A neural network is also commonly known as an Artificial Neural Network (ANN). These advanced computational systems comprise a processing unit called artificial neurons that can reflect the behavior of human intelligence, allowing computer programs to solve problems such as pattern recognition, computations, classifications, and processing various components to solve common problems. 

There are various types of neural networks, and understanding each of them and their capabilities is essential as they are an integral part of the field of Artificial Intelligence (AI) and its subfields. 

With AI revolutionizing the way businesses operate and highly impacting people’s lives, it is essential for aspirants aiming to break into an AI career to have comprehensive knowledge about neural networks ranging from theoretical aspects to the advanced necessities of building a neural network for complex problems across various domains. 

Therefore, it is crucial for newcomers or developers looking to switch careers in AI to upskill themselves to be well-equipped with the right skills and succeed as AI engineers.

Related reading: Top 15 Online Courses to Learn Python

1. IBM AI Engineering Professional Certificate – Coursera

IBM AI Engineering Professional Certificate – Coursera

To learn neural network concepts in detail, an AI certificate course by industry leaders is a value-addition to a career as it is widely recognized in the industry.

This certification by IBM on Coursera is one such course that equips students and professionals with cutting-edge methods of AI. The learners will cover various machine learning algorithms and deep learning neural networks to build intelligent systems for business and other industries. Furthermore, the learners will be well aware of the tools required to succeed as an AI engineer in the organizational development environment. 

The learners will delve into the concepts of supervised and unsupervised learning using Python programming language and understand the uses of various deep learning libraries to perform a wide range of tasks involving object recognition, computer vision, image classification, text analytics, natural language processing (NLP) and building recommender systems. 

Throughout the course, the learners will undergo hands-on training to build scalable machine learning systems and learn the critical training, testing, and deploying various deep learning architectures.

In addition to the professional certificate, the learners will also receive the digital IBM badge from recognizing an individual for their proficiency in AI engineering. 

The course curriculum includes:

Machine Learning with Python

The first module focuses on the purpose of machine learning and its real-world applications and provides a general overview of machine learning topics such as supervised and unsupervised learning, model evaluation, and various machine learning algorithms.

Introduction to Deep Learning and Neural Networks with Keras

In the second module, the learners will be introduced to deep learning and how deep learning models differ from ANN. The learners will also learn about various deep learning models and work on implementations to create their first deep learning model using Keras.

At the end of this module, the learners will have clarity about neural networks, deep learning models, unsupervised deep learning models such as autoencoders, and restricted Boltzmann machines. Besides, the learners will understand convolutional neural networks (CNN) and recurrent networks, their uses, and practical experience to build neural networks for real-world problems.

Introduction to Computer Vision and Image Processing

This module covers the concepts of image processing, image classification, and object detection and understands applications such as self-driving cars, robotics, and augmented reality using AI. The learners will learn to build, train, and test using custom images and classifiers with the detection models for higher classification performance.

Deep Neural Networks with PyTorch

The fourth module covers the concepts of building deep learning models using Python. The learners will gain a solid understanding of various libraries and packages in Python for AI model building. Furthermore, the learners will cover the fundamentals of linear regression, logistic regression, softmax, and feedforward deep neural networks.

In addition, the learners will explore activation function, normalization, and dropout layers in a neural network and their uses. Finally, the learners will cover concepts on CNN and how transfer learning is implemented to solve a specific problem.

Building Deep Learning Models with TensorFlow

In this module, the learners will cover advanced concepts on deep learning model building using TensorFlow. The concepts on unlabeled and unstructured data, shallow neural networks, and their uses are covered in-depth. Further, the learners deep dive into model-building stages with TensorFlow concepts such as main functions, operations, and execution pipelines.

In addition, the concepts on the uses of regression, classification, curve fitting, and minimizing error functions with TensorFlow models are covered with practical implementations. Finally, the learners will be familiar with various deep learning architectures, the application of TensorFlow for backpropagation, and tuning weights and biases for the neural networks being trained.

