EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login

Machine Learning Tutorial

Home Data Science Data Science Tutorials Machine Learning Tutorial

Basic

Introduction To Machine Learning

What is Machine Learning?

Uses of Machine Learning

Applications of Machine Learning

Naive Bayes in Machine Learning

Dataset Labelling

DataSet Example

Deep Learning Techniques

Dataset ZFS

Careers in Machine Learning

What is Machine Cycle?

Machine Learning Feature

Machine Learning Programming Languages

What is Kernel in Machine Learning

Machine Learning Tools

Machine Learning Models

Machine Learning Platform

Machine Learning Libraries

Machine Learning Life Cycle

Machine Learning System

Machine Learning Datasets

Machine Learning Certifications

Machine Learning Python vs R

Optimization for Machine Learning

Types of Machine Learning

Machine Learning Methods

Machine Learning Software

Machine Learning Techniques

Machine Learning Feature Selection

Ensemble Methods in Machine Learning

Support Vector Machine in Machine Learning

Decision Making Techniques

Restricted Boltzmann Machine

Regularization Machine Learning

What is Regression?

What is Linear Regression?

Dataset for Linear Regression

Decision tree limitations

What is Decision Tree?

What is Random Forest

Algorithms

Machine Learning Algorithms

Apriori Algorithm in Machine Learning

Types of Machine Learning Algorithms

Bayes Theorem

AdaBoost Algorithm

Classification Algorithms

Clustering Algorithms

Gradient Boosting Algorithm

Mean Shift Algorithm

Hierarchical Clustering Algorithm

Hierarchical Clustering Agglomerative

What is a Greedy Algorithm?

What is Genetic Algorithm?

Random Forest Algorithm

Nearest Neighbors Algorithm

Weak Law of Large Numbers

Ray Tracing Algorithm

SVM Algorithm

Naive Bayes Algorithm

Neural Network Algorithms

Boosting Algorithm

XGBoost Algorithm

Pattern Searching

Loss Functions in Machine Learning

Decision Tree in Machine Learning

Hyperparameter Machine Learning

Unsupervised Machine Learning

K- Means Clustering Algorithm

KNN Algorithm

Monty Hall Problem

Supervised

What is Supervised Learning?

Supervised Machine Learning

Supervised Machine Learning Algorithms

Perceptron Learning Algorithm

Simple Linear Regression

Polynomial Regression

Multivariate Regression

Regression in Machine Learning

Hierarchical Clustering Analysis

Linear Regression Analysis

Support Vector Regression

Multiple Linear Regression

Linear Algebra in Machine Learning

Statistics for Machine Learning

What is Regression Analysis?

Clustering Methods

Backward Elimination

Ensemble Techniques

Bagging and Boosting

Linear Regression Modeling

What is Reinforcement Learning?

Classification

Kernel Methods in Machine Learning

Clustering in Machine Learning

Machine Learning Architecture

Automation Anywhere Architecture

Machine Learning C++ Library

Machine Learning Frameworks

Data Preprocessing in Machine Learning

Data Science Machine Learning

Classification of Neural Network

Neural Network Machine Learning

What is Convolutional Neural Network?

Single Layer Neural Network

Kernel Methods

Forward and Backward Chaining

Forward Chaining

Backward Chaining

Deep Learning

What Is Deep learning

Deep Learning

Application of Deep Learning

Careers in Deep Learnings

Deep Learning Frameworks

Deep Learning Model

Deep Learning Algorithms

Deep Learning Technique

Deep Learning Networks

Deep Learning Libraries

Deep Learning Toolbox

Types of Neural Networks

Convolutional Neural Networks

Create Decision Tree

Deep Learning for NLP

Caffe Deep Learning

Deep Learning with TensorFlow

RPA

What is RPA?

What is Robotics?

Benefits of RPA

RPA Applications

Types of Robots

RPA Tools

Line Follower Robot

What is Blue Prism?

RPA vs BPM

Interview Questions

Deep Learning Interview Questions

Machine Learning Cheat Sheet

Machine Learning Tutorial

Machine Learning coined by Arthur Samuel in the 1950s is a subset of Artificial Intelligence that deals with algorithms, statistic models and analytics. Traditionally, machines were designed to follow certain instructions given to them and did not possess the ability to make decisions. Machine Learning changes this by being able to analyze, predict or classify various data to reach the optimal solution. Machine Learning enables a system to make statistically significant decisions based on the data collected during past interactions. Machine Learning makes way for a possibility wherein a system can gain intelligence over time.

Why do we need Machine Learning?

In the digital age, Data is something that is abundantly available. The conventional way of programming is not the best solution to a problem involving pattern recognition or retaining a chunk of memory from a previous interaction. It gets complex and messy when trying to update for new requirements. Moreover, the traditional programming approach fails to handle a huge variety of data whereas, with Machine Learning, the more is always, the merrier. With the massive volume of data we generate, state-of-the-art Neural Nets models for easy pattern recognition are now possible.

Let’s have an example of some of the most common things we do almost every other day, like ordering food, groceries, or even clothes. All these now just a click away are powered by Machine Learning, which can find patterns and behaviors and learn from them without being explicitly programmed.

Applications of Machine Learning

The advent of ML technology has revolutionized our lives. They are so much blended into our daily routines that we mostly depend on them to accomplish our tasks. The smartphones that most of us cannot imagine our life without are majorly driven by Machine Learning. Right from unlocking the phone to using the various Social Media and e-Commerce apps installed, run on complex neural nets that are rigorously trained to give us a seamless usage.

In the ever-progressing world, Machine Learning is being recognized by several sectors for their betterment and to stand out amongst their competitors. Sectors such as Finance, Retail, Healthcare, Transport to name a few uses Machine Learning to reach out to more people and to create a personalized bond with them by taking into account their likes and dislikes.

Top Companies such as IBM, Google, Microsoft, Intel, Apple, Tesla, Facebook, Netflix, Instagram use Machine Learning effectively for reliable, fast and effective business decision making.

Img: Various Applications of Machine Learning

 

ML Tutorial

Prerequisites for Machine Learning

The cool things that can be achieved with Machine Learning are what attracts everyone to this field. But what one fails to notice is that a lot goes into the background that makes an application driven by ML successful. Machine Learning is about how well you can communicate with the machine to get the work done.

Fluency in either Scripting Languages, i.e., Python or R, is essential. Contrary to popular belief, one does not need to be an established mathematician or statistician to start with Machine Learning. However, working knowledge on the basics is a must, the pre-defined libraries in programming Languages like Python and R can take care of the job pretty well. In addition, it is also necessary to take the rust off from one’s analytical skills since 80% of the time in building a successful ML model goes to analysis and selection of the right kind of data.

Target Audience

Machine Learning Tutorials are mainly targeted to grad students and working professionals like Analysts, Data Scientists or Developers who are assumed to have some prior knowledge on the fundamentals of Computer Science. However, the audience need not be limited to only this set of people. Anyone with basic analytical and programming skills and the right attitude and determination can ace Machine Learning.

Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

SPSS, Data visualization with Python, Matplotlib Library, Seaborn Package

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Special Offer - All in One Data Science Bundle (360+ Courses, 50+ projects) Learn More