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: 1. Financial Services 2. Health Care 3. Oil & Gas 4. Government 5. Retail 6. Transportation What we can do with Machine Learning? Using algorithms to build models that uncover connections, organizations can make better decisions without human intervention. 1. Opportunities for machine learning in business 2. Applying machine learning to IoT 3. Robot locomotion 4. Medical diagnosis 5. Search engines 6. Telecommunication 7. General game playing These are some few applications example and numerous others have not listed. Course content PART 1: INTRODUCING HOW MACHINES LEARN. Getting the Real Story about AI Learning in the Age of Big Data Having a Glance at the Future PART 2: PREPARING YOUR LEARNING TOOLS Installing an R Distribution Coding in R Using RStudio Installing a Python Distribution Coding in Python Using Anaconda Exploring Other Machine Learning Tools PART 3: GETTING STARTED WITH THE MATH BASICS Demystifying the Math behindMachine Learning Descending the Right Curve Validating Machine Learning Starting with Simple Learners PART 4: LEARNING FROM SMART AND BIG DATA Preprocessing Data Leveraging Similarity Working with Linear Models the Easy Way Hitting Complexity with Neural Networks Going a Step beyond Using SupportVector Machines Resorting to Ensembles of Learners PART 5: APPLYING LEARNING TO REAL PROBLEMS Classifying Images Scoring Opinions and Sentiments Recommending Products and Movies PART 6: THE PART OF TENS Ten Machine Learning Packages to Master Ten Ways to Improve Your MachineLearning Models

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