5 Coursera s Neural Networks for Machine Learning
Difference between Machine Learning and Artificial Intelligence
Artificial Intelligence (AI) and ML are not interchangeable terms. ML is sortof a subset of AI, which is a part of computer science trying to develop“machines capable of intelligent behavior.” Then, what is Machine Learning(ML)? “The science of getting computers to act without being explicitlyprogrammed,” says Stanford. So you get that difference? You need both AI andML experts to make smart machines that are truly intelligent.
Why are Machine Learning and Artificial Intelligence “Hot”?
“Machine learning is a core, transformative way by which we’re rethinkingeverything we’re doing” — Sundar Pichai, Google CEOThe pervasive commercial success of machine learning/artificial intelligenceis visible everywhere—from Amazon recommending what movies you might like tosee to self-driving Google cars that can tell a tree from a pedestrian.AI/ML has changed how data-driven business leaders make decisions, gage theirbusinesses, study human behavior, and view predictive analytics. If yourorganization needs to unleash the benefits of this extraordinary field, youneed the right minds—quants and translators.With breakthroughs such as parallel computation that’s cheap, Big Data, andimproved algorithms, utilitarian AI is what the world is moving toward. Theincreased need to handle huge amounts of data and the number of IoT connecteddevices that define the world today reinforce the importance of machinelearning.AI/ML, with tons of potential, is a great career choice for engineers or datamining/ pattern recognition enthusiasts out there. Also, Machine Learning isintegral to data science, which is touted as the sexiest job of the 21stcentury by the Harvard Business Review.An Evans Data Corp. study found that 36% of the 500 developers surveyed useelements of ML in their Big Data or other analytical projects. CEO JanelGarvin said, “Machine learning includes many techniques that are rapidly beingadopted at this time and the developers who already work with Big Data andadvanced analytics are in an excellent position to lead the way.”She added: “We are seeing more and more interest from developers in all formsof cognitive computing, including pattern recognition, natural languagerecognition, and neural networks and we fully expect that the programs oftomorrow are going to be based on these nascent technologies of today.”So, for people who have a degree in Computer Science, Machine Learning,Operational Research, or Statistics, the world could well be their oyster forsome time to come, right?
1. Machine Learning by Andrew Ng
Co-founder of Coursera, Andrew Ng, takes this 11-week course. He is anAssociate Professor at Stanford University and the Chief Scientist at Baidu.As an applied machine learning class, it talks about the best machine learningtechniques and statistical pattern recognition, and teaches you how toimplement learning algorithms.Broadly, it covers supervised and unsupervised learning, linear and logisticregression, regularization, and Naïve Bayes. He uses Octave and MatLab. Thecourse is rich in case studies and recent practical applications. Students areexpected to know the basics of probability, linear algebra, and computerscience. The course has rave reviews from the users.Go to Course: Start learning
2. Udacity’s Intro to Machine Learning
A part of Udacity’s Data Analyst Nanodegree, this approximately 10-week courseteaches all you need to know to handle data sets using machine learningtechniques to extract useful insights. Instructors Sebastian Thrun and KatieMalone will expect the beginners to know basic statistical concepts andPython.This course teaches you everything from clustering to decision trees, from MLalgorithms such as Adaboost to SVMs. People also recommend you take thefoundational Intro to Data Science course which deals with Data Manipulation,Data Analysis, Data Communication with Information Visualization, and Data atScale.Go to Course: Start learning
3. EdX’s Learning from Data(Introductory Machine Learning)
Yaser S. Abu-Mostafa, Professor of Electrical Engineering and Computer Scienceat the California Institute of Technology, will teach you the basictheoretical principles, algorithms, and applications of Machine Learning.The course requires an effort of 10 to 20 hours per week and lasts 10 weeks.They have another 5-week-course, Machine Learning for Data Science andAnalytics, where newbies can learn more about algorithms.Go to Course: Start learning
5. Coursera’s Neural Networks for Machine Learning
