- Blog Categories
- Software Development
- Data Science
- AI/ML
- Marketing
- General
- MBA
- Management
- Legal
- Software Development Projects and Ideas
- 12 Computer Science Project Ideas
- 28 Beginner Software Projects
- Top 10 Engineering Project Ideas
- Top 10 Easy Final Year Projects
- Top 10 Mini Projects for Engineers
- 25 Best Django Project Ideas
- Top 20 MERN Stack Project Ideas
- Top 12 Real Time Projects
- Top 6 Major CSE Projects
- 12 Robotics Projects for All Levels
- Java Programming Concepts
- Abstract Class in Java and Methods
- Constructor Overloading in Java
- StringBuffer vs StringBuilder
- Java Identifiers: Syntax & Examples
- Types of Variables in Java Explained
- Composition in Java: Examples
- Append in Java: Implementation
- Loose Coupling vs Tight Coupling
- Integrity Constraints in DBMS
- Different Types of Operators Explained
- Career and Interview Preparation in IT
- Top 14 IT Courses for Jobs
- Top 20 Highest Paying Languages
- 23 Top CS Interview Q&A
- Best IT Jobs without Coding
- Software Engineer Salary in India
- 44 Agile Methodology Interview Q&A
- 10 Software Engineering Challenges
- Top 15 Tech's Daily Life Impact
- 10 Best Backends for React
- Cloud Computing Reference Models
- Web Development and Security
- Find Installed NPM Version
- Install Specific NPM Package Version
- Make API Calls in Angular
- Install Bootstrap in Angular
- Use Axios in React: Guide
- StrictMode in React: Usage
- 75 Cyber Security Research Topics
- Top 7 Languages for Ethical Hacking
- Top 20 Docker Commands
- Advantages of OOP
- Data Science Projects and Applications
- 42 Python Project Ideas for Beginners
- 13 Data Science Project Ideas
- 13 Data Structure Project Ideas
- 12 Real-World Python Applications
- Python Banking Project
- Data Science Course Eligibility
- Association Rule Mining Overview
- Cluster Analysis in Data Mining
- Classification in Data Mining
- KDD Process in Data Mining
- Data Structures and Algorithms
- Binary Tree Types Explained
- Binary Search Algorithm
- Sorting in Data Structure
- Binary Tree in Data Structure
- Binary Tree vs Binary Search Tree
- Recursion in Data Structure
- Data Structure Search Methods: Explained
- Binary Tree Interview Q&A
- Linear vs Binary Search
- Priority Queue Overview
- Python Programming and Tools
- Top 30 Python Pattern Programs
- List vs Tuple
- Python Free Online Course
- Method Overriding in Python
- Top 21 Python Developer Skills
- Reverse a Number in Python
- Switch Case Functions in Python
- Info Retrieval System Overview
- Reverse a Number in Python
- Real-World Python Applications
- Data Science Careers and Comparisons
- Data Analyst Salary in India
- Data Scientist Salary in India
- Free Excel Certification Course
- Actuary Salary in India
- Data Analyst Interview Guide
- Pandas Interview Guide
- Tableau Filters Explained
- Data Mining Techniques Overview
- Data Analytics Lifecycle Phases
- Data Science Vs Analytics Comparison
- Artificial Intelligence and Machine Learning Projects
- Exciting IoT Project Ideas
- 16 Exciting AI Project Ideas
- 45+ Interesting ML Project Ideas
- Exciting Deep Learning Projects
- 12 Intriguing Linear Regression Projects
- 13 Neural Network Projects
- 5 Exciting Image Processing Projects
- Top 8 Thrilling AWS Projects
- 12 Engaging AI Projects in Python
- NLP Projects for Beginners
- Concepts and Algorithms in AIML
- Basic CNN Architecture Explained
- 6 Types of Regression Models
- Data Preprocessing Steps
- Bagging vs Boosting in ML
- Multinomial Naive Bayes Overview
- Gini Index for Decision Trees
- Bayesian Network Example
- Bayes Theorem Guide
- Top 10 Dimensionality Reduction Techniques
- Neural Network Step-by-Step Guide
- Technical Guides and Comparisons
- Make a Chatbot in Python
- Compute Square Roots in Python
- Permutation vs Combination
- Image Segmentation Techniques
- Generative AI vs Traditional AI
- AI vs Human Intelligence
- Random Forest vs Decision Tree
- Neural Network Overview
- Perceptron Learning Algorithm
- Selection Sort Algorithm
