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Machine Learning Practical Workout | 8 Real-World Projects

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

Platform: Udemy
Video: 14h 14m
Language: English
Next start: On Demand

Table of contents

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.”

You will 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

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
Professor & Best-selling Instructor, 250K+ students
Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan’s mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. 
Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Science, Technology, Engineering and Mathematics to over 280,000+ students globally. He has over 25 published journal and conference research papers on state estimation, AI, Machine learning, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. 
Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA.
* McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world.


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Platform: Udemy
Video: 14h 14m
Language: English
Next start: On Demand

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