Machine Learning Practical Workout | 8 Real-World Projects

机器学习是对算法的研究，可指导计算机从经验中学习。通过经验（即：更多的培训数据），计算机可以不断提高其性能。深度学习是利用多层人工神经网络的机器学习的子集。深度学习受到人脑的启发，并模仿生物神经元的操作。通过以分层方式连接多个人工神经元，形成了一个层次深层的人工神经网络。添加到网络中的隐藏层越多，网络将越“深”，可以建模的非线性关系就越复杂。深度学习广泛用于自动驾驶汽车，面部和语音识别以及医疗保健应用中。

本课程的目的是通过实用，轻松和有趣的方式为学生提供有关深度和机器学习技术的关键方面的知识。该课程为学生提供使用实际数据集训练深度和机器学习模型的实践经验。本课程以实用的方式涵盖了几种技术，项目包括但不限于：

（1）训练深度学习技术来执行图像分类任务。

（2）使用最先进的Facebook先知时间序列开发预测模型，以预测诸如未来商品价格之类的未来事件。

（3）开发自然语言处理模型以分析客户评论并识别垃圾邮件/火腿邮件。

（4）开发推荐器系统，例如Amazon和Netflix电影推荐器系统。

本课程面向希望对深度学习和机器学习模型有基本了解的学生。推荐编程的基础知识。但是，这些主题将在早期课程中广泛涉及；因此，该课程没有任何先决条件，并且对具有基础编程知识的任何学生开放。谁在这个过程中招收学生将掌握深和机器学习模型，能够直接应用这些技能来解决现实世界的挑战性的问题。”

New | h264, yuv420p, 1280×720 |ENGLISH, aac, 48000 Hz, stereo | 14h 13 mn | 6.67 GB
Created by: Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard

Build 8 Practical Projects and Go from Zero to Hero in Deep/Machine Learning, Artificial Neural Networks

What you’ll learn

Deep Learning Practical Applications
Machine Learning Practical Applications
How to use ARTIFICIAL NEURAL NETWORKS to predict car sales
How to use DEEP NEURAL NETWORKS for image classification
How to use LE-NET DEEP NETWORK to classify Traffic Signs
How to apply TRANSFER LEARNING for CNN image classification
How to use PROPHET TIME SERIES to predict crime
How to use PROPHET TIME SERIES to predict market conditions
How to develop NATURAL LANGUAGE PROCESSING MODEL to analyze Reviews
How to apply NATURAL LANGUAGE PROCESSING to develop spam filder
How to use USER-BASED COLLABORATIVE FILTERING to develop recommender system

Requirements

Deep Learning and Machine Learning basics
PC with Internet connetion

Description

“Deep Learning and Machine Learning are one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Machine/Deep Learning techniques are widely used in several sectors nowadays such as banking, healthcare, transportation and technology.

Machine learning is the study of algorithms that teach computers to learn from experience. Through experience (i.e.: more training data), computers can continuously improve their performance. Deep Learning is a subset of Machine learning that utilizes multi-layer Artificial Neural Networks. Deep Learning is inspired by the human brain and mimics the operation of biological neurons. A hierarchical, deep artificial neural network is formed by connecting multiple artificial neurons in a layered fashion. The more hidden layers added to the network, the more “deep” the network will be, the more complex nonlinear relationships that can be modeled. Deep learning is widely used in self-driving cars, face and speech recognition, and healthcare applications.

The purpose of this course is to provide students with knowledge of key aspects of deep and machine learning techniques in a practical, easy and fun way. The course provides students with practical hands-on experience in training deep and machine learning models using real-world dataset. This course covers several technique in a practical manner, the projects include but not limited to:

(1) Train Deep Learning techniques to perform image classification tasks.

(2) Develop prediction models to forecast future events such as future commodity prices using state of the art Facebook Prophet Time series.

(3) Develop Natural Language Processing Models to analyze customer reviews and identify spam/ham messages.

(4) Develop recommender systems such as Amazon and Netflix movie recommender systems.

The course is targeted towards students wanting to gain a fundamental understanding of Deep and machine learning models. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master deep and machine learning models and can directly apply these skills to solve real world challenging problems.”
Who this course is for:

Data Scientists who want to apply their knowledge on Real World Case Studies
Deep Learning practitioners who want to get more Practical Assigmetns
Machine Learning Enthusiasts who look to add more projects to their Portfolio