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If you're here, you already know the truth: Machine Learning is the future of everything.

In the coming years, there won't be a single industry in the world untouched by Machine Learning. A transformative force, you can either choose to understand it now, or lose out on a wave of incredible change. You probably already use apps many times each day that rely upon Machine Learning techniques. So why stay in the dark any longer?

There are many courses on Machine Learning already available. I built this course to be the best introduction to the topic. No subject is left untouched, and we never leave any area in the dark. If you take this course, you will be prepared to enter and understand any sub-discipline in the world of Machine Learning.

A short list of what you will learn:

• Advanced memory profiling to enhance the performance of your algorithms
• Build apps powered by the powerful Tensorflow JS library
• Develop programs that work either in the browser or with Node JS
• Write clean, easy to understand ML code, no one-name variables or confusing functions
• Pick up the basics of Linear Algebra so you can dramatically speed up your code with matrix-based operations. (Don't worry, I'll make the math easy!)
• Comprehend how to twist common algorithms to fit your unique use cases
• Plot the results of your analysis using a custom-build graphing library
• Learn performance-enhancing strategies that can be applied to any type of Javascript code
• Data loading techniques, both in the browser and Node JS environments

Node.js is probably THE most popular and modern server-side programming language you can dive into these days!

Node.js developers are in high demand and the language is used for everything from traditional web apps with server-side rendered views over REST APIs all the way up to GraphQL APIs and real-time web services. Not to mention its applications in build workflows for projects of all sizes.

This course will teach you all of that! From scratch with zero prior knowledge assumed. Though if you do bring some knowledge, you'll of course be able to quickly jump into the course modules that are most interesting to you.

Here's what you'll learn in this course:

• Node.js Basics & Basic Core Modules
• Parsing Requests & Sending Responses
• Rendering HTML Dynamically (on the Server)
• Using Express.js
• Working with Files and generating PDFs on the Server (on-the-fly)
• File Up- and Download
• Using the Model-View-Controller (MVC) Pattern
• Using Node.js with SQL (MySQL) and Sequelize
• Using Node.js with NoSQL (MongoDB) and Mongoose
• Working with Sessions & Cookies
• User Authentication and Authorization
• Sending E-Mails
• Validating User Input
• Data Pagination
• Handling Payments with Stripe.js
• Building REST APIs
• Authentication in REST APIs
• File Upload in REST APIs
• Building GraphQL APIs
• Authentication in GraphQL APIs
• File Upload in GraphQL APIs
• Building a Realtime Node.js App with Websockets
• Deploying a Node.js Application
• And Way More!

Does this look like a lot of content? It certainly is!

This is not a short course but it is the "Complete Guide" on Node.js after all. We'll dive into a lot of topics and we'll not just scratch the surface.

We'll also not just walk through boring theory and some slides. Instead, we'll build two major projects: An online shop (including checkout + payments) and a blog.

All topics and features of the course will be shown and used in these projects and you'll therefore learn about them in a realistic environment.

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.