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Outline of machine learning​

The following outline is provided as an overview of and topical guide to machine learning. Machine learning is a subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed".[2] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.[3] Such algorithms operate by building a modelfrom an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.

A Tour of Machine Learning Algorithms​

In this post, we take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available.
There are so many algorithms available that it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit.
I want to give you two ways to think about and categorize the algorithms you may come across in the field.
  • The first is a grouping of algorithms by the learning style.
  • The second is a grouping of algorithms by similarity in form or function (like grouping similar animals together).
Both approaches are useful, but we will focus in on the grouping of algorithms by similarity and go on a tour of a variety of different algorithm types.
After reading this post, you will have a much better understanding of the most popular machine learning algorithms for supervised learning and how they are related.

More details: ​https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

Top 10 Machine Learning Algorithms For Beginners

​To give you an example of the impact of machine learning, Man group’s AHL Dimension programme is a $5.1 billion dollar hedge fund which is partially managed by AI. After it started off, by the year 2015, its machine learning algorithms were contributing more than half of the profits of the fund even though the assets under its management were far less.
After reading this blog, you would be able to understand the basic logic behind some popular and incredibly resourceful machine learning algorithms which have been used by the trading community as well as serve as the foundation stone on which you step on to create the best machine learning algorithm. They are:
  • Linear Regression
  • Logistic regression
  • KNN Classification
  • Support Vector Machine (SVM)
  • Decision Trees
  • Random Forest
  • Artificial Neural Network
  • K-means Clustering
  • Naive Bayes theorem
  • Recurrent Neural Networks (RNN)

Other Lists of Algorithms

There are other great lists of algorithms out there if you’re interested. Below are few hand selected examples.
  • List of Machine Learning Algorithms: On Wikipedia. Although extensive, I do not find this list or the organization of the algorithms particularly useful.
  • Machine Learning Algorithms Category: Also on Wikipedia, slightly more useful than Wikipedias great list above. It organizes algorithms alphabetically.
  • CRAN Task View: Machine Learning & Statistical Learning: A list of all the packages and all the algorithms supported by each machine learning package in R. Gives you a grounded feeling of what’s out there and what people are using for analysis day-to-day.
  • Top 10 Algorithms in Data Mining: Published article and now a book (Affiliate Link) on the most popular algorithms for data mining. Another grounded and less overwhelming take on methods that you could go off and learn deeply.

​Machine Learning & Deep Learning Fundamentals

Watch the below video and for more information about the complete course ​https://deeplizard.com/learn/video/gZmobeGL0Yg
In this video, we introduce what this Deep Learning playlist will cover, and we also explain the concept of machine learning and how it contrasts with traditional programming. 🕒🦎 VIDEO SECTIONS 🦎🕒 00:00 Welcome to DEEPLIZARD - Go to deeplizard.com for learning resources 00:30 Help deeplizard add video timestamps - See example in the description 03:58 Collective Intelligence and the DEEPLIZARD HIVEMIND 💥🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎💥 👋 Hey, we're Chris and Mandy, the creators of deeplizard! 👀 CHECK OUT OUR VLOG: 🔗 https://youtube.com/deeplizardvlog 👉 Check out the blog post and other resources for this video: 🔗 https://deeplizard.com/learn/video/gZ... 💻 DOWNLOAD ACCESS TO CODE FILES 🤖 Available for members of the deeplizard hivemind: 🔗 https://deeplizard.com/resources 🧠 Support collective intelligence, join the deeplizard hivemind: 🔗 https://deeplizard.com/hivemind
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  • About
  • Research Interests
  • Projects
  • HONORS & AWARDS
  • Advice on Research & Learing
    • The Secret to Research Breakthroughs
    • Journals and Conferences
    • How to write Research Proposal
    • Learning Courses
    • IELTS/GRE >
      • More About IELTS
    • Machine Learning
    • How to Answer Common Interview Questions?
  • Country Guides
    • Study Abroad >
      • Fully Funded Scholarships for 2019 >
        • Fully Funded Scholarships for 2019 Version 4
        • Fully Funded Scholarships for 2019 Version 3
        • Study a MS/PhD program in Saudi Arabia
        • Fully Funded Graduate Research Scholarships, University of Melbourne, Australia
        • Fully Funded Master Scholarship, University of Westminster, UK
        • Fully Funded Scholarships 2019 Version 5
        • YOUNG PROFESSIONALS IN SPACE
      • University of Auckland
      • Fully Funded Studentships, London School of Economics and Political Science, UK
      • 2020 Famous Scholarships Positions
      • MEXT Japan Scholarship 2020 for Bachelor, Master & PhD [Fully Funded] Japanese Government Scholarship
      • Scholarships Blog
    • Fully Funded Scholarships for 2019 Version 2
    • PhD in New Zealand
    • Famous Universities Positions
    • Finland Scholarship Position
    • Sweden
    • New Zealand
    • Study in Norway
    • Ireland
    • Scholarships in China
    • Master Scholarships, Faculty of Medicine, Imperial College London, UK
    • Scholarships China (Apply before the deadline) >
      • Guangzhou University Important Files for PhD dissertation
      • Donghua University, China
  • Gallery
  • Contact
  • NewUpdate
    • World Youth Forum
    • Ehsaas Program 2020
    • Process to apply Umra Visa from china
    • Hajj 2020
    • Itikaf in the Holy Mosque
    • Insaf Sehat Card
    • All About Jenat
    • GoogleBaba
    • cricket
    • iPhone
    • Umrah
  • Blockchain
  • Software
    • ARYZip
    • Youtube
    • Grammarly
    • RDP
    • Telegram
    • Youtube Sub
    • Canva
  • Master of Social Work
  • Learn Crypto With Dr Hafiz