- Career and Practical Applications in AIML
- AI Salary in India Overview
- Biological Neural Network Basics
- Top 10 AI Challenges
- Production System in AI
- Top 8 Raspberry Pi Alternatives
- Top 8 Open Source Projects
- 14 Raspberry Pi Project Ideas
- 15 MATLAB Project Ideas
- Top 10 Python NLP Libraries
- Naive Bayes Explained
- Digital Marketing Projects and Strategies
- 10 Best Digital Marketing Projects
- 17 Fun Social Media Projects
- Top 6 SEO Project Ideas
- Digital Marketing Case Studies
- Coca-Cola Marketing Strategy
- Nestle Marketing Strategy Analysis
- Zomato Marketing Strategy
- Monetize Instagram Guide
- Become a Successful Instagram Influencer
- 8 Best Lead Generation Techniques
- Digital Marketing Careers and Salaries
- Digital Marketing Salary in India
- Top 10 Highest Paying Marketing Jobs
- Highest Paying Digital Marketing Jobs
- SEO Salary in India
- Brand Manager Salary in India
- Content Writer Salary Guide
- Digital Marketing Executive Roles
- Career in Digital Marketing Guide
- Future of Digital Marketing
- MBA in Digital Marketing Overview
- Digital Marketing Techniques and Channels
- 9 Types of Digital Marketing Channels
- Top 10 Benefits of Marketing Branding
- 100 Best YouTube Channel Ideas
- YouTube Earnings in India
- 7 Reasons to Study Digital Marketing
- Top 10 Digital Marketing Objectives
- 10 Best Digital Marketing Blogs
- Top 5 Industries Using Digital Marketing
- Growth of Digital Marketing in India
- Top Career Options in Marketing
- Interview Preparation and Skills
- 73 Google Analytics Interview Q&A
- 56 Social Media Marketing Q&A
- 78 Google AdWords Interview Q&A
- Top 133 SEO Interview Q&A
- 27+ Digital Marketing Q&A
- Digital Marketing Free Course
- Top 9 Skills for PPC Analysts
- Movies with Successful Social Media Campaigns
- Marketing Communication Steps
- Top 10 Reasons to Be an Affiliate Marketer
- Career Options and Paths
- Top 25 Highest Paying Jobs India
- Top 25 Highest Paying Jobs World
- Top 10 Highest Paid Commerce Job
- Career Options After 12th Arts
- Top 7 Commerce Courses Without Maths
- Top 7 Career Options After PCB
- Best Career Options for Commerce
- Career Options After 12th CS
- Top 10 Career Options After 10th
- 8 Best Career Options After BA
- Projects and Academic Pursuits
- 17 Exciting Final Year Projects
- Top 12 Commerce Project Topics
- Top 13 BCA Project Ideas
- Career Options After 12th Science
- Top 15 CS Jobs in India
- 12 Best Career Options After M.Com
- 9 Best Career Options After B.Sc
- 7 Best Career Options After BCA
- 22 Best Career Options After MCA
- 16 Top Career Options After CE
- Courses and Certifications
- 10 Best Job-Oriented Courses
- Best Online Computer Courses
- Top 15 Trending Online Courses
- Top 19 High Salary Certificate Courses
- 21 Best Programming Courses for Jobs
- What is SGPA? Convert to CGPA
- GPA to Percentage Calculator
- Highest Salary Engineering Stream
- 15 Top Career Options After Engineering
- 6 Top Career Options After BBA
- Job Market and Interview Preparation
- Why Should You Be Hired: 5 Answers
- Top 10 Future Career Options
- Top 15 Highest Paid IT Jobs India
- 5 Common Guesstimate Interview Q&A
- Average CEO Salary: Top Paid CEOs
- Career Options in Political Science
- Top 15 Highest Paying Non-IT Jobs
- Cover Letter Examples for Jobs
- Top 5 Highest Paying Freelance Jobs
- Top 10 Highest Paying Companies India
- Career Options and Paths After MBA
- 20 Best Careers After B.Com
- Career Options After MBA Marketing
- Top 14 Careers After MBA In HR
- Top 10 Highest Paying HR Jobs India
- How to Become an Investment Banker
- Career Options After MBA - High Paying
- Scope of MBA in Operations Management
- Best MBA for Working Professionals India
- MBA After BA - Is It Right For You?
- Best Online MBA Courses India
- MBA Project Ideas and Topics
- 11 Exciting MBA HR Project Ideas
- Top 15 MBA Project Ideas
- 18 Exciting MBA Marketing Projects
- MBA Project Ideas: Consumer Behavior
- What is Brand Management?
- What is Holistic Marketing?
