Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence
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An detailed introduction to https://machinelearningforkids.co.uk – a free tool for school children to learn about artificial intelligence and machine learning by making their own machine learning-powered projects.
I demonstrate some of the projects that children have made, and describe the lessons they learned through making them.
This is an updated version of a video I recorded a couple of years ago (https://www.youtube.com/watch?v=2drwelVD4Qw) – updated to reflect things like:
– changes in the tool’s UI
– move from Scratch 2 to Scratch 3
– support for sound machine learning models
– support for Python projects
– MIT App Inventor integration
– support for using the site without registration
“Machine Learning: Living in the Age of AI,” examines the extraordinary ways in which people are interacting with AI today. Hobbyists and teenagers are now developing tech powered by machine learning and WIRED shows the impacts of AI on schoolchildren and farmers and senior citizens, as well as looking at the implications that rapidly accelerating technology can have. The film was directed by filmmaker Chris Cannucciari, produced by WIRED, and supported by McCann Worldgroup.
Also, check out the free WIRED channel on Roku, Apple TV, Amazon Fire TV, and Android TV. Here you can find your favorite WIRED shows and new episodes of our latest hit series Tradecraft.
ABOUT WIRED
WIRED is where tomorrow is realized. Through thought-provoking stories and videos, WIRED explores the future of business, innovation, and culture.
Machine Learning: Living in the Age of AI | A WIRED Film
This Machine Learning basics video will help you understand what is Machine Learning, what are the types of Machine Learning – supervised, unsupervised & reinforcement learning, how Machine Learning works with simple examples, and will also explain how Machine Learning is being used in various industries. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results. Machine learning is starting to reshape how we live, and it’s time we understood what it is and why it matters. Now, let us deep dive into this short video and understand the basics of Machine Learning.
Below topics are explained in this Machine Learning basics video:
1. What is Machine Learning? ( 00:21 )
2. Types of Machine Learning ( 02:43 )
2. What is Supervised Learning? ( 02:53 )
3. What is Unsupervised Learning? ( 03:46 )
4. What is Reinforcement Learning? ( 04:37 )
5. Machine Learning applications ( 06:25 )
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
This video on “Supervised and Unsupervised Learning” will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabelled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.
Below are the topics explained in this supervised and unsupervised learning in Machine Learning Tutorial-
1. What is Machine Learning
– Types of Machine Learning
– Supervised Learning
– Unsupervised Learning
2. Supervised Learning
– Types of Supervised Learning
3. Unsupervised Learning
– Types of Unsupervised Learning
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
🔥 NIT Warangal Post Graduate Program on AI and Machine Learning: https://www.edureka.co/nitw-ai-ml-pgp
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on “AI vs Machine Learning vs Deep Learning” talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:
1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning
How it Works?
1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
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About the Course
Edureka’s Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:
1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
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Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next “Big Thing” and a must for Professionals in the Data Analytics domain.
For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Customer Review
Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: “I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the “customer of Edureka!”. Instructors are knowledge… able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring – All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working.”
This Machine Learning tutorial video is ideal for beginners to learn Machine Learning from scratch. By the end of this tutorial video, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases) and finally implement a Machine Learning project/ hands-on demo on Linear Regression Algorithm using Python.
This Machine Learning tutorial will cover the following topics:
1. Life without Machine Learning ( 01:06 )
2. Life with Machine Learning ( 02:29 )
3. What is Machine Learning ( 04:35 )
4. Machine Learning Process ( 05:27 )
5. Types of Machine Learning ( 06:14 )
6. Supervised Vs Unsupervised ( 09:32 )
7. The right Machine Learning solutions ( 10:35 )
8. Machine Learning Algorithms ( 13:33 )
9. Use case – Predicting the price of a house using Linear Regression ( 23:24 )
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.
– – – – – – –
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
– – – – – –
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
– – – – – – –
Who should take this Machine Learning Training Course?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
For many years scientists have been working to simulate human perception and human actions in machines. The aim: artificial intelligence. But if artificial intelligence is already superior to people in some areas, what role will we then still play in the future? And how can we trust these systems if today’s computers are susceptible to viruses and hacking? Check out latest video to find out more about.
Script download: www.explainity.com/education-project/transskripte/
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If you are interested in an own explainity explainer video, visit our website www.explainity.com and contact us. We are looking forward to your inquiry.
So we’ve talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever.
There are plenty of cool robots and robotics gadgets or toy robots that kids will absolutely love. Robot toys are best tech toys that will keep your kids entertained and maybe even spark their interest in robotics.We have created a shortlist of the top five robot toys for kids. 5 Best Robots for Kids : Games, Fun and Learning
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Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends.
This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You’ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.
In this series, we’ll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.
For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we’ll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we’ll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are.