AI Capstone Project with Deep Learning

The final capstone is mandatory for obtaining the certification and IBM digital badge. The projects will be conducted using real-world challenges, and learners must build a deep learning model from scratch. Finally, the learners will be required to demonstrate the validity of their model for approval of the project.

Instructor: Saeed Adghabozorgi, Alex Aklson, Samaya Madhavan, Romeo Kienzler, Joseph Santarcangelo, Aije Egwaikhide, and Jeremy Nilmeier

Level: Intermediate

Duration: 9 months

User Review: 4.5/5

No. of Reviews: 3870

Price: Free Enrollment (Additional charges for certification may apply)

2. Deep Learning Specialization by DeepLearning. AI – Coursera 

Deep Learning Specialization by DeepLearning. AI – Coursera

For learning about neural networks, there are various online courses. However, this is one course where learners will learn from the very best of industry. DeepLearning offers this program. AI which was created by the pioneer Andrew Ng on Coursera. 

In this specialization, the learners will begin from the fundamentals of deep learning to understand the capabilities and challenges of deep learning models. Next, the learners will explore the building and training of neural network architectures such as CNN and LSTMs, Transformers, and more.

Furthermore, the learners will learn how to make these models better with various strategies such as dropout, batch normalization, Xavier/He initialization in detail. Besides, the learners will master both the theoretical concepts and practical implementations using Python and TensorFlow for various real-world problems like speech recognition, chatbots, machine translation, and much more. 

The key takeaways from the course include:

  • Ability to build, train and validate deep neural network models.
  • Implementation of vectorized neural networks and identifying architecture patterns.
  • Applying deep learning to various applications.
  • Best practices and optimization algorithms.
  • Strategies to reduce errors in machine learning systems.
  • Perform deep learning techniques to provide end-to-end transfer and multi-task learning.
  • CNN for image recognition and classification tasks.
  • RNN for NLP problems and the implementation of HuggingFace Tokenizers.

The course curriculum includes the following:

  • Neural Networks and Deep Learning
  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization
  • Structuring Machine Learning Projects
  • Convolutional Neural Networks.

Instructor: Andrew Ng, Kian Katanforoosh, Younes Bensouda Mourri

Level: Intermediate

Duration: 5 months

User Review: 4.9/5

No. of Reviews: 118,736

Price: Free Enrollment (Additional charges for certification may apply)

3. Neural Networks in Python from Scratch: Complete Guide – Udemy

Neural Networks in Python from Scratch- Complete Guide – Udemy

This highest-rated course is available on the Udemy platform. The primary objective of this tutorial is to provide learners with the theoretical and mathematical concepts related to neural networks. 

First, the learners will cover critical concepts such as perceptron, activation function, and multilayer networks. Moreover, some advanced concepts like backpropagation algorithms and gradient descent are covered with follow-along demonstrations. Next, the course moves onto the practical part, where learners will learn to build neural networks from scratch.

Furthermore, the learners will gain solid programming experience using Python for building machine learning models targeting various problems in the field of data science. In addition, the most popular Python libraries for building machine learning models are covered with multiple examples and projects.

 The course modules are:

  • Introduction
  • Single Layer Perceptron
  • Multilayer Perceptron
  • Libraries for Neural Networks

Instructor: Jones Granatyr, JA Expert Academy and Ligency Team

Level: Beginner

Duration: 8 hours and 41 minutes

User Review: 4.7/5

No. of Reviews: 196

Price: $47.6

4. Deep Learning A-Z: Hands-On Artificial Neural Networks – Udemy

This certification course is available on Udemy. It is among the bestselling courses on the platform. In this program, the learners will learn about neural networks with six real-world projects to gain sufficient hands-on coding experience. Additionally, the learners will go over various tools and libraries used to build neural network models.

At the end of the course, the learners will gain proficiency with deep learning concepts and understand the difference between various neural networks and be able to apply concepts like self-organizing maps in practice and build different models for such CNN, RNN, Boltzmann machines, and Autoencoders for various problems like image recognition, stock price prediction, financial fraud detection, and recommender systems.

