Machine Learning with Neural Networks: 3-in-1

Machine Learning with Neural Networks: 3-in-1

Enhance applications with the power of Machine Learning to build high performing day-to-day apps with Neural Networks.

上次更新 6/2018

英语 [自动]

你将会学到的

Build predictive models in minutes by using scikit-learn

Get an overview of the most well known and powerful tools to solve computing problems using Machine Learning.

Develop efficient and intelligent applications by leveraging the power of Machine Learning

Learn K-means and K-NN clusteringExplore supervised learning algorithms

Plot different shapes using NumPy and Matplotlib, making the visualization more appealing

课程内容

3 个章节 • 47 个讲座 • 总时长 3 小时 17 分钟

要求

Basic knowledge of Anaconda and its libraries such as NumPy and sckit-learn will be useful.

说明

Machine learning is the branch of computer science that has to do with building algorithms guided by data. Machine Learning algorithms use training sets of real-world data to infer models that are more accurate and sophisticated than humans could devise on their own. Neural networks are a subset of algorithms built around a model of artificial neurons spread across three or more layers



This comprehensive 3-in-1 course is a step-by-step tutorial to developing real-world computer vision applications using OpenCV 3 with Python. Program advanced computer vision applications in Python using different features of the OpenCV library. Boost your knowledge of computer vision and image processing by developing real-world projects in OpenCV 3 with Python.



Contents and Overview



This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.



The first course, Getting Started with Machine Learning for Developers, covers basics of Machine Learning to make high performing day-to-day apps. This course introduces you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement regression, clustering, classification, and more, all with fun examples.



The second course, Effective Prediction with Machine Learning - Second Edition, covers programming fast Machine Learning algorithms with NumPy and scikit-learn. This course begins by taking you through videos on evaluating the statistical properties of data and generating synthetic data for machine learning modeling. As you progress through the sections, you will come across videos that will teach you to implement techniques such as data pre-processing, linear regression, logistic regression, and K-NN. You will also look at Pre-Model and Pre-Processing workflows, to help you choose the right models.



The third course, Neural Networks in Machine Learning for Developers, covers enhancing your applications with the power of Machine Learning. You will start with the very basics of neural networks and types. Then we learn about powerful variations in neural networks and Recurrent Neural Networks. Finally, we conclude with a synthetic introduction to more advanced Machine Learning techniques, such as GANs and reinforcement learning.



By the end of the course, you’ll enhance your applications with the power of Machine Learning to build high performing day-to-day apps with Neural Networks.



About the Authors



Julian Avila is a programmer and data scientist in finance and computer vision. He graduated from the Massachusetts Institute of Technology (MIT) in mathematics, where he researched quantum mechanical computation, a field involving physics, math, and computer science. While at MIT, Julian first picked up classical and flamenco guitars, Machine Learning, and artificial intelligence through discussions with friends in the CSAIL lab. He started programming in middle school, including games and geometrically artistic animations. He competed successfully in math and programming and worked for several groups at MIT. Julian has written complete software projects in elegant Python with just-in-time compilation. Some memorable projects of his include a large-scale facial recognition system for videos with neural networks on GPUs, recognizing parts of neurons within pictures, and stock-market trading programs.

此课程面向哪些人：

This course will appeal to any developer who wants to know what Machine Learning is and is keen to use it to make their day-to-day apps fast, high-performing, and accurate. Any developer who wants to enter the field of Machine Learning can effectively use this course as an entry point

This course is ideal for the Machine Learning beginner with some basic Python experience. A sound knowledge of Anaconda and its libraries such as NumPy and scikit-learn is required.