In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. If you have a basic understanding of Python, and the willingness to learn/ask questions, you will be able to follow along here with no issues. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets. Math involved is typically linear algebra, but I will do my best to still explain all of the math. If you are confused/lost/curious about anything, ask in the comments section on YouTube, the community here, or by emailing me. You will also need Scikit-Learn and Pandas installed, along with others that we’ll grab along the way.
Machine learning was defined in 1959 by Arthur Samuel as the “field of study that gives computers the ability to learn without being explicitly programmed.” This means imbuing knowledge to machines without hard-coding it.
Six lines of Python is all it takes to write your first machine learning program! In this episode, we’ll briefly introduce what machine learning is and why it’s important. Then, we’ll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up.
Created by Ray Dalio this simple but not simplistic and easy to follow 30 minute, animated video answers the question, “How does the economy really work?” Based on Dalio’s practical template for understanding the economy, which he developed over the course of his career, the video breaks down economic concepts like credit, deficits and interest rates, allowing viewers to learn the basic driving forces behind the economy, how economic policies work and why economic cycles occur.
Check out this and other cool science experiments at http://www.stevespanglerscience.com/experiments/ Looking for something fun to do this Fourth of July? Try making a smoke ring generator using a trash can, a shower curtain and some smoke bombs.
Check out this and other cool Halloween science experiments at http://www.stevespanglerscience.com/experiments/ Steve Spangler teaches the secret on how to make perfect smoke rings every time!
About Steve Spangler Science…
Steve Spangler is a celebrity teacher, science toy designer, speaker, author and an Emmy award-winning television personality. Spangler is probably best known for his Mentos and Diet Coke geyser experiment that went viral in 2005 and prompted more than 1,000 related YouTube videos. Spangler is the founder of www.SteveSpanglerScience.com, a Denver-based company specializing in the creation of science toys, classroom science demonstrations, teacher resources and home for Spangler’s popular science experiment archive and video collection. Spangler is a frequent guest on the Ellen DeGeneres Show where he takes classroom science experiments to the extreme. Check out his pool filled with 2,500 boxes of cornstarch!
On the education side, Spangler started his career as a science teacher in the Cherry Creek School district for 12 years. Today, Steve travels extensively training teachers in ways to make learning more engaging and fun. His hands-on science boot camps and summer institutes for teachers inspire and teach teachers how to prepare a new generation for an ever-changing work force. Over the last 15 years, he has also made more than 500 television appearances as an authority on hands-on science and inquiry-based learning.
On the business side, Spangler is the founder and CEO of Steve Spangler Science, a Denver-based company specializing in the creation of educational toys and kits and hands-on science training services for teachers. The companys unique business strategies and viral creations have been featured in the Wall Street Journal, Inc. Magazine, Wired and TIME Magazine where online readers voted Steve Spangler #18 in the Top 100 Most Influential People of the Year for 2006 (what were they thinking?). You’ll find more than 140 Spangler created products available online at SteveSpanglerScience.com and distributed to toy stores and mass-market retailers worldwide.
Spangler joined NBC affiliate 9News in 2001 as the science education specialist. His weekly experiments and science segments are designed to teach viewers creative ways to make learning fun. His now famous Mentos Geyser experiment, turning 2-liter bottles of soda into erupting fountains, became an Internet sensation in September 2005 when thousands of people started posting their own Mentos explosions on YouTube.com.
As founder of SteveSpanglerScience.com, Spangler and his design team have developed more than 140 educational toys and science-related products featured by mass-market retailers like Target, Wal-Mart, Toys R’ Us, Discovery Channel Stores and over 1,400 independent specialty toy stores. His educational science catalog and on-line business offers more than a thousand science toys and unique learning resources. Recently, Spangler has been featured in the Wall Street Journal, Inc. Magazine, WIRED, the History Channel, Food Network and TIME Magazine where on-line readers voted Steve Spangler #18 in the Top 100 Most Influential People of the Year for 2006.
His recent appearances on the Ellen DeGeneres Show have taught viewers how to blow up their food, shock their friends, create mountains of foam, play on a bed of nails, vanish in a cloud of smoke and how to turn 2,500 boxes of cornstarch and a garden hose into a swimming pool of fun.
Steve Spangler is a celebrity teacher, science toy designer, speaker, author and an Emmy award-winning television personality. Spangler is probably best known for his Mentos and Diet Coke geyser experiment that went viral in. Spangler is the founder of www.SteveSpanglerScience.com, a Denver-based company specializing in the creation of science toys, classroom science demonstrations, teacher resources and home for Spangler’s popular science experiment archive and video collection. Spangler is a frequent guest on the Ellen DeGeneres Show and Denver 9 News where he takes classroom science experiments to the extreme. For teachers, parents or DIY Science ideas – check out other sources of learning:
