Skip to content
geeksforgeeks
  • Courses
    • DSA to Development
    • Get IBM Certification
    • Newly Launched!
      • Master Django Framework
      • Become AWS Certified
    • For Working Professionals
      • Interview 101: DSA & System Design
      • Data Science Training Program
      • JAVA Backend Development (Live)
      • DevOps Engineering (LIVE)
      • Data Structures & Algorithms in Python
    • For Students
      • Placement Preparation Course
      • Data Science (Live)
      • Data Structure & Algorithm-Self Paced (C++/JAVA)
      • Master Competitive Programming (Live)
      • Full Stack Development with React & Node JS (Live)
    • Full Stack Development
    • Data Science Program
    • All Courses
  • Tutorials
    • Data Structures & Algorithms
    • ML & Data Science
    • Interview Corner
    • Programming Languages
    • Web Development
    • CS Subjects
    • DevOps And Linux
    • School Learning
  • Practice
    • Build your AI Agent
    • GfG 160
    • Problem of the Day
    • Practice Coding Problems
    • GfG SDE Sheet
  • Contests
    • Accenture Hackathon (Ending Soon!)
    • GfG Weekly [Rated Contest]
    • Job-A-Thon Hiring Challenge
    • All Contests and Events
  • Data Science
  • Data Science Projects
  • Data Analysis
  • Data Visualization
  • Machine Learning
  • ML Projects
  • Deep Learning
  • NLP
  • Computer Vision
  • Artificial Intelligence
Open In App
Next Article:
Python for Machine Learning
Next article icon

Machine Learning with Python Tutorial

Last Updated : 22 Apr, 2025
Comments
Improve
Suggest changes
Like Article
Like
Report

Python language is widely used in Machine Learning because it provides libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and Keras. These libraries offer tools and functions essential for data manipulation, analysis, and building machine learning models. It is well-known for its readability and offers platform independence. These all things make it the perfect language of choice for Machine Learning.

Machine Learning is a subdomain of artificial intelligence. It allows computers to learn and improve from experience without being explicitly programmed, and It is designed in such a way that allows systems to identify patterns, make predictions, and make decisions based on data.

So, let’s start Python Machine Learning guide to learn more about ML.

Introduction

  • Introduction to Machine Learning
  • What is Machine Learning?
  • ML – Applications
  • Difference between ML and AI
  • Best Python Libraries for Machine Learning

Data Processing

  • Understanding Data Processing
  • Generate test datasets
  • Create Test DataSets using Sklearn
  • Data Preprocessing
  • Data Cleansing
  • Label Encoding of datasets
  • One Hot Encoding of datasets
  • Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python

Supervised learning

  • Types of Learning – Supervised Learning
  • Getting started with Classification
  • Types of Regression Techniques
  • Classification vs Regression

Linear Regression

  • Introduction to Linear Regression
  • Implementing Linear Regression
  • Univariate Linear Regression
  • Multiple Linear Regression
  • Linear Regression using sklearn
  • Linear Regression Using Tensorflow
  • Linear Regression using PyTorch
  • Boston Housing Kaggle Challenge with Linear Regression [Project]

Polynomial Regression

  • Polynomial Regression ( From Scratch using Python )
  • Polynomial Regression
  • Polynomial Regression for Non-Linear Data
  • Polynomial Regression using Turicreate

Logistic Regression

  • Understanding Logistic Regression
  • Implementing Logistic Regression
  • Logistic Regression using Tensorflow
  • Softmax Regression using TensorFlow
  • Softmax Regression Using Keras

Naive Bayes

  • Naive Bayes Classifiers
  •  Naive Bayes Scratch Implementation using Python
  • Complement Naive Bayes (CNB) Algorithm
  • Applying Multinomial Naive Bayes to NLP Problems

Support Vector

  • Support Vector Machine Algorithm
  • Support Vector Machines(SVMs) in Python
  • SVM Hyperparameter Tuning using GridSearchCV
  • Creating linear kernel SVM in Python
  • Major Kernel Functions in Support Vector Machine (SVM)
  • Using SVM to perform classification on a non-linear dataset

Decision Tree

  • Decision Tree
  • Implementing Decision tree
  • Decision Tree Regression using sklearn

Random Forest

  • Random Forest Regression in Python
  • Random Forest Classifier using Scikit-learn
  • Hyperparameters of Random Forest Classifier
  • Voting Classifier using Sklearn
  • Bagging classifier

K-nearest neighbor (KNN)

