O'Reilly - Trends in AI, Data Science, and Big Data (2017)

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O'Reilly - Trends in AI, Data Science, and Big Data (2017)
English | Size: 0.99 GB
Category: Tutorial

In this video, O’Reilly’s Chief Data Scientist, Ben Lorica highlights some recent research initiatives and trends in data from both the AI community and the big data/data science world. Topics include: the emergence of deep learning as a general-purpose machine learning technique; strategies for overcoming the main bottlenecks in running successful AI/machine learning projects (i.e., lack of training data and deploying/monitoring models in production); the transition from offline to continuous learning (including reinforcement learning); and the emerging software and hardware infrastructure for AI and machine learning. This is a must-view for every data scientist, data architect/engineer, data/business analyst, and manager or CxO who wants to stay current in the rapidly evolving world of big data, data science, and AI.
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O'Reilly - Supervised Classification Algorithms

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O'Reilly - Supervised Classification Algorithms
English | Size: 497.49 MB
Category: Tutorial

Classification is the sub-field of machine learning encountered more frequently than any other in data science applications. There are many different classification techniques and this course explains some of the most important ones, including algorithms such as logistic regression, k-nearest neighbors (k-NN), decision trees, ensemble models like random forests, and support vector machines. The course also covers Naive Bayes classifiers and in so doing, covers Bayes' theorem and basic Bayesian inference, both of which are widely used in training many machine learning algorithms. A basic knowledge of algebra is required. A solid understanding of differential calculus will be necessary for logistic regression, Support Vector Machines and Bayesian Inference.
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O'Reilly - Writing User Stories

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O'Reilly - Writing User Stories
English | Size: 1.08 GB
Category: Tutorial

The art of user story writing is a foundational element of the product manager’s skill set. In this course, you’ll gain hands-on experience writing clear and concise user stories that effectively communicate product requirements to an agile development team. In addition, you’ll practice strategies for obtaining stakeholder buy-ins, prioritizing user stories, and measuring the success of launched features. Familiarity with the agile development processes is helpful but not required.
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O'Reilly - Linear Regression

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O'Reilly - Linear Regression
English | Size: 208.34 MB
Category: Tutorial

Linear regression is one of the most important machine learning tools. It is the simplest of the predictive modeling techniques and it is widely used, whether on its own or in combination with other techniques. This course teaches the principles and practices of linear regression. It reviews the meaning of modeling, explains linear regression's key concepts (e.g., cost function, R-squared metric, etc.), describes the practice and need for hypothesis testing, illustrates how to implement linear regression computationally, and showcases an implementation of ridge regression. An understanding of basic mathematics is required, and some knowledge of linear algebra and differential calculus will allow the viewer to understand all of the subtle details.
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O'Reilly - Clustering and Unsupervised Learning

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O'Reilly - Clustering and Unsupervised Learning
English | Size: 163.25 MB
Category: Tutorioal

This course introduces clustering, a common technique used widely in unsupervised machine learning. The course begins by defining what clustering means through graphical explanations, and describes the common applications of clustering. Next, it explores k-means clustering in detail, including the concepts of distance functions and k-modes; illustrates hierarchical clustering through visual examples of dendrograms, and discusses different types of clustering algorithms. The course ends with a comparison of the performance of different algorithms. An understanding of basic algebra is required and some knowledge of linear algebra will be helpful.
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O'Reilly - Deploying Elastic Cloud Compute (EC2) Instances

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O'Reilly - Deploying Elastic Cloud Compute (EC2) Instances
English | Size: 0.99 GB
Category: Tutorial

This course examines a key process for AWS: The process of deploying a basic EC2 instance using either Linux or ShiChuang. It demonstrates how to connect to those instances, explains how to manage the underlying images and snapshots, and compares EC2 to other AWS services like Elastic Beanstalk and Lightsail. Learners should understand how to use SSH (secure shell) and have access to an AWS account (i.e., capable of deploying EC2 instances and working with security groups, key pairs, snapshots, and billing).
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O'Reilly - Recommendation System

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O'Reilly - Recommendation System
English | Size: 327.51 MB
Category: Tutorial

Recommendation systems are a class of machine learning models with many applications. The idea behind recommendation systems is simple: filtering information to suggest items (anything from clothes to films) to users with the predicted probability that the users will enjoy such items. This course provides an introduction to recommendation systems. It starts by looking at the applications for these systems with a focus on the big companies whose fortune is built upon them. It then goes through a discussion of the different types of recommendation systems and how to implement them. You'll explore non-personalized systems, association rule learning, collaborative filtering, personalized systems, and the methods used to assess the quality (i.e., how good are the recommendations?) of a recommendation system. Learners should understand basic logic, supervised learning, and statistics.
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O'Reilly - Neural Networks

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O'Reilly - Neural Networks
English | Size: 229.32 MB
Category: Tutorial

Neural networks form the foundation for deep learning, the most advanced and popular machine learning technique in use today. This course provides an introduction to neural networks. It begins with an overview of a neural network's basic concepts and building blocks - neurons, weights, activations, and layers - before explaining how to train one using gradient descent. The optimization technique is explained with a visual example and different issues such as parameter initialization and model validation are discussed. The course covers the different types of neural network architectures, explains the differences between them, and illustrates practical applications for each. Because training a neural network can be very slow, the course will offer up some tricks for speeding up the process and improving results. The course ends with a review of the history of this fascinating field, from its origin to its fall, and then its subsequent rise in modern days. Requirements include a clear understanding of supervised learning and optimization.
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O'Reilly - Dealing With Real-World Data

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O'Reilly - Dealing With Real-World Data
English | Size: 217.11 MB
Category: Tutorial

This course covers a subject central to the practice of data science and machine learning: the tricky and often overlooked problem of how to deal with real-world data. It provides an overview of the things data scientists think about when gaining access to a data set. You'll learn about data types, data exploration, the curse of dimensionality, PCA, model evaluation, and more, in this pragmatic introduction to the terminology and concepts surrounding data and machine learning. Learners with a basic working knowledge of mathematics will be able to enjoy the course and immediately start working on machine learning problems.
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O’Reilly - Reinforcement Learning (RL) in Python

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O’Reilly - Reinforcement Learning (RL) in Python
English | Size: 428.53 MB
Category: Tutorial

Understanding the Machine Learning Approach that Drives AlphaGo
In March 2016, the Google DeepMind program called AlphaGo, beat eighteen-time world champion Lee Sedol in a five-game Go match. Reinforcement learning was integral to AlphaGo's win.
In this course, you'll delve into the fascinating world of reinforcement learning to see how this machine learning approach actually works. You'll learn what reinforcement learning is, how it's used to optimize decision making over time, and how it solves problems in games, advertising, and stock trading.
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