Machine Learning Practical Workout | 8 Real-World Projects – Course Site
Machine Learning Practical Workout | 8 Real-World Projects – Course Site
Build 8 Practical Projects and Go from Zero to Hero in Deep/Machine Learning, Artificial Neural Networks
What you’ll learn
Machine Learning Practical Workout | 8 Real-World Projects – Course Site
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
Learn 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
Learn how to use PROPHET TIME SERIES to predict market conditions
How to develop a NATURAL LANGUAGE PROCESSING MODEL to analyze Reviews
How to apply NATURAL LANGUAGE PROCESSING to develop spam folder
Learn how to use USER-BASED COLLABORATIVE FILTERING to develop a recommender system
Requirements
Deep Learning and Machine Learning basics
PC with an Internet connection
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 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. The more hidden layers added to the network, the more “deep” the network will be, the more complex nonlinear relationships that can be modeled.
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 datasets.
This course covers several techniques 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.
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 Assignments
Machine Learning Enthusiasts who look to add more projects to their Portfolio
Linear Programming for Machine Learning – Course Site
Last updated 2/2020
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