  • K Nearest Neighbors with Python | ML
  • Implementation of K-Nearest Neighbors from Scratch using Python
  • K-nearest neighbor algorithm in Python
  • Implementation of KNN classifier using Sklearn
  • Imputation using the KNNimputer()
  • Implementation of KNN using OpenCV

Unsupervised Learning

  • Types of Learning – Unsupervised Learning
  • Clustering in Machine Learning
  • Different Types of Clustering Algorithm
  • K means Clustering – Introduction
  • Elbow Method for optimal value of k in KMeans
  • K-means++ Algorithm
  • Analysis of test data using K-Means Clustering in Python
  • Mini Batch K-means clustering algorithm
  • Mean-Shift Clustering
  • DBSCAN – Density based clustering
  • Implementing DBSCAN algorithm using Sklearn
  • Fuzzy Clustering
  • Spectral Clustering
  • OPTICS Clustering
  • OPTICS Clustering Implementing using Sklearn
  • Hierarchical clustering (Agglomerative and Divisive clustering)
  • Implementing Agglomerative Clustering using Sklearn
  • Gaussian Mixture Model

Projects using Machine Learning

  • Rainfall prediction using Linear regression
  • Identifying handwritten digits using Logistic Regression in PyTorch
  • Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
  • Implement Face recognition using k-NN with scikit-learn
  • Credit Card Fraud Detection
  • Image compression using K-means clustering

Applications of Machine Learning

  • How Does Google Use Machine Learning?
  • How Does NASA Use Machine Learning?
  • Targeted Advertising using Machine Learning
  • How Machine Learning Is Used by Famous Companies?

Applications Based on Machine Learning

Machine Learning is the most rapidly evolving technology; we are in the era of AI and ML. It is used to solve many real-world problems which cannot be solved with the standard approach. Following are some applications of ML.

  • Sentiment analysis
  • Fraud detection
  • Error detection and prevention
  • Weather forecasting and prediction
  • Speech synthesis
  • Recommendation of products to customers in online shopping.
  • Stock market analysis and forecasting
  • Speech recognition
  • Fraud prevention
  • Customer segmentation
  • Object recognition
  • Emotion analysis

GeeksforGeeks Courses

Machine Learning Basic and Advanced – Self Paced Course

Understanding the core idea of building systems has now become easier. With our Machine Learning Basic and Advanced – Self Paced Course, you will not only learn about the concepts of machine learning but will gain hands-on experience implementing effective techniques. This Machine Learning course will provide you with the skills needed to become a successful Machine Learning Engineer today. Enrol now!

Conclusion

Well, this is the end of this write-up here you will get all the details as well as all the resources about machine learning with Python tutorial. We are sure that this Python machine learning guide will provide a solid foundation in the field of machine learning.



Next Article
Python for Machine Learning
author
abhishek1
Improve
Article Tags :
  • AI-ML-DS
  • Machine Learning
  • python
Practice Tags :
  • Machine Learning
  • python

