If you want to master the principles of neural networks as well as how to develop Deep Learning modules using Keras 2.0, this DataCamp course is for you. This course will provide you with an overview of Deep Learning, which is used to build the most intriguing capabilities in fields such as natural language processing, artificial intelligence, robotics, and image identification. This course is divided into four parts, the first of which, "Basics of Deep Learning and Neural Networks," is completely free to enroll in. Throughout the course, you will receive a hands-on, practical understanding of how to apply deep learning using Keras 2.0, the most recent version of a cutting-edge Python deep learning toolkit. This is one of the best online courses to learn Deep Learning Python.

You'll learn about the underlying principles and terminology used in deep learning, as well as why deep learning approaches are so effective today. You'll create rudimentary neural networks and use them to make predictions. Discover how to optimize the predictions made by your neural networks. Backward propagation, one of the most essential techniques in deep learning, will be used. Understanding how it operates will provide you with a solid basis for the second part of the course. You will develop deep learning models for regression and classification using the Keras package. You'll learn about the Specify-Compile-Fit methodology for making predictions, and by the conclusion of the chapter, you'll have everything you need to create deep neural networks. Learn how to improve your Keras deep learning models. Begin with understanding how to verify your models, then explore the idea of model capacity, and lastly experiment with larger and deeper networks.

Highlights:

• Understand the core principles and terminology used in deep learning, as well as why deep learning approaches are so effective.
• Discover how to improve neural network forecasts using the 'Backward Propagation' approach.
• Learn how to utilize the Keras library to create deep learning models for classification and regression.
• Understand the Specify-Compile-Fit methodology, which is used to produce precise predictions.
• Get ongoing assistance from a team of professionals to answer your Deep Learning and Python-related questions.
  

Duration: 4 hours
Digitaldefynd Rating: 4.5 out of 5

Link to enroll: https://www.datacamp.com/courses/deep-learning-in-python

This course aims to offer a thorough grasp of Deep Learning modules so that you may learn how to create deep neural networks by growing and enhancing the number of training layers for each network. You will first master the fundamentals of calculus before moving on to comprehend backpropagation and its application in neural network training for deep learning. Eder Santana created the course after working with Kernel Machines and Deep Learning for over five years. Throughout the course, he will teach you some of the most sophisticated Deep Learning techniques for building neural networks. This is one of the best online courses to learn Deep Learning Python.

You already know how to design an artificial neural network in Python and have a TensorFlow plug-and-play script. Neural networks are a machine learning mainstay that is always a top competitor in Kaggle competitions. This is the course for you if you want to increase your knowledge of neural networks and deep learning. Backpropagation was been covered, but there were many unsolved questions. How can you boost training speed by modifying it? This course will teach you about batch and stochastic gradient descent, two approaches that allow you to train on a small sample of data at each iteration, considerably reducing training time.

Highlights:

• An introductory course that will teach you the fundamentals of Deep Learning and Neural Networks using Python.
• Discover how to use Theano to demonstrate the primary method of seamless CPU and GPU utilization, as well as how to deploy convolutional neural networks for image processing.
• Study recurrent neural networks and develop a theory that focuses on supervised learning and fits into your product offerings such as Search, Image Recognition, and Object Processing.
• Have confidence in implementing Deep Learning in your present work as well as future research after completing the course.

Duration: 2-3 hours
Digitaldefynd Rating: 3.9 out of 5

Link to enroll: https://www.udemy.com/course/draft/780922/

This course will teach you how to create your first artificial neural network using fundamental deep learning techniques. You will start with the fundamental building blocks to create full-fledged non-linear neural networks using Python and NumPy, and then on to the most important training method — backpropagation. The course is built by Lazy Programmer Inc.'s skilled teachers, who will present you with practical examples throughout the course to help you understand how deep learning can be utilized on anything. After completing the course, you may go on to some of the top deep learning courses and tutorials. Here is one of the best online courses to learn Deep Learning Python.

This course will teach you how to create your FIRST artificial neural network using deep learning techniques. Following on from the last course on logistic regression, use Python and Numpy to create full-fledged non-linear neural networks straight away. This course's contents are completely free. This course is for you if you want to start your road to becoming a deep learning expert, or if you are interested in machine learning and data science in general. Udemy goes beyond fundamental models like logistic regression and linear regression, and I show you something that learns features automatically.

Highlights:

• An advanced course focusing on 'how to create and comprehend' deep neural networks using Python and NumPy.
• Discover how deep learning works and how to create a neural network from scratch with Python, NumPy, and Google TensorFlow.
• Discover the many forms of neural networks and the various sorts of issues that these neural networks can handle.
• Understand neural network terminologies such as activation, backpropagation, and feedforward.
• Enrollment is completely free, and you may study from the convenience of your own home

Duration: 10-11 hours

Digitaldefynd Rating: 4.6 out of 5

Link to enroll: https://www.udemy.com/course/data-science-deep-learning-in-python/

This is one of the greatest Deep Learning courses online, and it is meant to teach you the ideas and techniques for transforming training data into convincing automated predictions. Throughout the course, you will learn about representation, overfitting, regularization, generalization, VC dimension, and many other subjects. This course is part of the MITx MicroMaster Program in Data Science, which means that if you complete it, you will be able to continue on to master more advanced python data science principles. After completing the course, you will receive a certificate of completion, which you may include with your resume or LinkedIn profile to highlight your talents. It is at the top of the best online courses to learn Deep Learning Python.

Understand the underlying concepts of machine learning issues such as classification, regression, clustering, and reinforcement learning. Models such as linear models, kernel machines, neural networks, and graphical models are implemented and analyzed. Select appropriate models for various uses. Implement and manage machine learning projects ranging from training through validation, parameter optimization, and feature engineering. There are many things you can gain after joining the class.

Highlights:

• Gain a fundamental knowledge of the ideas behind machine learning tasks including classification, regression, clustering, and reinforcement learning.
• Understand and apply models such as linear models, kernel machines, neural networks, and graphical models.
• Learn how to implement and manage machine learning projects, including training, validation, parameter optimization, and feature engineering.
• Receive advice and support from a team of expert educators who will aid you in better understanding the topics through hands-on projects and activities.
• Various video lectures, quizzes, and effective learning resources are included to assist you in better comprehending the themes.
  

Duration: Self-paced
Digitaldefynd Rating: 4.5 out of 5

Link to enroll: https://www.edx.org/course/machine-learning-with-python-from-linear-models-to