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

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
• Content From: https://www.udemy.com/course/deep-learning-machine-learning-practical/