Artificial intelligence and machine learning have become buzzwords these days. Both these technologies are empowering the world with their exceptional capabilities since their inception. From the fraud detection system used by financial institutions to self-driving cars and social media feeds, many things today are powered by AI and ML.
Deep learning is closely associated with ML and AI. Also, you should be familiar with the fact that machine learning is the subset of artificial intelligence , while deep learning is the subset of machine learning . Deep learning came to light in a single night when a robot player bet against a human player in the game of AlphaGo.
Many companies today hire professionals with deep learning expertise to develop robust solutions. As a result, many students aim to master deep learning to get employed by renowned companies.
Well, if you are looking for a course to master deep learning, you have landed at the right place.
This article will help you find the best deep learning course, depending on your level of experience. It highlights the 10 best online deep learning courses. But before discussing the best deep learning courses, the article provides a brief overview of deep learning.
What is Deep Learning?
Deep learning is the subset of machine learning and is a neural network with two or more layers. A neural network, also known as an artificial neural network (ANN) or simulated neural network (SNN), is a computing system, which is the collection of interconnected nodes or units called artificial neurons. It mimics the behavior of the human brain, learns large amounts of data, and makes predictions with incredible accuracy.
A neural network with a single layer attempts to provide approximate accuracy, while additional hidden layers in a deep neural network intend to optimize and refine the prediction for accuracy.
10 Best Online Deep Learning Courses to Pursue in 2022
Below is a curated list of the best online deep learning courses offered by leading course providers, such as Udemy, Coursera, Pluralsight, upGrad, and LinkedIn. Also, the following online deep learning courses provide you with the flexibility to learn at your own pace.
1. Data Science: Deep Learning and Neural Networks in Python
Course Provider: Udemy
Suitable For: Intermediates
Duration: 11 hours of on-demand videos
This course will help you start building your first artificial neural network using deep learning techniques. If you are interested in mastering deep learning to build machine learning and data science projects, this course is for you. It includes multiple practical examples that can help you gain clarity on using deep learning techniques.
Moreover, at the end of the course, you will work on a project where you can leverage deep learning techniques for facial expression recognition. To take this course, you need to have knowledge of the following:
- Calculus
- Matrix Arithmetic
- Probability
- Python Coding
- Numpy Coding
- Logistic regression and linear regression
Course Highlights
This course will help you learn:
- Different types of neural networks and types of problems for which they are used.
- Various terms associated with neural networks include ‘feedforward’, ‘activation’, and ‘backproportion’.
- Writing neural network code from scratch using Python and Numpy.
- Coding a neural network using Google’s TensorFlow.
- Creating a neural network using softmax with an output with k>2 classes.
- Setting up the Anaconda environment and installing Python, Numpy, Scipy, Panda, IPython, TensorFlow, and Theano.
You can showcase your expertise in neural networks by pursuing a certification from Udemy after the completion of this course.
2. Deep Learning A-Z: Hands-On Artificial Neural Networks
Course Provider: Udemy
Suitable For: Beginners
Duration: 22.5 hours on-demand videos
Deep Learning A-Z is all about learning deep learning algorithms in Python. You will find two different volumes of this course, representing two fundamental branches of deep learning, namely supervised deep learning and unsupervised deep learning. Each volume focuses on three different deep learning algorithms.
Moreover, you will be able to work on real-world datasets to solve real-world business problems in this course. The following are the six real-world problems that you will solve in this course:
- Artificial Neural Networks (ANNs) to solve the Customer Churn problem.
- Convolutional Neural Networks (CNNs) for Image Recognition.
- Recurrent Neural Networks (RNNs) to Predict Stock Prices.
- Self-Organizing Maps to detect Frauds.
- Boltzmann Machines to create a Recommender Machine.
- Stacked Autoencoders to take a challenge for the Netflix $1 Million prize.
To take this course, you must have sound knowledge of high-school-level mathematics and basic Python programming .
Course Highlights
This course will help you get:
- Sound knowledge of Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs).
- An in-depth understanding of Boltzmann Machines, AutoEncoders, and Self-Organizing Maps.
- Knowledge of how to put various deep learning concepts into practice.
- Insights into simple linear regression, logistic regression, and multi-linear regression.
Moreover, you will find a step-by-step guide on using PyTorch and TensorFlow, which are two popular machine learning libraries. Also, the course will introduce you to two popular deep learning libraries, namely Theano and Kears.
Upon completing this course, you will get a certificate from Udemy.
3. Introduction to Deep Learning
Course Provider: Coursera
Suitable For: Experts
Duration: 34 hours approximately
Introduction to Deep Learning is intended to provide the learners with a fundamental understanding of neural networks and their applications in computer vision and natural language processing (NLP). It begins with a discussion of the stochastic optimization methods and linear models useful in training deep neural networks.
