Scikit-learn has evolved as a robust library for Machine Learning applications in Python with support for a wide range of Supervised and Unsupervised Learning Algorithms. With this course you will learn the Decision Tree algorithms and Ensemble Models to build Random Forest, Regression Analysis. You will focus on Decision Trees and Ensemble Algorithms. Moving forward, you learn to use scikit-learn to classify text and Multiclass with scikit-learn. You will explore various algorithms for classification.

You will not regret taking this course. Check out all that you'll learn: First we will install PyCharm 2017.2.3 and explore the interface. I will show you every step of the way. You will learn crucial Python 3.6.2 Even if you have coding knowledge, going back to the basics is the key to success as a programmer.

Learn how to create Artificial Intelligent Agents that have Flocking Behavior and apply them to your projects in games or movies. You have seen Flocking behavior in nature, in games, in movies and in architectural simulations but you might have missed it. Both pseudocode and Unity C# lectures complement each other giving you a full perspective. You will have access to the course forum where you can discuss each topic with like-minded, A.I. passionate, students like yourself. With the help of this course you will be able to understand a piece of nature and replicate it, essentially reverse engineer a piece of nature.

This course prepares participants to begin running data analysis on databases. Both univariate and multivariate analysis are covered with a particular focus on regression analysis. Regression analysis is done in Excel, SAS, and Stata to give viewers a sense of familiarity with a variety of different software package structures. The focus in this course is on financial data though the techniques are also applicable to more general forms of data like that used in marketing or management analyses. If you would like Continuing Education Credit (e.g.

Know the key concepts of Machine Learning. Learn how to create disruptive business models based on Machine Learning. Every few years, there is a technological trend that leads to the creation of thousands of startups and/or new businesses. At present, we can say without any doubt that one of these trends is Machine Learning (Artificial Intelligence). To put it in context, McKinsey (one of the leading Management Consulting companies worldwide) tells us that Tech giants including Baidu and Google are spending between $20B to $30B on AI, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions.

With Core ML, you can integrate trained machine learning models into your apps. In this course, you'll get an an introduction to the Core ML framework. You'll learn how to incorporate Apple's Core ML framework into your app. You'll also get a quick overview of machine learning fundamentals, and exposure to real-world examples of companies using machine learning technology in their iOS apps In this course you'll learn the advantages of using machine learning models, computer vision, and natural language processing in modern apps. In addition, this course walks through the development of sample apps that leverage different machine learning features.

This course will give you a fundamental understanding of Machine Learning overall with a focus on building classification models. Basic ML concepts of ML are explained, including Supervised and Unsupervised Learning; Regression and Classification; and Overfitting. There are 3 lab sections which focus on building classification models using Support Vector Machines, Decision Trees and Random Forests using real data sets. The implementation will be performed using the scikit-learn library for Python. The Intro to ML Classification Models course is meant for developers or data scientists (or anybody else) who knows basic Python programming and wishes to learn about Machine Learning, with a focus on solving the problem of classification.

To help you stay up to date on online training opportunities, we're releasing a monthly list of the latest free data and artificial intelligence (AI) sessions in one convenient post. Whether on Windows, Linux, or Docker containers, you have the flexibility of leveraging SQL Server 2017's industry-leading performance and security wherever you like. Here's a rundown of recent and upcoming training sessions to help you learn more. Extended support for SQL Server 2008 and 2008 R2 is coming to an end on July 9, 2019, which means it's time to choose your path to modernization. Without support, security updates will no longer be available, and you may run the risk of non-compliance with industry regulations such as GDPR (General Data Protection Regulation).

ML has become a fundamental part of the 21st century; from Netflix recommendations to fraud detection, ML is ever- present in our daily lives. At its roots, ML effectively applies statistics and pattern recognition, we will use these ideas to help solve a range of modern-day problems. C is a very fast language to execute your code and is extensively used when your final "models" are being deployed. If you want to run a program, with a lot of array calculation then C should be your weapon of choice. This course will start off with a broad overview of ML and the varying methods associated with it.

Neural networks are a rapidly developing paradigm for information processing based loosely on how neurons in the brain processes information. A neural network consists of multiple layers of node, where each node performs a unit of computation, and passes the result onto the next node. Multiple nodes can pass inputs to a single node, and vice-versa. The neural network also contains a set of weights, which can be refined over time as the network learns from sample data. The weights are used to describe and refine the connection strengths between nodes.