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Not hard and fast but you have basic knowledge of math, exposure to computer and internet. Computer knowledge is required to run programming languages and math for building algorithms.Course Duration: 6.5 Hours
Course Instructor: Sanjeev Kumar
Course Name: An Introduction to Machine Learning
The term machine learning has all sorts of meanings attached to it today, especially after Hollywood’s (and others’) movie studios have gotten into the picture. Films such as Ex Machina have tantalized the imaginations of moviegoers the world over and made machine learning into all sorts of things that it really isn’t. Of course, most of us have to live in the real world, where machine learning actually does perform an incredible array of tasks that have nothing to do with androids that can pass the Turing Test (fooling their makers into believing they’re human). An Introduction to Machine Learning provides you with a view of machine learning in the real world and exposes you to the amazing feats you really can perform using this technology. Even though the tasks that you perform using machine learning may seem a bit mundane when compared to the movie version, by the time you finish this video lectures, you realize that these mundane tasks have the power to impact the lives of everyone on the planet in nearly every aspect of their daily lives. In short, machine learning is an incredible technology — just not in the way that some people have imagined.
Why do use Machine Learning?
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real-time – organizations are able to work more efficiently or gain an advantage over competitors. The Different Industries which are engaging heavily for development and deployment of Machine Learning are:
Financial ServicesHealth CareOil & GasGovernmentRetailTransportation
What we can do with Machine Learning?
Using algorithms to build models that uncover connections, organizations can make better decisions without human intervention.
Opportunities for machine learning in businessApplying machine learning to IoTRobot locomotionMedical diagnosisSearch enginesTelecommunicationGeneral game playing
These are some few applications example and numerous others have not listed.
PART 1: INTRODUCING HOW MACHINES LEARN.
Getting the Real Story about AILearning in the Age of Big DataHaving a Glance at the Future
PART 2: PREPARING YOUR LEARNING TOOLS
Installing an R DistributionCoding in R Using RStudioInstalling a Python DistributionCoding in Python Using AnacondaExploring Other Machine Learning Tools
PART 3: GETTING STARTED WITH THE MATH BASICS
Demystifying the Math behindMachine LearningDescending the Right CurveValidating Machine LearningStarting with Simple Learners
PART 4: LEARNING FROM SMART AND BIG DATA
Preprocessing DataLeveraging SimilarityWorking with Linear Models the Easy WayHitting Complexity with Neural NetworksGoing a Step beyond Using SupportVector MachinesResorting to Ensembles of Learners
PART 5: APPLYING LEARNING TO REAL PROBLEMS
Classifying ImagesScoring Opinions and SentimentsRecommending Products and Movies
PART 6: THE PART OF TENS
Ten Machine Learning Packages to MasterTen Ways to Improve Your MachineLearning ModelsWho this course is for:
Every one whom are interested in making things better understable, usable, and reliable.
It’s a beginner course and believe that learned students will benefited maximally and able to concrete concepts for their future endeavor.