The course contents are:

  • Introduction
  • ANN
  • ANN Intuition and Building an Ann
  • CNN, CNN Intuition and Building a CNN
  • RNN, RNN Intuition and Building an RNN
  • Evaluating and Improving RNN
  • Self-Organizing Maps
  • SOM Intuition and Building a SOM
  • Mega Case Study
  • Building Boltzmann Machine
  • Building Autoencoders
  • Regression and Classification
  • Data Preprocessing Template
  • Logistic Regression Implementation
  • Bonus Lectures/ Machine Learning Basics

Instructor: Krill Eremenko, Hadelin de Ponteves, and Ligency Team

Level: Beginner/Intermediate

Duration: 22 hours and 37 minutes

User Review: 4.6/5

No. of Reviews: 38,607

Price: $47.6

5. Deep Learning Nanodegree Program – Udacity

This specialization course is developed in collaboration with AWS and Facebook AI and is available on Udacity. In this course, the learners will gain mastery over neural network concepts and implementations using deep learning frameworks such as PyTorch.

Moreover, the learners will understand how to build CNN for image recognition and RNNs for sequence regeneration tasks. In addition, the learners will deep dive into advanced concepts and models such as generative adversarial networks (GAN) for image generation and learn the deployment of models. 

The program is created specifically for intermediate learners; therefore, participants are expected to have a working knowledge of Python programming and its libraries, such as NumPy and Pandas. However, there are additional prerequisites of familiarity with calculus and linear algebra.

The course curriculum includes:

  • Introduction
  • Neural Networks
  • Convolutional Neural Networks
  • RNN
  • GAN
  • Deploying a Sentiment Analysis Model

Instructor: Mat Leonard, Luis Serrano, Cezanne Camacho, Alexis Cook, Jennifer Staab, Sean Carrell, Ortal Arel, Jay Alammar, and Daniel Jiang

Level: Intermediate

Duration: 4 months

User Review: 4.7/5

No. of Reviews: 2100

Price: Monthly Access: $310.8/ 4-Month Access: $1056.8

6. Deep Learning Course (With Keras and TensorFlow) Certification Training – Simplilearn

This certification training program is available on Simplilearn. In this course, the learners will learn the best practices and master the deep learning concepts using Keras and TensorFlow. The learners will be introduced to the various frameworks, the fundamentals of ANN, PyTorch, Autoencoders, and more. 

Additionally, the learners will explore building models and interpreting the results and have clarity over how to create an efficient deep learning algorithm. However, this program is ideal for intermediate and advanced learners. Therefore, the participants must be familiar with the basics of deep learning and programming skills. 

The course contents are:

  • Introduction to AI and Deep Learning
  • Introduction to TensorFlow
  • ANN
  • Deep Neural Network and Tools
  • Deep Neural Net Optimization, Tuning, and Interpretability
  • CNN
  • RNN
  • Restricted Boltzmann Machines
  • Autoencoders
  • Free Module: Math Refresher
  • Projects

Instructor: Industry Professionals

Level: Intermediate/Advanced

Duration: 90-Days Flexible Learning

User Review: 4.6/5

No. of Reviews: 1657

Price: $272

7. Deep Learning and Neural Networks for Financial Engineering by New York University – edX

Deep Learning and Neural Networks for Financial Engineering by New York University – edX

This deep learning course ventures into AI and demonstrates how neural networks can be used in various disciplines while focusing on their implementations in finance. It is available on the edX platform. 

The learners will understand various deep learning techniques, data sources, images, and finance text to identify various features.

At the end of this course, the learners will be able to utilize neural networks and deep learning techniques for building prediction models in finance. Moreover, the learners will have clarity on using data from various sources and implementing techniques like image recognition and NLP for predictions. 

Finally, the learners will develop advanced programming skills to build various neural network models for complex problems like portfolio management and optimizing portfolios, risk management, and streamlining various AI-related operations in finance. 