Further, you will explore popular building blocks of neural networks, including convolutional and recurrent layers and fully connected layers. You will learn how to use these building blocks of neural networks to build complex architectures using TensorFlow and Theano. Moreover, you will build a neural network for image captioning.
As this course is intended for experts, the following are some prerequisites to take this course:
- A strong foundation in Python.
- An in-depth understanding of probability and linear algebra.
- Sound knowledge of logistic regression, linear regression, regularization of linear models, and gradient descent for linear models.
Course Highlights
This course has four different sections, as listed below:
- Introduction to Optimization
This section talks about linear models and stochastic optimization methods. Also, it introduces you to linear regression, logistic regression, gradient descent, model validation and regularization, and overfitting problems.
- Introduction to Neural Networks
You will find everything about neural networks in this section. It begins with introducing neural networks and ends with a guide to building your first deep network model.
- Deep Learning for Images
This section walks you through different building blocks of deep learning and helps you understand how to leverage them to build Convolutional Neural Network (CNN) architectures. Moreover, it gives you an overview of modern CNN architectures and computer vision.
- Unsupervised Representation Learning
This section takes you deeper into the unsupervised parts of deep learning. It guides you in generating, morphing, and searching images with deep learning. After each section, you will find interesting quizzes to help you gain a better understanding of the topics you learned.
You will receive a shareable certificate from Coursera upon completing this course.
4. Building Advanced Deep Learning and NLP Projects
Course Provider: Educative
Suitable For: Intermediates
Duration: 5 hours approximately
As its name indicates, this course not only introduces you to the concepts of deep learning but also helps you work on interesting deep learning and natural language processing (NLP) projects. It is a project-based course, which includes a total of 12 projects.
In addition, you will learn to use the most common machine learning and Python libraries, including Numpy, Scipy, TensorFlow, Scikit-Learn, Pandas, Matplotlib, and many others.
To make the most of this course, you need to have a sound understanding of Python programming and artificial neural networks.
Course Highlights
Since this course is project-based, you will work on the following projects throughout this course.
- Build a COVID-19 Detection System Using X-Rays
- Building a Pokemon Classifier Using Transfer Learning
- Embedding: Two Mini Projects
- IMDB Reviews Sentiment Analysis
- Deciphering Text Using Character-Level RNNs
- Emoji Predictor Using Transfer Learning in NLP
There is no requirement for installing any software to work on these projects. You can learn and practice with live code environments in your browser.
You need to take an exam to earn a certificate at the end of this course.
5. Convolutional Neural Networks
Course Provider: Coursera
Suitable For: Intermediates
Duration: 39 hours
The Convolutional Neural Networks course walks you through the evolution of computer vision and its applications, including autonomous driving, face recognition, and reading radiology images. This course intends to provide you with all the skill sets required to build a powerful convolutional neural network.
Taking this course requires you to have a solid knowledge of Python programming, especially conditional statements and loops, data structures, and a basic understanding of linear algebra and machine learning (ML).
Course Highlights
The entire course is divided into four sections that are as follows:
- Foundations of Convolutional Neural Networks
This section teaches you to implement the foundational layers of convolutional neural networks, such as pooling and convolutions, and organize them in a deep network to solve multi-class image classification problems.
- Deep Convolutional Models: Case Studies
You will discover some popular and powerful practice tricks and techniques used in deep CNNs and learn them to apply to your own deep CNN.
- Object Detection
This section guides you through applying your knowledge of convolutional neural networks (CNNs) to one of the aspects of computer vision, i.e., object detection.
- Special Applications: Face recognition & Neural Style Transfer
You will get to know how you can apply CNN to various fields. Also, you will learn to create your own algorithm and generate art and recognize faces. You will find interesting quizzes at the end of each section.
Finally, you will get a shareable certificate from Coursera.
6. Complete Guide to TensorFlow for Deep Learning with Python
Course Provider: Udemy
Suitable For: Beginners
Duration: 14 hours on-demand video
This course aims to educate you on using Google’s deep learning framework Tensorflow with Python to build artificial neural networks (ANNs) and solve real-world problems with cutting-edge deep learning techniques.
Having the fundamental knowledge of mathematics and Python programming is sufficient to learn this course. This course is ideal for beginners with fundamental Python knowledge and an urge to learn deep learning techniques with TensorFlow.
Course Highlights
In this course, you will find a thorough introduction to neural networks, artificial neural networks (ANNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), AutoEncoders, reinforcement learning, densely connected networks, TensorFlow, and OpenAI Gym. At the end of this course, you will learn:
- How to use TensorFlow for classification and regression tasks, time-series analysis, and recurrent neural networks (RNNs).
- The working of neural networks.
- To conduct reinforcement learning with OpenAI Gym.
- To build your own neural network from scratch in Python.
- TensorFlow for image classification with convolutional neural networks (CNNs).
