Best Courses on Neural Networks

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Best Courses on Neural Networks

IBM (International Business Machines) define neural network as artificial neural networks (ANNs) or simulated neural networks (SNNs) which are a subset of machine learning and are at the heart of deep learning algorithms. Neural networking is a part of machine learning and artificial engineering. Therefore, it is an in-demand skill and finds its application in a wide array of fields, including computer science and finance. 

To develop this skill, here is a list of the best online courses on Neural networking.

1. Deep Learning: Recurrent Neural Networks in Python (Udemy)

This course is a 4.6 rated course on the platform of Udemy. This course is provided with twelve-hour long-demand videos that cover basics and builds their way up to some advanced topics. This course a wide range of topics, including machine Learning and Neurons, feedforward Artificial Neural Networks, Recurrent Neural Networks, Time Series, and Sequence Data, Natural Language Processing (NLP), In-Depth: Loss Functions, Gradient Descent, and much more. Apart from learning these topics, you will also get to learn to set up your environment. At the end of the course, you will get a video where your instructor will answer some frequently asked questions. This is a basic course in neural networking but has some pre-requisites that students must meet to understand the course better. They are basic knowledge of mathematics in matrix, derivatives, arithmetic, and probability. Basic knowledge of programming language Python, NumPy, and Matplotlib. Along with 12 hours of demand video, you will get one article and lifetime access to them. You will also get access to the resources on television. You will also receive certification for completion of the course. You can join this course and know more about it using the following link. 

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2. Neural Networks and Deep Learning (Coursera)

This is a 4.9 rated course on the platform of Coursera. This course is developed by Deep learning &AI. This is one part of a 5-course program. This basic course will help you gain knowledge and establish your foundational concept on neural networks and deep learning. This course covers a wide range of topics; some of the important topics are Deep Learning, Artificial Neural networks, backpropagation, Python Programming, and Neural Network Architecture. Apart from this, you will learn to identify key parameters in the architecture of neural networks and apply deep learning to your applications. You will also learn to implement neural networks efficiently. The instructor for this course is Andrew Ng. (co-founder of Deep Learning & AI) and the curriculum of the course has been developed by Kian Katanforoosh and Younes Bensouda Mourri, who are top instructors at Coursera. As an intermediate course, the pre-requisites for this course are intermediate Python skills: basic programming, understanding of loops, if/else statements, data structures, and basic knowledge of linear algebra & ML. You will also receive certification for completion of the course. You can join this course and know more about it using the following link.

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3. Hands-on Artificial Intelligence (edx)

This course is a self-paced course offered by IBM (International Business Machines). This course will help you build your fundamental knowledge of neural networks. You will learn to build, train and deploy deep learning architecture. The course covers a lot of topics; some of them are Pytorch Basics for Machine Learning, Tensorflow, GPUs to Scale and Speed-up Deep Learning, and applied Deep Learning Capstone Project. The course is taught by top instructors at IBM (International Business Machines), including Romeo Kienzler (Chief Data Scientist, IBM), Samaya Madhavan (Advisory Software Engineer, IBM), Saeed Aghabozorgi (PhD, Sr. data scientist, IBM), Joseph Santarcangelo (PhD., Data Scientist, IBM), and Alex Aklson (Ph.D., Data Scientist, IBM). The course duration is eight months, where you will be provided with information from basics to advance. This program consists of six skill-building courses, one of which is about Pytorch. You can join this course and know more about it using the following link.

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4. Deep Learning A-Z™: Hands-On Artificial Neural Networks (Udemy)

This course is a 4.5 rated course on the platform of Udemy. This course is a complete guide to artificial neural networks. The course covers topics such as building and intuition of artificial neural networks, building and intuition of convolutional neural networks, and building and intuition of recurrent neural networks. The course also includes building intuition, a case study of self-organizing maps, and building and intuition of autoencoders. The course ends with machine learning basics such as regression and classification intuition, pre-processing data template, and logistic regression implementation. They mention that the course has pre-requisites to meet for better understanding of the course, which is high school level mathematics and basic knowledge of python, which is a programming language. Along with over twenty-two hours of on-demand video, you will get 37 articles and 5 downloadable resources to get a lifetime. You will also get access to the resources on television and mobile. You will also receive certification for completion of the course. You can join this course and know more about it using the following link. 