- What is Green Marketing?
- Intro to Organizational Behavior Model
- Tech Skills Every MBA Should Learn
- Most Demanding Short Term Courses MBA
- MBA Salary, Resume, and Skills
- MBA Salary in India
- HR Salary in India
- Investment Banker Salary India
- MBA Resume Samples
- Sample SOP for MBA
- Sample SOP for Internship
- 7 Ways MBA Helps Your Career
- Must-have Skills in Sales Career
- 8 Skills MBA Helps You Improve
- Top 20+ SAP FICO Interview Q&A
- MBA Specializations and Comparative Guides
- Why MBA After B.Tech? 5 Reasons
- How to Answer 'Why MBA After Engineering?'
- Why MBA in Finance
- MBA After BSc: 10 Reasons
- Which MBA Specialization to choose?
- Top 10 MBA Specializations
- MBA vs Masters: Which to Choose?
- Benefits of MBA After CA
- 5 Steps to Management Consultant
- 37 Must-Read HR Interview Q&A
- Fundamentals and Theories of Management
- What is Management? Objectives & Functions
- Nature and Scope of Management
- Decision Making in Management
- Management Process: Definition & Functions
- Importance of Management
- What are Motivation Theories?
- Tools of Financial Statement Analysis
- Negotiation Skills: Definition & Benefits
- Career Development in HRM
- Top 20 Must-Have HRM Policies
- Project and Supply Chain Management
- Top 20 Project Management Case Studies
- 10 Innovative Supply Chain Projects
- Latest Management Project Topics
- 10 Project Management Project Ideas
- 6 Types of Supply Chain Models
- Top 10 Advantages of SCM
- Top 10 Supply Chain Books
- What is Project Description?
- Top 10 Project Management Companies
- Best Project Management Courses Online
- Salaries and Career Paths in Management
- Project Manager Salary in India
- Average Product Manager Salary India
- Supply Chain Management Salary India
- Salary After BBA in India
- PGDM Salary in India
- Top 7 Career Options in Management
- CSPO Certification Cost
- Why Choose Product Management?
- Product Management in Pharma
- Product Design in Operations Management
- Industry-Specific Management and Case Studies
- Amazon Business Case Study
- Service Delivery Manager Job
- Product Management Examples
- Product Management in Automobiles
- Product Management in Banking
- Sample SOP for Business Management
- Video Game Design Components
- Top 5 Business Courses India
- Free Management Online Course
- SCM Interview Q&A
- Fundamentals and Types of Law
- Acceptance in Contract Law
- Offer in Contract Law
- 9 Types of Evidence
- Types of Law in India
- Introduction to Contract Law
- Negotiable Instrument Act
- Corporate Tax Basics
- Intellectual Property Law
- Workmen Compensation Explained
- Lawyer vs Advocate Difference
- Law Education and Courses
- LLM Subjects & Syllabus
- Corporate Law Subjects
- LLM Course Duration
- Top 10 Online LLM Courses
- Online LLM Degree
- Step-by-Step Guide to Studying Law
- Top 5 Law Books to Read
- Why Legal Studies?
- Pursuing a Career in Law
- How to Become Lawyer in India
- Career Options and Salaries in Law
- Career Options in Law India
- Corporate Lawyer Salary India
- How To Become a Corporate Lawyer
- Career in Law: Starting, Salary
- Career Opportunities: Corporate Law
- Business Lawyer: Role & Salary Info
- Average Lawyer Salary India
- Top Career Options for Lawyers
- Types of Lawyers in India
- Steps to Become SC Lawyer in India
- Tutorials
- Software Tutorials
- C Tutorials
- Recursion in C: Fibonacci Series
- Checking String Palindromes in C
- Prime Number Program in C
- Implementing Square Root in C
- Matrix Multiplication in C
- Understanding Double Data Type
- Factorial of a Number in C
- Structure of a C Program
- Building a Calculator Program in C
- Compiling C Programs on Linux
- Java Tutorials
- Handling String Input in Java
- Determining Even and Odd Numbers
- Prime Number Checker
- Sorting a String
- User-Defined Exceptions
- Understanding the Thread Life Cycle
- Swapping Two Numbers
- Using Final Classes
- Area of a Triangle
- Skills
- Explore Skills
- Management Skills
- Software Engineering
- JavaScript
- Data Structure
- React.js
- Core Java
- Node.js
- Blockchain
- SQL
- Full stack development
- Devops
- NFT
- BigData
- Cyber Security
- Cloud Computing
- Database Design with MySQL
- Cryptocurrency
- Python
- Digital Marketings
- Advertising
- Influencer Marketing
- Search Engine Optimization
- Performance Marketing
- Search Engine Marketing
- Email Marketing
- Content Marketing
- Social Media Marketing
- Display Advertising
- Marketing Analytics
- Web Analytics
- Affiliate Marketing
- MBA
- MBA in Finance
- MBA in HR
- MBA in Marketing
- MBA in Business Analytics
- MBA in Operations Management
- MBA in International Business
- MBA in Information Technology
- MBA in Healthcare Management
- MBA In General Management
- MBA in Agriculture
- MBA in Supply Chain Management
- MBA in Entrepreneurship
- MBA in Project Management
- Management Program
- Consumer Behaviour
- Supply Chain Management
- Financial Analytics
- Introduction to Fintech
- Introduction to HR Analytics
- Fundamentals of Communication
