deep Deterministic Policy Gradient (DDPG)

Other Emerging Types

Some additional specialized types include:

• 🔹 Semi-Supervised Learning

Mix of labeled & unlabeled data → Used when labeling data is expensive.
✅ Example: Google’s image recognition models (train on a few labeled images + many unlabeled ones).

• 🔹 Self-Supervised Learning

Learns from data without explicit labels → Popular in NLP & vision.
✅ Example: GPT (ChatGPT), BERT for text understanding.

• 🔹 Federated Learning

Decentralized ML where models train across multiple devices without sharing raw data.
✅ Example: Google’s Gboard (keyboard learns from user typing without sending data to Google’s

Introduction

History

      In 1950

              - Samuel 's checker- playing program

      In 1960

               - Neural network : Rosenblatt's  perceptron

               - Pattern Recognition

               - Minsky & Papert prove limitations of Perception

      In 1970

               - Symbolic concept induction

               - Expert system & knowledge acquisition bottleneck

               - Quinlan's ID3

               - Natural language processing (symbolic)

     In 1980

            - Advance decision tree & rule learning 

            - Learning & planning and problem solving 

            - Resurgence of neural network

            -  Valiant's PAC learning theory

            -  Focus on experimental methodology

    In 1990   - ML & Statistics

            - Support Vector Machines

            - Data Mining 

            - Adaptive agents & web applications 

            - Text learning 

            - Reinforcement learning 

            - Ensembles

            - Bayes Net learning

    In 1994  - Self-driving car road test

    In 1997   -  Deep Blue beats Gary Kasparov

Popularity of this field in recent time & the reasons behind that

          - New software/algorithms

                 ➡️Neural networks

                 ➡️Deep learning

          -  New hardware

                 ➡️GPU's

          - Cloud Enabled

          - Availability of Big Data

    In 2009   -  Google builds self driving car

    In 2011  - Watson wins Jeopardy

    In 2014  - Human vision surpassed by ML systems

    In 2015  -  Machine translation system driven by Neural networks

Machine Learning   : Definition

     " Learning is the ability to improve  one's behaviour based on experience."

• Build computer systems that automatically improve with experience.
• What are the fundamental laws that govern all learning processes?
• Machine Learning explores algorithms that can  

            -   learn from data/build a model from data

            -   use the model from prediction, decision making or solving some tasks. For Example - Model can be used for prediction, decision making or solving any particular task.

 Tom Mitchell's definition

 "A computer program is said to be learn from experience E with respect to some class of tasks T & performance measure P, if it's performance at tasks in T, as measured by P, improves with experience E".

 Components of a learning problem

• Task : The behaviour or task that's being improved.   

             -  For example : classification, Prediction, acting in an environment.

• Data : The experiences that are being used to improve performance in the task.
• Measure of improvement : 

            -   For example : increasing accuracy in prediction, new skills that were not present initially, improved speed.
 

So this is our learning system. It is a box to which  we feed the  experiences  or the data and there is a problem or a task that require solution & you can also give background knowledge which will help the system & for problem/task the learning program comes up with a answer or a solution & its corresponding performance can be measured .Inside there are two components Learner and Reasoner. See the Learner takes experiences and from it can also takes the background knowledge and from this the Learner builds model and this models can be used by the Reasoner which given a task find the particular solution  to the task.

How we can go about creating a learner ?

these are the following steps to creating a learner-

1.  Choose the training experience(Features)
2.  Choose the target function(that to be learned)
3.  Choose how to expressed the target function
4. Choose a  learning algorithm to infer the target function.

Many domains and applications

Medicine:

• Diagnose a disease

              ーInput : symptoms,lab measurements, test result, DNA test,.....

              ーOutput :  one of set of possible diseases, or "none of  the above".

• Data mine historical medical records to learn which future patients will respond best to which treatments. 

Vision:

• Say what objects appear in an image
• Convert hand-written digits to characters 0....9
• Detect where objects appear in an image 

Robot control :

• Design autonomous mobile robots that learn to navigate from their own experience

Financial:

• predict if a stock will rise or fall

             - in the next few milliseconds.

• predict if a user will click on an advertisement or not

              - in order to decide which advertisement to show.

Some other applications

• Fraud detection : Credit card Providers
• Determine whether or not someone will default on a home mortgage.
• Understand consumer sentiment based off of unstructured text data.
• Forecasting women's conviction rates based off external macroeconomic factors.