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5. Convolutional Neural Networks in TensorFlow (Coursera)

This course is a 4.7 rated course on the platform of Coursera. This course is offered by DeepLearning.AI, which is an education technology company. The instructor for this particular course is Lead AI Advocate at Google, Laurence Moroney. This course is mainly meant for software developers and is a part of the upcoming Machine Learning in Tensorflow Specialization program offered at Coursera by DeepLearning.AI. This course covers inductive transfer, augmentation, dropouts, machine learning, and neural networking with Tensorflow. This course is of intermediate level and has some pre-requisites to meet before starting the course: high school level mathematics and basic knowledge of python, a programming language. They estimate that the course may require approximately twenty-six hours to complete. You will also receive certification for completion of the course. You can join this course and know more about it using the following link.

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6. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization (Coursera)

This course is a 4.7 rated course on the platform of Coursera. This course is offered by deep learning. AI, which is an education technology company. The instructor for this particular course is Andrew Ng. (co-founder of Deep learning. &AI) and the curriculum of the course has been developed by Kian Katanforoosh and Younes Bensouda Mourri, who are top instructors at Coursera. As this course is an intermediate course, the pre-requisites for this course are intermediate Python skills: basic programming, understanding of loops, if/else statements, data structures, and basic knowledge of linear algebra & ML. The course will cover topics such as Tensorflow, deep learning, mathematical optimization, and hyperparameter tuning. Apart from learning these topics, you will also learn to develop test sets and analyze variance for building deep learning applications. You will also learn to use standard neural network techniques; they estimate that the course may require approximately twenty-two hours to complete. You will also receive certification for completion of the course. You can join this course and know more about it using the following link.

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7. Neural Networks (Google)

This is a 4.5 out of 5 rated courses offered by Google. This is a short course offered by google, consisting of a few videos of a total duration estimated to be of few hours. This course is backed up by study materials and practice exercises. Topics explored in this course are neural networks, training neural nets, multiclass neural nets, and embeddings. They estimate that the course may require approximately three hours to complete, including video lectures and exercises. So, it can be concluded that the course can be completed in one or two sittings. You can join this course and know more about it using the following link.

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8. Deep Learning and Neural Networks for Financial Engineering offered by New York University on edx

This course is offered by New York University. This seven-week-long course covers basics and builds its way up to some advanced topics. The instructor of the course is an Adjunct Professor of New York University Tandon School of Engineering. This course will help you build your strength in Artificial intelligence focusing on neural networks, and you will get to learn its applications in the field of finance. The topics covered in this course include classical machine learning, notational conventions, linear regression, neural networks, TensorFlow, Keras, convolutional neural networks, recurrent neural networks, training neural networks, and advanced level topics interpretation and transfer learning, neural language processing. You can join this course and know more about it using the following link. 

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9. CS50’s Introduction to Artificial Intelligence with Python offered by Harvard University on edx

This course is offered by Harvard University. The instructors for the course are David J. Malan (Professor of the Practice of Computer Science, Harvard University) and Brian Yu (Senior Preceptor in Computer Science, Harvard University). The areas covered in this course are graph search algorithms, adversarial search, knowledge representation, logical inference, probability theory, Bayesian networks, Markov models, constraint satisfaction, machine learning, reinforcement learning, neural networks, and natural language processing. Techniques They estimate that the course may require seven weeks to complete. You will also receive certification for completing the course, but you will need to pay a minimal fee to get that; otherwise, the course is free of cost. You can join this course and know more about it using the following link.

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10. Create Machine Learning models and Derive Insights from Data Offered by New York University on edx

 This course is offered by New York University. This four-month-long course covers the basics of machine learning and builds its way up to applications of it in finance. This is a program consisting of two skill-developing courses. The instructor of the course is an Adjunct Professor of New York University Tandon School of Engineering. This program covers classical machine learning and neural networks. This course focus on the applications of machine learning and neural networks in the field of finance. You will also receive certification for completion of the course. You can join this course and know more about it using the following link.

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