- Art of Effective Communication
- Introduction to Research Methodology
- Mastering Sales Technique
- Business Communication
- Fundamentals of Journalism
- Economics Masterclass
- Free Courses
Machine Learning with Python: List of Algorithms You Need to Master
Updated on 01 March, 2024
8.03K+ views
• 8 min read
Table of Contents
Diving into the world of Machine Learning with Python opens up a horizon of opportunities for anyone interested in the intersection of technology and innovation. Python, with its simplicity and robust libraries, is the perfect companion for those venturing into machine learning, providing an accessible yet powerful toolkit for crafting sophisticated solutions. Machine learning itself offers a canvas for creativity, where data and algorithms blend to forge systems that can adapt and learn.
The synergy between machine learning and Python paves the way for cutting-edge developments capable of addressing intricate problems. This article is designed to be an all-encompassing primer for individuals keen on navigating this vibrant domain, encompassing essential topics from the basics of Python and machine learning, to the integration of the two, the diversity of machine learning algorithms, and an overview of some key algorithms in Python.
What is Python?
It is an objective-oriented programing language that was developed in 1991 by Guido van Rossum. It is very to understand and learn. Python is popular amongst developers because it improves code reusability and program modularity. Python is a high-level interactive programming language that enables direct interaction between developers and interpreters – something that makes code writing very easy.
What is machine learning (ML)?
Machine learning is a branch of artificial intelligence that allows computers to undergo automatic learning and become better over time through experience. The main objective of machine learning is to come up with computer programs that have the capability to improve themselves based on new data without requiring any explicit programming for the same.
ML works in conjunction with statistical tools and data predict outputs. It also has an association with the Bayesian predictive model and data mining algorithm. After receiving input from the user, computers use an algorithm to deliver an output. There are several applications of machine learning, including predictive maintenance, fraud detection, automatic translation, video surveillance, and more.
If you are a beginner and interested to learn more about data science, check out our data science certification from top universities.
How do machine learning and Python add up?
Python has several features that make it an ideal match with machine learning. Some of these features are mentioned below:
1. It is easy to code. Writing code in Python is as easy as one, two, and three. It is far easier than other languages like Java and C++.
2. Integrated. It neither takes a lot of time not effort to integrate it with C, C++, and other programming languages.
3. Portable. It is an independent programming language. The same program written using Python can be executed on macOS or Windows. It doesn’t need different codes to run on different operating systems.
4. Object-oriented. It is the perfect example of an OOPs-based programming language. Concepts like objects, classes, encapsulation, inheritance, and polymorphism, amongst others, are common with object-oriented languages. Python supports all of these and more.
5. Dynamic. It is one of the very few dynamically typed languages. This means you are not required to declare the data type while writing code as it is decided at runtime when variables are declared.
Types of machine learning algorithms
Machine learning algorithms are broadly two categories- supervised and unsupervised. Let us discuss these two types in detail.
1. Supervised learning
Supervised learning is the most preferred type when it comes to practical machine learning problems. It has two types of variables – input variables and input variables. An algorithm is used to learn a function that maps the input to the output. The objective here is to estimate the mapping function in such a way that you or your machine can predict the output variable based on the input variable provided to you for a given data set, their are various types of supervised learning you must know.
It is referred to as supervised learning it works like how teachers supervise the learning process in the class. Here a training data set supervises the learning of an algorithm. We have the desired output – the algorithm under the supervision of the dataset continues to make iterative predictions until the desired level of performance is achieved.
This type of algorithm can be further separated into two groups- classification and regression. Classification algorithms are those that feature a category as the output variable. On the other hand, regression algorithms are those that have real value as the output variable – weight or dollars.
