Machine Learning Practical Workout | 8 Real-World Projects

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Machine Learning Practical Workout 8 Real-World Projects Udemy coupons

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

Created by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard | 14 hours on-demand video course

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.

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

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