In these free video courses, our PD team will teach you the basics of programming and teaching with the Hummingbird Bit Robotics Kit or the Finch Robot 2.0. These courses will also show you activities to inspire your own classroom integration and walk you through our free online resources. Each video course will take you approximately 2-3 hours to complete.

Add SQLMaestros to your address book. Spotlight: Sessions announced for Azure Analytics & Artificial Intelligence Virtual Symposium. Here is the SQLMaestros Bulletin of 06 October, 2020. Join Us General Sessions Part-2 announced for Data Platform Virtual Summit 2020. Session 1: Real-Life Machine Learning projects – advanced linear regression considerations Session 2: Synapse and Power BI better together Session 3: Getting Started with Azure Synapse Analytics Session 4: Azure Digital Twins in a Nutshell Session 5: Building Analytics Solutions Faster with Azure Synapse Analytics Session 6: How does Azure Cosmos DB work under the hood?

In this article, I will present my favorite top-10 Machine Learning YouTube Channels to follow in order to keep up with the current trends. Jeremy Howard is an Australian data scientist and entrepreneur. He is a founding researcher at fast.ai, a research institute dedicated to make Deep Learning more accessible. Prior to it, Howard was the President and Chief Scientist at Kaggle. Another useful YouTube Channel is that of Rachel Thomas, co-founder of fast.ai.

Statistical approaches to processing natural language text have become dominant during the recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. This course explains why predictive analytics projects are ultimately classification problems, and how data scientists can choose the right strategy for their projects. This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience.

Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.

This video course is a practical guide for developers who want to get started with building computer vision applications using Python 3. The video is divided into six sections: Throughout this video course, three image processing libraries: Pillow, Scikit-Image, and OpenCV are used to implement different computer vision algorithms. The course will help you build Computer Vision applications that are capable of working in real-world scenarios effectively. Some of the applications that we look at in the course are Optical Character Recognition, Object Tracking and building a Computer Vision as a Service platform that works over the internet. Saurabh Kapur is a computer science student at Indraprastha Institute of Information Technology, Delhi. His interests are in computer vision, numerical analysis, and algorithm design.

This video course presents deep learning architectures coded in Python using Keras, a modular neural network library that runs on top of either Google's TensorFlow or Lisa Lab's Theano backends. This video course introduces Generative Adversarial Networks (GANs) that are used to reproduce synthetic data that looks like data generated by humans, and then teach how to forge the MNIST and CIFAR-10 dataset with the help of Keras Adversarial GANs. Practical applications include code for predicting the surrounding words given the current word, sentiment analysis, and synthetic generation of texts. We will learn about a specific form of word embedding word2vec. This embedding has proven more effective and has been widely adopted in the deep learning and NLP communities.

This video course will demonstrate the steps for analyzing unstructured data with the R/R Studio software. The approaches will be illustrated using practical applications for business, healthcare, and retail data, among others. At the end the video course you will have mastered obtaining and visualizing data with R. You will also be confident with data cleaning, preparation, and sentiment analysis with R. Dr. Bharatendra Rai is a professor of Business Statistics and Operations Management in the Charlton College of Business at UMass Dartmouth. He received his Ph.D. in Industrial Engineering from Wayne State University, Detroit.

Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models and TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. This course is your guide to exploring the possibilities with deep learning; it will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With this video course, you will dig your teeth deeper into the hidden layers of abstraction using raw data.