The course modules are:

Week 0: Classical Machine Learning: Overview

  • Guided entry for students who have not taken the first course in the series
  • Notational conventions
  • Basic ideas: linear regression, classification

Week 1: Introduction to Neural Networks and Deep Learning

  • Neural Networks Overview
  • Coding Neural Networks: Tensorflow, Keras
  • Practical Colab

Week 2: Convolutional Neural Networks

  •  A neural network is a Universal Function Approximator
  •  Convolutional Neural Networks (CNN): Introduction
  •  CNN: Multiple input/output features
  •  CNN: Space and time

Week 3: Recurrent Neural Networks

  •  Recurrent Neural Networks (RNN): Introduction
  •  RNN Overview
  •  Generating text with an RNN

Week 4: Training Neural Networks

  • Backpropagation
  • Vanishing and exploding gradients
  • Initializing and maintaining weights
  • Improving trainability
  • How big should my Neural Network be?

Week 5: Interpretation and Transfer Learning

  • Interpretation: Preview
  • Transfer Learning
  • Tensors, Matrix Gradients

Week 6: Advanced Recurrent Architectures

  • Gradients of an RNN
  • RNN Gradients that vanish and explode
  • Residual connections
  • Neural Programming
  • LSTM
  • Attention: introduction

Week 7: Advanced topics

  • Natural Language Processing (NLP)
  • Interpretation: what is going on inside a Neural Network
  • Attention
  • Adversarial examples

Instructor: Ken Perry

Level: Intermediate

Duration: 7 weeks

User Review: NA

No. of Reviews: NA

Price: Pricing details available on Sign-Up

8. Deep Learning: Recurrent Neural Networks in Python – Udemy

Deep Learning- Recurrent Neural Networks in Python – Udemy

This training program is offered on Udemy. The course focuses on providing knowledge about deep learning architectures, especially RNN. The learners will know how RNN is used for sequence modeling and its uses in time series analysis, forecasting, and NLP-related problems. 

In this course, the learners will begin from the fundamentals of machine learning, neural network architectures and then touch upon essential concepts of neural networks for performing classification and regression. Additionally, the learners will explore the concepts of sequential data, time-series data, and how to build models for text data for an NLP problem.

Furthermore, the learners will cover the implementation stages of RNN using TensorFlow and the uses of GRU and LSTM. Next, the learners will be familiar with building a model for time series forecasting using TensorFlow, including projects on predicting stock prices and text classification using RNN with features like spam detection, sentiment analysis, parts-of-speech tagging. Finally, the learners will also understand how to use embedding in TensorFlow for NLP.

 The course contents include:

  • Introduction
  • Google Colab
  • Machine Learning and Neurons
  • Feedforward ANN
  • RNN, Time Series, and Sequence Data
  • NLP
  • In-Depth: Loss Functions
  • In-Depth: Gradient Descent
  • Extras: Setting up the Environment/ Extra Help with Python Coding/ Effective Learning Strategies for Machine Learning
  • Summary

Instructor: Lazy Programmers Inc.

Level: Intermediate

Duration: 11 hours and 49 minutes

User Review: 4.6/5

No. of Reviews: 3447

Price: $47.6

9. Deep Learning Training: TensorFlow Certification – Edureka

This is a certification course available on Edureka. In this program, the learners will master various popular algorithms such as CNN, RCNN, RNN, LSTM, and more using the TensorFlow 2 package in Python.

 In addition, there are various real-time projects such as emotion and gender detection, auto image captioning. Furthermore, the learners will be familiar with writing TensorFlow codes to build deep learning models for text and image processing. 

The learners will also cover the concepts on a single layer and multilayer perceptron, CNN algorithms for image processing and classification. Besides, the advanced concepts of transfer learning, RCNN, ROI, Pooling, Faster RCNN, and Mask RCNN are covered thoroughly with hands-on demonstrations. 

