Machine Learning Practical Workout | 8 Real-World Projects
Machine Learning Practical Workout | 8 Real-World Projects
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
- 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
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."
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