I'm On A Quest To Give You The Education You'll Never Get In School. The things that the world's highest achievers spent their entire lives discovering, that no professor or teacher will ever tell you. Because when I was in college, I was mad. I'd just read a book and everything inside was the opposite of what I was learning in all my classes. So I ran into the dean's office and said "I'm literally learning more from the books I get on Amazon for five bucks than these classes that cost thousands of dollars each!" And all she had to tell me is...they're working on it!

In this course, we will teach you advanced techniques in machine learning with the latest code in R. Now is the time to take control of your data and start producing superior statistical analysis with R. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning and more. This course starts with teaching you how to set up the R environment, which includes installing RStudio and R packages. This course aims to excite you with awesome projects focused on analysis, visualization, and machine learning. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, and more.

This course is about the fundamental concepts of machine learning, focusing on neural networks, SVM and decision trees. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very very good guess about stock prices movement in the market. In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. The first chapter is about regression: very easy yet very powerful and widely used machine learning technique.

Andrew Ng, one of the founders of Coursera, has set out to raise a $150 million fund – dubbed AI Fund – in order to invest in artificial intelligence startups. The news comes just a few months after he announced his own startup, deeplearning.ai. The fund's existence was revealed because of a filing with the US Securities and Exchange Commission (SEC). The document filed with the SEC was filed under Andrew Ng's name on 14 August. At the end of June, we reported that Ng had left the Chinese company, Baidu, where he was in charge of the AI team to form his new startup, deeplearning.ai.

Bharani Kumar is an Alumnus of premier institutions like IIT & ISB with 15 years professional experience and worked in various MNCs such as HSBC, ITC, Infosys, Deloitte in various capacities such as Data Scientist, Project Manager, Service Delivery Manager, Process Consultant, Delivery Head etc. He has trained over 1500 professionals across the globe on Business Analytics, Agile, PMP, Lean Six Sigma, Business analytics and the likes. He has 8 years of extensive experience in corporate, open house and online training. He is a thorough implementer with abilities in Business Analytics and Agile projects. He worked in Delivery management focusing on maximizing business value articulation.

Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming the ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, and many more. Feel free to contact him on LinkedIn for more information on in-person training sessions.

What is holding you back from learning faster? Is it your overall learning system? You've probably heard it before: "a bad craftsman blames his tools." But when is the last time you saw someone building a house with a hammer, a hand saw and some 2x4s? When you build a house, you need the right tools and materials to build a house.

What is machine learning / ai? How to lean machine learning in python? Good questions here is a point to start searching for an answer. In the world of today and especially tomorrow machine learning will be the driving force of the economy. No matter who you are, an entrepreneur or an employee, machine learning will be on your agenda.

TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. It will not only help you discover what TensorFlow is and how to use it, but will also show you the unbelievable things that can be done in machine learning with the help of examples/real-world use cases. We start off with the basic installation of Tensorflow, moving on to covering the unique features of the library such as Data Flow Graphs, training, and visualization of performance with TensorBoard--all within an example-rich context using problems from multiple sources.. The focus is on introducing new concepts through problems that are coded and solved over the course of each section.

This course explores several data science and machine learning techniques that every data science practitioner should be familiar with. This course explores the fundamental concepts in these big four topics, and provides the student with an overview of the problems that can be solved nowadays. I only focus on the computational and practical implications of these techniques, and it is assumed that the student is partially familiar with Statistics-ML-Data Science - or is willing to complement the techniques presented here with theoretical material. The teaching strategy is to briefly explain the theory behind these techniques, show how these techniques work in very simple problems, and finally present the student with some real examples. I believe that these real examples add an enormous value to the student, as it helps understand why these techniques are so used nowadays (because they solve real problems!)