Finally, the learners will be aware of concepts like Boltzmann Machine and Autoencoders and the uses of GAN in the modern AI industry. The course curriculum includes:

  • Introduction to Deep Learning
  • Getting Started with TensorFlow 2
  • CNN
  • Regional CNN
  • GAN
  • Emotion and Gender Detection
  • Introduction to RNN and GRU
  • LSTM
  • Auto Image Captioning using CNN and LSTM

Instructor: Industry Professionals

Level: Intermediate

Duration: 5 weeks

User Review: 5/5

No. of Reviews: 20,000

Price: $272

10. Deep Learning Applications for Computer Vision – Coursera

Deep Learning Applications for Computer Vision – Coursera

The University of Colorado Boulder offers this course on Coursera. The program focuses on computer vision as a field of study and research. 

First, the learners will understand how computer vision tasks are performed and the suggested approaches for model building. Next, the learners will cover the deep learning techniques and understand their practical implementation for computer vision problems. The learners will also learn to analyze the results and the advantages and disadvantages of various methods. 

Finally, the learners will explore hands-on training to learn to use machine learning tools and libraries to build models. The learners will perform image classification, object detection, object segmentation, facial recognition, and activity and pose estimation tasks using advanced neural networks built from scratch. 

The course modules are:

Introduction and Background

The first module introduces the field of computer vision and how information can be extracted from images. Furthermore, the learners will cover the primary categories of tasks in computer vision. In addition, the learners will understand how deep learning techniques impact the field of computer vision.

Classic Computer Vision Tools

The second module allows learners to explore various computer vision tools and techniques, and concepts on convolution operation, linear filters, and algorithms for feature detection and image detection.

Image Classification in Computer Vision

In the third module, the learners will review the challenges in object recognition in the classic computer vision approach. Next, the learners will understand the necessary steps to perform object recognition and image classification tasks using the computer vision pipeline.

Neural Networks and Deep Learning

The fourth module focuses on the image classification pipeline using neural networks. The learners will understand the differences between common problems and computer vision problems with neural network models.

The essential components of neural networks are covered in-depth and practical sessions cover the implementation of neural networks for image classification using TensorFlow.

CNN and Deep Learning Advanced Tools

The final module provides learners with the concepts of various CNN components, including parameters and hyperparameters in a deep neural network. The learners will also cover essential concepts on improving the accuracy of the deep learning models and the essential factors in improving the overall performance of these models. Furthermore, the project in this module requires learners to build and train a deep neural network for image classification.

Instructor: Ioana Fleming

Level: Intermediate

Duration: 22 hours

User Review: NA

No. of Reviews: NA

Price: Free Enrollment (Additional charges for certification may apply)


With the endless potential of AI, it is evident why AI engineers continue to witness high demand in the industry. The capabilities of AI to improve and simplify complex problems have resulted in industries integrating AI across domains.

As per the Stanford AI index, the AI job postings have grown five-fold since 2013 worldwide. On the other hand, a LinkedIn report on rising jobs in 2021 suggests that nearly 150 million technical jobs are expected in the next 5 years, with the most substantial demand seen for professionals with artificial intelligence experience.

At the same time, the national average salary for AI engineers in the US is $120,000, while senior positions are offered $170,000 and more depending on their experience and skillsets. With such staggering numbers, it is seemingly straightforward that AI is a future-proof job, and the high demand for professionals with AI skills will persist for a long-time.

Therefore, it is imperative for aspirants or developers seeking a lucrative career in AI to update and upskill themselves with the latest AI technologies and have a solid foundation on neural networks concepts in theory and practice to master complex model building in an organizational environment. Thus, the courses highlighted in this article provide the necessary head start to embark on a successful career as an AI engineer.

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Kaushik Das

Kaushik is an artificial intelligence researcher and a data scientist with expertise in medical image processing, intelligent automation, computer vision, and big data engineering—a technical and a scientific writer by passion. He is also passionate about sports, photography, traveling, and exploring new technologies.

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