Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics).

Machine Learning and Predictive Analytics. #MachineLearning

Intro to Predictive Analytics is the second video in this machine learning course. This video explains how machine learning algorithms are used in the field of data analytics to create models of reality.

This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1

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Machine Learning for Engineering and Science Applications – Intro Video

Machine Learning represents a new paradigm in programming, where instead of programming explicit rules in a language such as Java or C++, you build a system which is trained on data to infer the rules itself. But what does ML actually look like? In part one of Machine Learning Zero to Hero, AI Advocate Laurence Moroney (lmoroney@) walks through a basic Hello World example of building an ML model, introducing ideas which we’ll apply in later episodes to a more interesting problem: computer vision.

Try this code out for yourself in the Hello World of Machine Learning → https://goo.gle/2Zp2ZF3

This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish.

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Fallout 4 – Live Action Opening

Colorized using DeOldify [AI Machine Learning] [IMPRESSIVE RESULTS]

DeOldify is an AI Neural Network software that is capable of colorizing black and white images using thousands of pictures as references.

I took the original video file from the Fallout 4 PC disk, and used DeOldify with the Video training model to restore the
color from the FMV.

This is my second attempt at colorizing this video, and the results are much more natural-looking and stable.

EPILEPSY WARNING
—————-
THE COLORIZATION PROCESS INTRODUCES SOME FLICKERING AND COLOR-INSTABILITY.
IF YOU EXPERIENCE DISCOMFORT WATCHING THIS VIDEO, OR
HAVE A HISTORY OF EPILEPSY OR SEIZURES, PLEASE PAUSE
THE VIDEO AND LOWER THE BRIGHTNESS OF YOUR SCREEN.

To begin, what is regression in terms of us using it with machine learning? The goal is to take continuous data, find the equation that best fits the data, and be able forecast out a specific value. With simple linear regression, you are just simply doing this by creating a best fit line.

From here, we can use the equation of that line to forecast out into the future, where the ‘date’ is the x-axis, what the price will be.

A popular use with regression is to predict stock prices. This is done because we are considering the fluidity of price over time, and attempting to forecast the next fluid price in the future using a continuous dataset.

Regression is a form of supervised machine learning, which is where the scientist teaches the machine by showing it features and then showing it was the correct answer is, over and over, to teach the machine. Once the machine is taught, the scientist will usually “test” the machine on some unseen data, where the scientist still knows what the correct answer is, but the machine doesn’t. The machine’s answers are compared to the known answers, and the machine’s accuracy can be measured. If the accuracy is high enough, the scientist may consider actually employing the algorithm in the real world.

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The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.

In this series, we’ll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.

For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we’ll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we’ll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are.

In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. If you have a basic understanding of Python, and the willingness to learn/ask questions, you will be able to follow along here with no issues. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets. Math involved is typically linear algebra, but I will do my best to still explain all of the math. If you are confused/lost/curious about anything, ask in the comments section on YouTube, the community here, or by emailing me. You will also need Scikit-Learn and Pandas installed, along with others that we’ll grab along the way.

Machine learning was defined in 1959 by Arthur Samuel as the “field of study that gives computers the ability to learn without being explicitly programmed.” This means imbuing knowledge to machines without hard-coding it.

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Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics).

Machine Learning and Predictive Analytics. #MachineLearning

Intro to Machine Learning is the first video in this machine learning course. This video explains machine learning vs predictive analytics and how companies are using machine learning platforms and algorithms to develop intelligent software.

This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1

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There are revolutionary changes happening in hardware and software that are democratizing machine learning (ML). Whether you’re new to ML or already an expert, Google Cloud Platform has a variety of tools for users. This session will start with the basics: using a pre-trained ML model with a single API call. It’ll then look at building and training custom models with TensorFlow and Cloud ML Engine, and will end with a demo of AutoML Vision – a new tool for training a custom image classification model without writing model code.

Rate this session by signing-in on the I/O website here → https://goo.gl/4n5aYA

Watch more GCP sessions from I/O ’18 here → https://goo.gl/qw2mR1
See all the sessions from Google I/O ’18 here → https://goo.gl/q1Tr8x

Subscribe to the Google Cloud Platform channel → https://goo.gl/S0AS51

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