Welcome to Serverless Data Analysis with Big Query on Google's Cloud This is the second course in a series of courses designed to help you attain the coveted Google Certified Data Engineer. Additionally, the series of courses is going to show you the role of the data engineer on the Google Cloud Platform. At this juncture the Google Certified Data Engineer is the only real world certification for data and machine learning engineers. Note: This is not a programmers course on BigQuery. The goal of this course and the entire series of courses is to provide students with the foundation of the services you'll need to know for the Google Certified Data Engineering Exam.

Really well explained, and precisely the right amount of information. Mike provides clear and concise explanations and has a deep subject knowledge of Google's Cloud. Welcome to An Introduction to Machine Learning for Data Engineers. This course is part of my series for data engineering. The course is a prerequisite for my course titled Tensorflow on the Google Cloud Platform for Data Engineers.

TensorFlow is an open source software library for Machine Intelligence. The independent solutions in this video course will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through solutions on training models, model evaluation and sentiment analysis – each using Google's machine learning library TensorFlow.This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow and Caesar's Entertainment.

In this course we learn that stand alone data analysis is fine but what most companies these days are looking for is to do Predictive analysis using their data. In this advanced course, we will make you ready to start doing Predictive Analysis on your data by showing you how to build Machine Learning models with scikit-learn and pandas. In this course, you will be training models and be making data based predictions using scikit-learn.The user will like this as a standalone product as Making Predictions data using Machine Learning is an absolute minimum skill for any Data Analyst \ Data Scientist these days. We will teach users how to use scikit-learn to make data based predictions. User will learn how to bring in their data using pandas, apply some machine learning models and take out the predictions.

"I took a few of your courses and you are an amazing teacher. Your courses have brought me up to speed on how to create databases and how to interact and handle Data Engineers and Data Scientists. I will be forever grateful." "By taking this course my perception has changed and now data science for me is more about data wrangling. Welcome to The Complete Course for Machine Learning Engineers.

The mission of this course is to turn you into a productive, innovative data analyst who can leverage Go to build robust and valuable applications. To this end, the course clearly introduces the technical aspects of building predictive models in Go, but also helps you understand how machine learning workflows are applied in real-world scenarios. This course shows you how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives you patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization. You'll begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources.

The aim of this course is to provide a practical understanding of advanced Machine Learning algorithms in Apache Spark to make predictions and recommendation and derive insights from large distributed datasets. This course starts with an introduction to the key concepts and data types that are fundamental to understanding distributed data processing and Machine Learning with Spark. Further to this, we provide practical recipes that demonstrate some of the most popular algorithms in Spark, leading to the creation of sophisticated Machine Learning pipelines and applications. The final sections are dedicated to more advanced use cases for Machine Learning: streaming, Natural Language Processing, and Deep Learning. In each section, we briefly establish the theoretical basis of the topic under discussion and then cement our understanding with practical use cases.

If you're serious about building scalable, flexible and powerful machine learning models in the cloud, then this course is for you. These data science skills are in great demand, but there's no easy way to acquire this knowledge. Rather than rely on hit and trial method, this course will provide you with all the information you need to get started with your machine learning projects. Startups and technology companies pay big bucks for experience and skills in these technologies They demand data science and cloud engineers make sense of their dormant data collected on their servers - and in turn, you can demand top dollar for your abilities. You may be a data science veteran or an enthusiast - if you invest your time and bring an eagerness to learn, we guarantee you real, actionable education at a fraction of the cost you can demand as a data science engineer or a consultant.

Learn to work in an exciting area of computer science and artificial intelligence. In this course we will train an artificial brain to make the game work. No matter where the present falls, the computer will know exactly how get it. Make an AI Christmas game! Our Unity game will have a holiday setting featuring a sled.

Welcome to Data Visualization in Python for Machine learning engineers. This is the third course in a series designed to prepare you for becoming a machine learning engineer. I'll keep this updated and list only the courses that are live. Here is a list of the courses that can be taken right now. Please take them in order.