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If you’re looking for free online resources to learn more about deep learning and how to develop AI, these free deep learning courses can help beginners. These free courses were professionally produced and trusted by thousands of people who wanted to study deep learning on Udemy, Coursera, edX, and Pluralsight. The great thing about today’s world is that online courses in Artificial Intelligence, Machine Learning, and Deep Learning are growing day by day, and they not only cover the artificial intelligence, but also its subsets such as machine learning and deep learning.
Deep Learning Basics (courtesy of Udemy)
Deep learning is a branch of artificial intelligence that involves building networks of neurons that mimic the human brain to tackle complex tasks such as recognizing faces and objects. This course will teach you the basics of this subject without any prior knowledge required. Starting with the basics and essential ideas of this discipline known as deep learning, you will take hands-on courses in which you will learn how to improve deep learning models such as accuracy.
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Neural Networks and Deep Learning (offered by Coursera)
This is perhaps one of the best known and most successful deep learning courses available on the Coursera platform. This course has over 900,000 students enrolled, which is ridiculous, and 32% of participants report having started a new profession after completing it. This course is part of Andrew Ng’s deep learning specialization on Coursera, which he and his company DeepLearning.ai created. It paves the way for you to gain the insights and capabilities you’ll need to apply machine learning to your work, advance your technical career, and take the first step into the world of AI. This course begins with the creation of basic neural networks and the knowledge of their essential parameters. After that, you will try out various methods such as adjusting hyperparameters to improve the performance of neural networks and how to identify faults in machine learning systems. You will also learn how to create a convolutional neural network for object recognition and how to work with text entry.
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Applied Deep Learning: Creating a Chatbot – Theory, Application (offered by Udemy)
On Udemy, there is another fantastic free and hands-on deep learning course. This 6 hour course is comparable to any paid course and can be used to understand how Chatbots work and how to program them in Python and PyTorch. This course will show you how to use deep learning to solve problems. Since this is an intermediate level course, you should have a basic understanding of deep learning and neural networks.
This is one of the free deep learning courses that will teach you the following:
- Find out how sequence modeling works.
- Find out how chatbots work from the ground up.
- Find out how RNNs and LSTMs work in theory.
- Learn PyTorch from scratch.
- How to use PyTorch to create a chatbot
- Understand the principles behind various sequence modeling applications.
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Intensive deep learning course for beginners (offered by the FreeCodeCamp YouTube channel)
On the FreeCodeCamp YouTube channel, there is a nice little free crash course in deep learning. If you are new to deep learning, this course will give you an overview of what deep learning is and how you can use it to solve real world problems. This course was created by Jason Dsouza, and it will teach you the basics of deep learning without requiring you to write any code. You will master the essential ideas and vocabulary of deep learning, a sub-branch of machine learning, in this course, intended for absolute novices without any programming expertise.
This crash course in deep learning covers the following topics:
What is deep learning and how does it work?
- Neural networks: an overview
- What is the learning process for neural networks?
- How are basic terminologies used in deep learning?
- Activation functions, loss functions and optimizers
- Supervised, unsupervised and reinforcement learning
- Fully connected direct-acting neural networks, recurrent neural networks and convolutional neural networks.
Data Science: An Introduction to Deep Learning with Python in 2021 (offered by Udemy)
This is a beginner-level Python course for building deep learning algorithms. This 2 hour session is ideal for beginners who want to learn Python programming and deep learning.
This course will teach you the following:
- Understand what artificial neural networks are and how they work.
- How to Create Artificial Neural Networks Using Tensorflow
- How deep learning can be used to classify images and data
- How to integrate convolutional neural networks into your daily routine.
In 2021, more than 2,500 students have already registered for this course to master deep learning using Python.
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Amazing AI: Music Editing with Deep Learning (provided by Udemy)
On Udemy, there is another great course to learn more about AI and deep learning. This is a short course with only 30 minutes of information, but it is fascinating and valuable.
This course will teach you the following:
- How to split music into multiple songs using Spleeter library?
- How to edit music using deep learning?
- How to develop in Python with Google Collab?
This course has already attracted over 6,000 students, and it now has a grade of 4.7 which is excellent.
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Deep Learning Specialization (Offered by Coursera)
With Andrew Ng himself teaching the subject, this is certainly one of the most sought after deep learning certificates. This certification course will teach you the basics of deep learning, how to build neural networks, and how to manage machine learning projects. Real-time case studies will include, among others, sign language reading, musical creation and natural language processing. In addition to the theory, you will learn how to put everything together with Python and TensorFlow.
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Machine learning with Python (offered by edX)
Machine learning, like deep learning, is a subset of artificial intelligence that works using built-in algorithms that can learn from experience. This IBM-created course will teach you how to use the Python programming language to learn more about this area. You will start by studying this science and its many models, including supervised and unsupervised learning. After that, you will learn about linear regressions and classification techniques such as decision trees and logistic regression. After that, you will learn about unsupervised learning and how to create a recommendation system.
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