Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great scripting language that can power your applications and provide great speed, safety, and scalability. By exposing Python as a series of simple recipes, you can gain insight into specific language features in a particular context. Having a tangible context helps make the language or standard library features easier to understand. This video comes with over 100 recipes on the latest version of Python.

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.

Do you wish to be a data scientist but don't know where to begin? Want to implement statistics for data science? Want to get acquainted with R programs? Want to learn about the logic involved in computing statistics? If so, then this is the course for you.

Apple recently announced their partnership with IBM to leverage IBM's Watson service to train machine learning models for CoreML. So that mean you now can build apps that leverage Watson machine learning models on iPhone and iPad, even when your device is offline. Your apps can quickly analyze images, accurately classify visual content, and easily train models using Watson Services. With this video series you will learn to onboard with not only pre-trained Watson models but customize and train models that continuously learn over time. In Apple's own words "You can build apps that seamlessly integrate with IBM Cloud using the IBM Cloud Developer Console for Apple. This allows you to quickly tap into Watson Services for Core ML, as well as other IBM cloud services including authentication, data, analytics, and more. The console provides a catalog of starter kits designed for common frameworks that integrate with IBM Cloud."

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do research as experienced investor. Learning stock technical analysis is indispensable for finance careers in areas such as equity research and equity trading. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors stock technical trading research and development. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for back-testing to achieve greater effectiveness.

Apache Spark lets you apply machine learning techniques to data in real time, giving users immediate machine-learning based insights based on what's happening right now. It's used to create machine learning models and programs that are distributed and much faster compared to standard machine learning toolkits such as R or Python. If you're a data professional who is familiar with machine learning and wants to use Apache Spark for developing efficient and fast machine learning systems, then this learning path is for you. This comprehensive 2-in-1 course teaches you to build machine learning systems, perform analytics, and predictions with Apache Spark. You'll learn through practical demonstrations of use cases, clear explanations, and interesting real-world applications. Each section briefly establishes theoretical basis for the topic under discussion and then cement your understanding with practical use cases.

Here I will be posting some of the data science and machine learning projects that I have been working on. The main motivation for making this blog is that I will soon be starting the Fast AI Deep Learning course. Blogging along with the lectures seemed like a great opportunity for me to be really hands-on with the material and get acquainted with other students. Let me first start by giving you a quick background of my journey into data science. About a year ago I started writing my master thesis for the study Business Administration.

This is a very basic introductory course to the fundamentals of the Scala programming language for anyone new to the language. Scala was derived from Java which is one of the top-five programming languages in the world today. It is a versatile and elegant object –oriented programming language. This means it is class based and treats everything as an object. It has a robust security .

In this video from Intel AI DevCon 2018, Andrew Ng from Deeplearning.ai and Landing.ai When you ask Siri for directions, peruse Netflix's recommendations or get a fraud alert from your bank, these interactions are led by computer systems using large amounts of data to predict your needs. The market is only going to grow. By 2020, the research firm IDC predicts that AI will help drive worldwide revenues to over $47 billion, up from $8 billion in 2016. Still, Andrew NG says fears that AI will replace humans are misplaced: "Despite all the hype and excitement about AI, it's still extremely limited today relative to what human intelligence is."

For almost all machine learning projects, the main steps of the ideal solution remains same. For each step, I was doing some research on the web depending on my business object and jotting down the best resources I ran across. The resources include Online Courses, Kernels from Kaggle, Cheat Sheets and Blog Posts. Below I've listed them and categorised by each step (all of the resources are free except the ones that have'paid' in the end):