Similar Reads

  • AI With Python Tutorial
    This AI with Python tutorial covers the fundamental and advanced artificial intelligence (AI) concepts using Python. Whether you're a complete beginner or an experienced professional, this tutorial offers a step-by-step guide to mastering AI techniques. Why to use Python for AI?Python provides a cle
    9 min read
  • Python for Machine Learning
    Welcome to "Python for Machine Learning," a comprehensive guide to mastering one of the most powerful tools in the data science toolkit. Python is widely recognized for its simplicity, versatility, and extensive ecosystem of libraries, making it the go-to programming language for machine learning. I
    6 min read
  • Learn Data Science Tutorial With Python
    Data Science has become one of the fastest-growing fields in recent years, helping organizations to make informed decisions, solve problems and understand human behavior. As the volume of data grows so does the demand for skilled data scientists. The most common languages used for data science are P
    3 min read
  • Top Python Notebooks for Machine Learning
    Notebooks illustrates the analysis process step-by-step manner by arranging the stuff like text, code, images, output, etc. This helps a data scientist record the process of thinking while designing the process of research. Traditionally, notebooks were used to record work and replicate findings, si
    6 min read
  • 5 Reasons Why Python is Used for Machine Learning
    Machine learning (ML) stands out as a key technology in the fast-coming field of artificial intelligence and solutions based on data, with implications for a variety of sectors. Python, a programming language, is central to this transformation, becoming a top choice for machine learning researchers,
    7 min read
  • OpenCV Tutorial in Python
    OpenCV, short for Open Source Computer Vision Library, is an open-source computer vision and machine learning software library. Originally developed by Intel, it is now maintained by a community of developers under the OpenCV Foundation. OpenCVis a huge open-source library for computer vision, machi
    3 min read
  • NumPy Tutorial - Python Library
    NumPy (short for Numerical Python ) is one of the most fundamental libraries in Python for scientific computing. It provides support for large, multi-dimensional arrays and matrices along with a collection of mathematical functions to operate on arrays. At its core it introduces the ndarray (n-dimen
    3 min read
  • Getting Started with Python Programming
    Python is a versatile, interpreted programming language celebrated for its simplicity and readability. This guide will walk us through installing Python, running first program and exploring interactive coding—all essential steps for beginners. Install PythonBefore starting this Python course first,
    3 min read
  • Python - Create UIs for prototyping Machine Learning model with Gradio
    Gradio is an open-source python library which allows you to quickly create easy to use, customizable UI components for your ML model, any API, or any arbitrary function in just a few lines of code. You can integrate the GUI directly into your Python notebook, or you can share the link to anyone.Requ
    4 min read
  • Best Python libraries for Machine Learning
    Machine learning has become an important component in various fields, enabling organizations to analyze data, make predictions, and automate processes. Python is known for its simplicity and versatility as it offers a wide range of libraries that facilitate machine learning tasks. These libraries al
    9 min read
geeksforgeeks-footer-logo
Corporate & Communications Address:
A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)
Registered Address:
K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305
GFG App on Play Store GFG App on App Store
Advertise with us
  • Company
  • About Us
  • Legal
  • Privacy Policy
  • In Media
  • Contact Us
  • Advertise with us
  • GFG Corporate Solution
  • Placement Training Program
  • Languages
  • Python
  • Java
  • C++
  • PHP
  • GoLang
  • SQL
  • R Language
  • Android Tutorial
  • Tutorials Archive
  • DSA
  • Data Structures
  • Algorithms
  • DSA for Beginners
  • Basic DSA Problems
  • DSA Roadmap
  • Top 100 DSA Interview Problems
  • DSA Roadmap by Sandeep Jain
  • All Cheat Sheets
  • Data Science & ML
  • Data Science With Python
  • Data Science For Beginner
  • Machine Learning
  • ML Maths
  • Data Visualisation
  • Pandas
  • NumPy
  • NLP
  • Deep Learning
  • Web Technologies
  • HTML
  • CSS
  • JavaScript
  • TypeScript
  • ReactJS
  • NextJS
  • Bootstrap
  • Web Design
  • Python Tutorial
  • Python Programming Examples
  • Python Projects
  • Python Tkinter
  • Python Web Scraping
  • OpenCV Tutorial
  • Python Interview Question
  • Django
  • Computer Science
  • Operating Systems
  • Computer Network
  • Database Management System
  • Software Engineering
  • Digital Logic Design
  • Engineering Maths
  • Software Development
  • Software Testing
  • DevOps
  • Git
  • Linux
  • AWS
  • Docker
  • Kubernetes
  • Azure
  • GCP
  • DevOps Roadmap
  • System Design
  • High Level Design
  • Low Level Design
  • UML Diagrams
  • Interview Guide
  • Design Patterns
  • OOAD
  • System Design Bootcamp
  • Interview Questions
  • Inteview Preparation
  • Competitive Programming
  • Top DS or Algo for CP
  • Company-Wise Recruitment Process
  • Company-Wise Preparation
  • Aptitude Preparation
  • Puzzles
  • School Subjects
  • Mathematics
  • Physics
  • Chemistry
  • Biology
  • Social Science
  • English Grammar
  • Commerce
  • World GK
  • GeeksforGeeks Videos
  • DSA
  • Python
  • Java
  • C++
  • Web Development
  • Data Science
  • CS Subjects
@GeeksforGeeks, Sanchhaya Education Private Limited, All rights reserved
We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy
Lightbox
Improvement
Suggest Changes
Help us improve. Share your suggestions to enhance the article. Contribute your expertise and make a difference in the GeeksforGeeks portal.
geeksforgeeks-suggest-icon
Create Improvement
Enhance the article with your expertise. Contribute to the GeeksforGeeks community and help create better learning resources for all.
geeksforgeeks-improvement-icon
Suggest Changes
min 4 words, max Words Limit:1000

Thank You!

Your suggestions are valuable to us.

What kind of Experience do you want to share?

Interview Experiences
Admission Experiences
Career Journeys
Work Experiences
Campus Experiences
Competitive Exam Experiences