This team has decades of practical experience in working with Java and with billions of rows of data. If you are an analyst or a data scientist, you're used to having multiple systems for working with data. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code. Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.

In this course, you will learn the basics of Machine Learning and Data Mining; almost everything you need to get started. You will understand what Big Data is and what Data Science and Data Analytics is. You will learn algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Trees, and Neural Networks. You'll also understand how to combine algorithms into ensembles. Preprocessing data will be taught and you will understand how to clean your data, transform it, how to handle categorical features, and how to handle unbalanced data.

The course will mostly focus on helping you implement different statistical analysis techniques on your data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects immediately! TAKE ACTION NOW:) You'll also have my continuous support when you take this course just to make sure you're successful with it. If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you're not completely satisfied with the course.

Whatever be your motivation to learn Computer Vision, I can assure you that you've come to the right course. This course is tailor made for an individual who wishes to transition quickly from an absolute beginner to a Computer Vision expert in a few weeks. The most difficult concepts are explained in plain and simple manner using code examples. I personally guarantee this is the number one course for you. This may not be your first OpenCV course, but trust me - It will definitely be your last. I assure you, that you will receive fast, friendly, responsive support by email, and on the Udemy.

About this course: We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine. Course Objectives: Create machine learning models in TensorFlow Use the TensorFlow libraries to solve numerical problems Troubleshoot and debug common TensorFlow code pitfalls Use tf.estimator to create, train, and evaluate an ML model Train, deploy, and productionalize ML models at scale with Cloud ML Engine

About this course: This course is an introduction into formal concept analysis (FCA), a mathematical theory oriented at applications in knowledge representation, knowledge acquisition, data analysis and visualization. It provides tools for understanding the data by representing it as a hierarchy of concepts or, more exactly, a concept lattice. FCA can help in processing a wide class of data types providing a framework in which various data analysis and knowledge acquisition techniques can be formulated. In this course, we focus on some of these techniques, as well as cover the theoretical foundations and algorithmic issues of FCA. Upon completion of the course, the students will be able to use the mathematical techniques and computational tools of formal concept analysis in their own research projects involving data processing.

Machine Learning Scientist and Apress author of Building Intelligent Systems: A Guide to Machine Learning Engineering, Geoff Hulten, gives an overview of what you need to know when approaching your own applied machine learning project. Intelligent Systems connect machine learning with users to create positive impact for your organization and customers. This webinar introduces an approach to building intelligent systems that has been proven in some of the largest, most important software systems in the world. Geoff covers the five key elements that must be balanced to make your Intelligent System effective and to run it efficiently over its life cycle.

Participants were given a mall as a context and then use machine learning to simulate an aspect of the mall. The idea in the workshop is to open both tech and non-technical people for understanding and apply machine learning.

With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering.

This course is all about A/B testing. A/B testing is used everywhere. A/B testing is all about comparing things. If you're a data scientist, and you want to tell the rest of the company, "logo A is better than logo B", well you can't just say that without proving it using numbers and statistics. Traditional A/B testing has been around for a long time, and it's full of approximations and confusing definitions. In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.