If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

In this 2 hour long project-based course, you will learn how to create an HTML outline of a Plotly Dash dashboard. You will design interactive dropdown lists, radio buttons, and most importantly, scatter plots and bar charts that respond to your mouse's hover. You will learn how to visualize dimensionality reduction results intuitively and interactively, and see how these models can be used in Customer Segmentation. Furthermore, we will discuss how to critically evaluate these models, and what to look out for in a well-performing model. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

By the end of this project, you will learn about the concept of lexical style in textual analysis in R. You will know how to load and pre-process a data set of text documents by converting the data set into a corpus and document feature matrix. You will know how to calculate the type to token ration which evaluates the level of complexity of a text, and know how to isolate terms of particular lexical interest in a text and visualize the variation in frequency of such terms in texts over time.

This is the fifth course in the IBM AI Enterprise Workflow Certification specialization.   You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises.  Apache Spark is a very commonly used framework for running machine learning models.  Best practices for using Spark will be covered in this course.  Best practices for data manipulation, model training, and model tuning will also be covered.  The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies.   By the end of this course you will be able to: 1.  Use Apache Spark's RDDs, dataframes, and a pipeline 2.  Employ spark-submit scripts to interface with Spark environments 3.  Explain how collaborative filtering and content-based filtering work 4.  Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5.  Analyze hyperparameters in machine learning models on Apache Spark 6.  Deploy machine learning algorithms using the Apache Spark machine learning interface 7.  Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

This is a self-paced lab that takes place in the Google Cloud console. This lab focuses on how to reverse-engineer the relationships between data tables and the pitfalls to avoid when joining them together.

SAS Viya is an in-memory distributed environment used to analyze big data quickly and efficiently. In this course, you’ll learn how to use the SAS Viya APIs to take control of SAS Cloud Analytic Services from a Jupyter Notebook using R or Python. You’ll learn to upload data into the cloud, analyze data, and create predictive models with SAS Viya using familiar open source functionality via the SWAT package -- the SAS Scripting Wrapper for Analytics Transfer. You’ll learn how to create both machine learning and deep learning models to tackle a variety of data sets and complex problems. And once SAS Viya has done the heavy lifting, you’ll be able to download data to the client and use native open source syntax to compare results and create graphics.

Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.

In this 2-hour long project-based course, you will learn how to interpret the dataset for machine learning, how different features impact on a mode and how to evaluate them.

This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.

Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will provide an intuitive understanding of foundational integral calculus, including integration by parts, area under a curve, and integral computation. It will also cover root-finding methods, matrix decomposition, and partial derivatives. This course is designed to prepare learners to successfully complete Statistical Modeling for Data Science Application, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program. Logo courtesy of ThisisEngineering RAEng on Unsplash.com