You'll start by absorbing the most valuable PyTorch basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python-based data science in real -life. After taking this course, you'll easily use packages like Numpy, Pandas, and PIL to work with real data in Python along with gaining fluency in PyTorch.

In industries from healthcare to education to finance to manufacturing, quarantine and extended work-from-home forced companies to use technology to reimagine nearly every facet of their operations. As the world reopens in fits and starts, we analyze the industries poised to thrive in a post-Covid world. As the Covid-19 pandemic has charted its unprecedented path around the world, it's carried with it the question: What will Covid-19's legacy be? From healthcare to education to entertainment to manufacturing, technology innovators are stepping forward to help answer that question. "Crisis can be… a catalyst or can speed up changes that are on the way -- it almost can serve as an accelerant." In the wake of the outbreak, everything from doctors appointments to schooling to workouts went online. As more people have worked, learned, banked, exercised, relaxed, and even sought medical care from home during Covid-19, they have gotten a crash course in just how much can be accomplished at ...

But, at last, it is up to you, which cloud platform do you want to learn? But, don't forget to get the proper knowledge about the cloud platform

This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you. You already learned about backpropagation, but there were a lot of unanswered questions.

Welcome to Machine Learning with Imbalanced Datasets. In this course, you will learn multiple techniques which you can use with imbalanced datasets to improve the performance of your machine learning models. If you are working with imbalanced datasets right now and want to improve the performance of your models, or you simply want to learn more about how to tackle data imbalance, this course will show you how. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about working with imbalanced datasets. Throughout this comprehensive course, we cover almost every available methodology to work with imbalanced datasets, discussing their logic, their implementation in Python, their advantages and shortcomings, and the considerations to have when using the technique.

Probability for improved business decisions: Introduction, Combinatorics, Bayesian Inference, Distributions. Welcome to Probability and Statistics for Business and Data Science! In this course we cover what you need to know about probability and statistics to succeed in business and the data science field! This practical course will go over theory and implementation of statistics to real world problems. Each section has example problems, in course quizzes, and assessment tests.

PyTorch is one of the most used libraries for building deep learning models, especially neural network-based models. In many tasks related to deep learning, we find the use of PyTorch because of its features and capabilities like production-ready, distributed training, robust ecosystem, and cloud support. In this article, we will learn how we can build a simple neural network using the PyTorch library in just a few steps. For this purpose, we will demonstrate a hands-on implementation where we will build a simple neural network for a classification problem. Let's start with the first step, where we will create a dataset for implementation.

This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. We will guide you through complex topics in a practical way, and reinforce learning with in-depth labs and quizzes. This is the tutorial you've been looking for to become a modern JavaScript machine learning master in 2020. It doesn't just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on you resume.

「お父さんAIスケッチ」で使われている、TensorFlowとPython3で画像生成（GAN：Generative Adversarial Network。敵対的生成ネットワーク）に挑戦。TensorFlowによる開発手順も確認できます。

Data Science & Deep Learning for Business 20 Case Studies - Use Python to solve problems in Retail, Marketing, Product Recommendation, Customer Clustering, NLP, Forecasting & more! Machine Learning from Linear Regressions (polynomial & multivariate), K-NNs, Logistic Regressions, SVMs, Decision Trees & Random Forests Unsupervised Machine Learning with K-Means, Mean-Shift, DBSCAN, EM with GMMs, PCA and t-SNE Build a Product Recommendation Tool using collaborative & item/content based Hypothesis Testing and A/B Testing - Understand t-tests and p values Natural Langauge Processing - Summarize Reviews, Sentiment Analysis on Airline Tweets & Spam Detection To use Google Colab's iPython notebooks for fast, relaible cloud based data science work Deploy your Machine Learning Models on the cloud using AWS Advanced Pandas techniques from Vectorizing to Parallel Processsng Statistical Theory, Probability Theory, Distributions, Exploratory Data Analysis Predicting Employee Churn, Insurance Premiums, Airbnb prices, credit card fraud and who to target for donations Big Data skills using PySpark for Data Manipulation and Machine Learning Cluster customers based on Exploratory Data Analysis, then using K-Means to detect customer segments Build a Stock Trading Bot using re-inforement learning Apply Data Science & Analytics to Retail, performing segementation, analyzing trends, determining valuable customers and more! To use Google Colab's iPython notebooks for fast, relaible cloud based data science work Welcome to the course on Data Science & Deep Learning for Business 20 Case Studies! This course teaches you how Data Science & Deep Learning can be used to solve real-world business problems and how you can apply these techniques to 20 real-world case studies. Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade!