Machine Learning Tutorial
Last Updated : 19 Jun, 2025
Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. In simple words, ML teaches the systems to think and understand like humans by learning from the data.
Machine Learning is mainly divided into three core types: Supervised, Unsupervised and Reinforcement Learning along with two additional types, Semi-Supervised and Self-Supervised Learning.
- Supervised Learning: Trains models on labeled data to predict or classify new, unseen data.
- Unsupervised Learning: Finds patterns or groups in unlabeled data, like clustering or dimensionality reduction.
- Reinforcement Learning: Learns through trial and error to maximize rewards, ideal for decision-making tasks.
Note: The following are not part of the original three core types of ML, but they have become increasingly important in real-world applications, especially in deep learning.
Additional Types:
- Self-Supervised Learning: Self-supervised learning is often considered a subset of unsupervised learning, but it has grown into its own field due to its success in training large-scale models. It generates its own labels from the data, without any manual labeling.
- Semi-Supervised Learning: This approach combines a small amount of labeled data with a large amount of unlabeled data. It’s useful when labeling data is expensive or time-consuming.
Module 1: Machine Learning Pipeline
In order to make predictions there are some steps through which data passes in order to produce a machine learning model that can make predictions.
- ML workflow
- Data Cleaning
- Feature Scaling
- Data Preprocessing in Python
Module 2: Supervised Learning
Supervised learning algorithms are generally categorized into two main types:
- Classification - where the goal is to predict discrete labels or categories
- Regression - where the aim is to predict continuous numerical values.
Supervised Learning There are many algorithms used in supervised learning each suited to different types of problems. Some of the most commonly used supervised learning algorithms are:
1. Linear Regression
This is one of the simplest ways to predict numbers using a straight line. It helps find the relationship between input and output.
2. Logistic Regression
Used when the output is a "yes or no" type answer. It helps in predicting categories like pass/fail or spam/not spam.
3. Decision Trees
A model that makes decisions by asking a series of simple questions, like a flowchart. Easy to understand and use.
4. Support Vector Machines (SVM)
A bit more advanced—it tries to draw the best line (or boundary) to separate different categories of data.
5. k-Nearest Neighbors (k-NN)
This model looks at the closest data points (neighbors) to make predictions. Super simple and based on similarity.
6. Naïve Bayes
A quick and smart way to classify things based on probability. It works well for text and spam detection.
7. Random Forest (Bagging Algorithm)
A powerful model that builds lots of decision trees and combines them for better accuracy and stability.
Introduction to Ensemble Learning
Ensemble learning combines multiple simple models to create a stronger, smarter model. There are mainly two types of ensemble learning:
- Bagging that combines multiple models trained independently.
- Boosting that builds models sequentially each correcting the errors of the previous one.
Module 3: Unsupervised learning
Unsupervised learning are again divided into three main categories based on their purpose:
Unsupervised learning1. Clustering
Clustering algorithms group data points into clusters based on their similarities or differences. Types of clustering algorithms are:
Centroid-based Methods:
Distribution-based Methods:
Connectivity based methods:
Density Based methods:
2. Dimensionality Reduction
Dimensionality reduction is used to simplify datasets by reducing the number of features while retaining the most important information.
3. Association Rule
Find patterns between items in large datasets typically in market basket analysis.
Module 4: Reinforcement Learning
Reinforcement learning interacts with environment and learn from them based on rewards.
Reinforcement Learning1. Model-Based Methods
These methods use a model of the environment to predict outcomes and help the agent plan actions by simulating potential results.
2. Model-Free Methods
The agent learns directly from experience by interacting with the environment and adjusting its actions based on feedback.
Module 5: Semi Supervised Learning
It uses a mix of labeled and unlabeled data making it helpful when labeling data is costly or it is very limited.
Semi Supervised LearningModule 6: Deployment of ML Models
The trained ML model must be integrated into an application or service to make its predictions accessible.
APIs allow other applications or systems to access the ML model's functionality and integrate them into larger workflows.
MLOps ensure they are deployed, monitored and maintained efficiently in real-world production systems.
For project ideas refer to: 100+ Machine Learning Projects with Source Code [2025] for hands-on implementation on projects
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