In this Course you learn k-Nearest Neighbors & Naive Bayes Classification Methods. In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. The k-NN algorithm is among the simplest of all machine learning algorithms. For classification, a useful technique can be to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. The neighbors are taken from a set of objects for which the class (for k-NN classification).

Neural Networks are at the forefront of almost all recent major technology breakthroughs. The intersection of big data, parallel programming, and AI generated a new wave of Neural Network research. In this course, you will be taken through some of the best uses of Neural Networks using TensorFlow. You'll explore Deep Reinforcement Learning algorithms such as Generative Networks and Deep Q Learning. You will learn to implement some more complex types of neural networks such as Deep Q Learning with OpenAI Gym, autoencoders, and Siamese neural networks.

Welcome to Managing Big Data on Google's Cloud Platform. 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 course on Big Data.

Data Scientists with Natural Language Processing & Text Mining Skills are the Hottest and Most In-Demand Job Applicants Today! Data Scientist was recently dubbed "The Sexiest Job of the 21st Century" by Harvard Business Review, Glassdoor reports that Data Scientist was named the "Best Job in America for 2016," and business media from Forbes to The New York Times frequently report about the increasing demand for data scientists. Most of this boom is using data that is organized and structured from your databases and spreadsheets but a huge opportunity awaits from the untapped unstructured text data (aka tweets, Facebook posts, blog posts, comments, SMS, chats, voice transcripts, etc.). Within the data science field, natural language processing is an extremely hot area in academia, startups and is just being started to be used widely within the mainstream of corporate America. Data Scientist job posting with natural language processing skills roughly doubled in 2016.

In this course, you will learn what hyperparameters are, what Genetic Algorithm is, and what hyperparameter optimization is. In this course, you will apply Genetic Algorithm to optimize the performance of Support Vector Machines and Multilayer Perceptron Neural Networks. Hyperparameter optimization will be done on a regression dataset for the prediction of cooling and heating loads of buildings. The SVM and MLP will be applied on the dataset without optimization and compare their results to after their optimization. By the end of this course, you will have learnt how to code Genetic Algorithm in Python and how to optimize your Machine Learning algorithms for maximal performance.

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.

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.

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.

Created by industry thought leader Ajit Jaokar in 1999, our research is based on two courses conducted by Ajit: Big Data for Telecoms at Oxford University and the newly launched citysciences program program at UPM ( Technical University of Madrid) which apply machine learning techniques to IoT and Smart cities applications. "Great course with many interactions, either group or one to one that helps in the learning. In addition, tailored curriculum to the need of each student and interaction with companies involved in this field makes it even more impactful. As for myself, it allowed me to go into topics of interests that help me in reshaping my career. "This DSIOT course is a great way to get up-to-speed.