Instructional Material


Dive into Deep Learning with 15 free online courses

@machinelearnbot

This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one -- learning how to get a GPU server online suitable for deep learning -- and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. Prominent review (by Anonymous): "This is really a hidden gem in a field that rapidly growing. Jeremy Howard does an excellent job of both walking through the basics and presenting state of the art results. I was surprised time and again when not only was he presenting material developed within the last year, but even within the week the course was running … You practice on real life data through Kaggle competitions.


Data Science: Learn Machine Learning Without Coding

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One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. The course will teach you those fundamental concepts by implementing practical exercises which are based on live examples.


100% off Data Science: Learn Machine Learning Without Coding course coupon -

@machinelearnbot

One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. A DIFFERENT & MORE EFFECTIVE APPROACH TO LEARNING DATA SCIENCE: In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. WE'LL BUILD SUPERVISED MACHINE LEARNING ALGORITHMS TOGETHER: I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. LEARN BOTH THE THEORY & APPLICATION OF MACHINE LEARNING: The course will teach you those fundamental concepts by implementing practical exercises which are based on live examples.


Keras Tutorial: Deep Learning in Python

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Would you like to take a course on Keras and deep learning in Python? Consider taking DataCamp's Deep Learning in Python course! Also, don't miss our Keras cheat sheet, which shows you the six steps that you need to go through to build neural networks in Python with code examples! Before going deeper into Keras and how you can use it to get started with deep learning in Python, you should probably know a thing or two about neural networks. As you briefly read in the previous section, neural networks found their inspiration and biology, where the term "neural network" can also be used for neurons.


Keras Tutorial: The Ultimate Beginner's Guide to Deep Learning in Python

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In this step-by-step Keras tutorial, you'll learn how to build a convolutional neural network in Python! In fact, we'll be training a classifier for handwritten digits that boasts over 99% accuracy on the famous MNIST dataset. Before we begin, we should note that this guide is geared toward beginners who are interested in applied deep learning. Our goal is to introduce you to one of the most popular and powerful libraries for building neural networks in Python. That means we'll brush over much of the theory and math, but we'll also point you to great resources for learning those.


Azure Machine Learning - Classification Predictive Analysis Using Iris Dataset

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So, in today's tutorial, I shall be demonstrating the classical classification predictive analysis problem using iris dataset in Microsoft Azure Machine Learning Studio. We have used the "Split Data" module to separate our training data from our test data in the provided dataset, so, we can measure the accuracy of our Machine learning model, i.e., we take 75% data as training data and remaining 25% data will be treated as test data. You can experiment with any multiclass classification module as provided in Microsoft Azure Machine Learning Studio or you can import your own multiclass algorithm into the Microsoft Azure Machine Learning Studio. In this tutorial, we learned how to create a machine learning model in Microsoft Azure Machine Learning Studio, how to import our own dataset into Microsoft Azure Machine Learning Studio, different machine learning modules of Microsoft Azure Machine Learning Studio, how to measure the accuracy of our machine learning model, how to generate the evaluation statistics that our model produces, and also, how to use train data and test data from the provided dataset.


How to Prepare Movie Review Data for Sentiment Analysis - Machine Learning Mastery

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We can use the split() function to split the loaded document into tokens separated by white space. We can use the data cleaning and chosen vocabulary to prepare each movie review and save the prepared versions of the reviews ready for modeling. One approach could be to save all the positive reviews in one file and all the negative reviews in another file, with the filtered tokens separated by white space for each review on separate lines. We can then call process_docs() for both the directories of positive and negative reviews, then call save_list() from the previous section to save each list of processed reviews to a file.


playlist?list=PLAwxTw4SYaPl0N6-e1GvyLp5-MUMUjOKo

#artificialintelligence

This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree. The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff. This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS).


Machine Learning:Supervised Learning Part 1a of 3 - YouTube

#artificialintelligence

This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree. The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff. This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS).


Deep Learning with TensorFlow - Welcome

@machinelearnbot

Enroll in the course for free at: https://bigdatauniversity.com/courses... Deep Learning with TensorFlow Introduction The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this TensorFlow course you'll use Google's library to apply deep learning to different data types in order to solve real world problems. Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layer, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple "Hello Word" example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.