Emeritus Distinguished Professor Gregory Hinton, who also works atGoogle’sMountain View facility, from the University of Toronto teaches this 16-weekadvanced course offered by Coursera.A pioneer in the field of deep learning, Hinton’s lecture videos on YouTubetalk about the application of neural networks in image segmentation, humanmotion, modeling language, speech and object recognition, and so on. Studentsare expected to be comfortable with calculus and have requisite experience inPython programming.Go to Course: Start learning
6. Google’s Deep Learning
Udacity offers this amazing free course which “takes machine learning to thenext level.” Google’s 3-month course is not for beginners. It talks about themotivation for deep learning, deep neural networks, convolutional networks,and deep models for text and sequences.Course leads Vincent Vanhoucke and Arpan Chakraborty expect the learners tohave programming experience in Python and some GitHub experience and to knowthe basic concepts of ML and statistics, linear algebra, and calculus. TheTensorFlow (Google’s own deep learning library) course has an added advantageof being self-paced.Go to Course: Start learning
9. Coursera’s Machine Learning Specialization
The University of Washington has created five courses, with practical casestudies, to teach you the basics of Machine Learning. This 6-week course whichrequire between 5 and 8 hours of study a week, will cover ML foundations,classification, clustering, regression, recommender systems and dimensionalityreduction, and project using deep learning.Amazon’s Emily Fox and Carlos Guestrin are the instructors, and they expectthe learners to have basic math and programming skills along with a workingknowledge of Python. Course access is free though getting a valid certificateis not.Go to Course: Start learning
The Future of Artificial Intelligence Programming
Artificial intelligence programs of today can accomplish many functions thatwere only dreamed of in the early days of AI. AI programs can not only playgames, but can beat the champions of the most complex games, such as chess.Automated vehicles can drive without human intervention, while programmershone the abilities of a program to respond in human-like manners. The advancesof today show clearly that the future of artificial intelligence is extremelybright.Love Letters to ComputersVIDEOLove Letters for Computers is a free resource including a series of videos,resources, classroom materials and a teacher journal that will help you planhow to integrate computer science into your curriculum for children inkindergarten and first years of primary school. It’s built around thephilosophy of Hello Ruby: an unplugged, creative and playful approach tointroducing computer science.If you’re interested in additional hands-on training or translating thismaterial, visit the More section.
In this video we’ll familiarise ourselves with the key concepts and practicesof computational thinking. We’ll reflect on how CT can be offered acrosscurriculum and offer cross-curricular examples. Covering decomposition,abstraction, pattern recognition and algorithms – and how the concepts ofcomputational thinking can be related back to everyday, real-life activities.We’ll learn more about tinkering, creating, persevering, debugging,collaborating..VIDEO
Machine Learning and AI
How does machine learning work? What kind of jobs will robots do, what abouthumans? How do computers learn? This video will explore machine learning, AIand how computers are changing our society.VIDEO
AI vs. Machine Learning
Most of our smartphone, daily device or even the internet uses Artificialintelligence. Very often, AI and machine learning are used interchangeably bybig companies that want to announce their latest innovation. However, Machinelearning and AI are different in some ways.AI- artificial intelligence- is the science of training machines to performhuman tasks. The term was invented in the 1950s when scientists beganexploring how computers could solve problems on their own.Artificial Intelligence vs Machine LearningArtificial Intelligence is a computer that is given human-like properties.Take our brain; it works effortlessly and seamlessly to calculate the worldaround us. Artificial Intelligence is the concept that a computer can do thesame. It can be said that AI is the large science that mimics human aptitudes.Machine learning is a distinct subset of AI that trains a machine how tolearn. Machine learning models look for patterns in data and try to conclude.In a nutshell, the machine does not need to be explicitly programmed bypeople. The programmers give some examples, and the computer is going to learnwhat to do from those samples.