2. Unsupervised machine learning
In this type of machine learning algorithm, you have the input variables. No output variables are available. The objective of unsupervised learning is modeling data distribution or data structure to learn more about the data set. These algorithms are known as unsupervised learning algorithms – because they neither provide you the desired outputs nor they have anyone supervising the learning.
Algorithms are completely on their own, and they are responsible for both finding and presenting interesting learnings in a data set. These algorithms are further grouped into association and clustering problems. Clustering problems are those that have inherent groupings in the given data. On the other hand, association problems are those that have rules that define large parts of the data.
Some common machine learning algorithms in Python
1. Linear regression
This is a supervised machine learning algorithm in Python. It predicts an outcome and observes features. Based on the number of variables it runs on – one or many – we can refer to it as simple linear regression or multiple linear regression. It is amongst the most popular ML algorithms in Python.
It has a simple function – creating a line by putting weights against variables and then making a prediction. Linear regression is often used to predict real values like the cost of items. If there is a line that optimally defines the relationship that exists between independent and dependent variables, it is the regression line. Learn more about linear regression in Machine Learning.
2. Logistic regression
Again, this is a supervised ML algorithm. It is used in predicting discrete values, such as true or false, 0 or 1, and yes or no. It works on independent variables. A logistic function is used to make an estimation that provides either 0 or 1 as output. Though it is named regression, this algorithm is actually the classification type.
3. Support vector machines (SVM)
This is also a supervised learning algorithm. It belongs to supervised algorithm classification. It creates a line that separates different categories of a data set. This line is optimized by calculating the vector. It is done to make sure that the points that are the closest in each are the farthest apart from each other. Mostly it is the linear vector but sometimes it could be something else too.
4. Decision tree
This again falls under the supervised ML algorithms. However, it is used for both regression and classification. How does this algorithm work? It takes an instance, navigates the entire tree, and holds a feature comparison using a conditional statement. The side descends is based on the result. This ML algorithm in Python can work on continuous as well as categorical dependent variables.
Read: Prerequisite of Machine Learning
5. Naïve Bayes
This classification method is based on Bayes’ theorem. This classification method holds an assumption between predictors. So a Naïve Bayes classifier works on the assumption that a specific feature in a class has no relation whatsoever with any other feature of the same class. For instance, a fruit has several characteristics that make it what it is.
According to a Naïve Bayes classifier, each of these characteristics will contribute independently to the probability of that fruit being a certain type. This holds true even if the features are dependent on each other. Its model is quite simple and works great with larger data sets.
Also read: Machine Learning Libraries You Should Know About
Conclusion
In this blog, we learned about machine learning in Python and the various algorithms that we can use to train our machines to predict and perform better.
If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.
If you’re interested to learn data science & want to get your hands dirty on various tools and libraries, check out Executive PG Program in Data Science.
Frequently Asked Questions (FAQs)
1. What are the languages used in Machine Learning other than Python?
Besides Python, developers use R, Javascript, Java, C++, etc. R provides a software environment at no cost for statistical analysis and visualizations using the graph data structure. R is prioritized for biomedical data and bioengineering statistics. Javascript has popular libraries like Tensorflow.js, an advanced project developed by Google. Flexible APIs are available to train and build models directly in Javascript. Java provides software environments like Elka, RapidMiner, Weka, JavaML, Deeplearning4j, etc., for machine learning problems. C++ has many powerful libraries like Torch, TensorFlow, mlpack, etc., and efficiently performs tasks.
2. What are the differences between Supervised Learning and Unsupervised Learning?
Supervised learning contains known input data with labels to classify possible outcomes. Unsupervised learning deals with random input data that is further classified using unsupervised algorithms. Supervised learning uses offline interpretations, whereas unsupervised learning uses real-time data interpretations. The number of possible outcomes is already known in supervised learning, whereas, in the case of unsupervised learning, algorithms perform computations to find the number of results. Accuracy and reliability in supervised learning are better than unsupervised learning due to known possible classes of outcomes. Supervised learning predicts output based on categories, whereas unsupervised learning finds patterns in data for its predictions.
3. How is Linear Regression different from Logistic Regression?
Linear regression uses a set of independent variables to predict a continuous variable, whereas Logistic regression predicts a categorical variable. Linear regression is used for regression problems, and Logistic regression is used for classification problems. Linear regression gives a straight line, linear graph plot with a value that can exceed the limit from zero to one. Logistic Regression gives an S-shape curve in the graph plot within the range of zero to one to classify the inputs. Linear regression requires a linear relationship between the independent and dependent variables, which is not necessary in the case of Logistic regression.
RELATED PROGRAMS