- How to solve unsupervised learning problems with AutoEncoders and TensorFlow.
- The process for developing Generative Adversarial Networks with TensorFlow.
7. Advanced AI: Deep Reinforcement Learning in Python
Course Provider: Udemy
Suitable For: Intermediates and Experts
Duration: 10.5 hours on-demand videos
This course is a definitive guide to mastering artificial intelligence using deep learning and neural networks. It talks about the applications of deep learning, neural networks, and reinforcement learning. To take this course, here are the prerequisites:
- Understand the basics of reinforcement learning, dynamic programming, Monte Carlo, TD programming, and MDPs.
- Knowledge of college-level mathematics.
- Hands-on experience building machine learning models in Python and Numpy.
- Knowledge of building ANNs and CNNs using Theano and TensorFlow.
If you have a strong background in machine learning and wish to learn state-of-art artificial intelligence techniques, this course is for you.
Course Highlights
This course will help you learn to:
- Build various deep learning agents, including DQN and AC3.
- Apply a wide range of reinforcement learning algorithms to solve any problem.
- Use Convolutional Neural Networks (CNNs) using Deep Q-Learning.
- Reinforcement learning with Rutherford backscattering spectrometry (RBS) networks.
- Q-Learning with deep neural networks.
- Policy gradient methods with Neural Networks.
Along with the above topics, you will also learn how to install Theano, TensorFlow, IPython, Pandas, Scipy, Matplotlib, and Numpy. You will also find a step-by-step guide on setting up the Anaconda environment.
Also, you will get a certificate of completion from Udemy to showcase your deep learning expertise.
8. Deep Learning with Keras
Course Provider: Pluralsight
Suitable For: Intermediates
Duration: 2 hours 30 minutes approximately
Deep Learning with Keras helps you understand how to implement a neural network using Keras. Keras is an open-source library for artificial neural networks. Using this library, you can create a fully-functional neural network with a few lines of code.
Initially, this course intends to make you familiar with how Keras will implement multiple layers of neurons, with each layer having some specific functionality. Later, you will learn to use various methods of Keras to connect these layers of neurons together to form a structure of a deep neural network.
Also, it helps you to implement state-of-the-art neural networks, such as CNNs and RNNs using Keras.
Course Highlights
This course covers the following topics:
- Introduction to Keras and neural networks.
- Installing Keras and TensorFlow and creating the first neural network.
- Introduction to models.
- Estimating layers and neurons and constructing models in Keras.
- Creating layers and shaping and merging them together.
- Building CNNs using Keras.
- Building RNNs using Keras.
- Introduction to Keras datasets and pre-trained models.
After learning this course, you will gain expertise to create a fully-functioning deep neural network (DNN) effectively.
9. Advanced Certificate Programme in Machine Learning & Deep Learning
Course Provider: upGrad
Suitable For: Experts
Duration: 8 months (12 hours per week)
This course is all about machine learning, deep learning, computer vision, neural networks, statistics, and exploratory data analysis (EDA). To take this course, you should hold a bachelor’s degree or master’s degree in science, maths, or statistics with a minimum of 50% marks.
While taking this course, you will work with various machine learning and deep learning libraries, such as TensorFlow and Keras, the Python programming language, MySQL, Excel, and Python libraries, such as Scikit-Learn, Matplotlib, Numpy, and Pandas.
Course Highlights
This course covers the following topics:
- Pre-Program Preparatory Content
You will learn Python for data science and data visualization, along with data analysis using SQL, advanced SQL, data analysis in Excel, and mathematics for machine learning.
- Statistics and Exploratory Data Analysis (EDA)
This section intends to make you familiar with exploratory data analysis (EDA), Git and GitHub, inferential statistics, and hypothesis testing.
- Machine Learning I
Here, you will gain in-depth knowledge of linear regression, logistic regression, and Naive Bayes.
- Machine Learning II
The second section of machine learning provides you with knowledge of advanced regression techniques, support vector machines (SVMs), tree models, unsupervised learning, and Telecom churn case study.
- Deep Learning
This section introduces you to neural networks, convolutional neural networks, and recurrent neural networks. You will also work on the neural network project - Gesture Recognition.
You will receive a certificate from IIIT Banglore upon completion of this course.
10. Deep Learning: Image Recognition
Course Provider: LinkedIn
Suitable For: Intermediates
Duration: 1 hour 43 minutes approximately
This course aims to help you learn how to build a neural network that can recognize objects in images. You will find one project file in this course and multiple exercise files for practicing.
Course Highlights
This course covers the following topics:
- Setting up Your Development Environment.
- How does Classification work?
- Designing a Deep Neural Network for Image Recognition.
- Building and Training the Deep Neural Network.
- Fine-Tuning Pre-trained Neural Networks.
- Using an Image Recognition API.
After completing this course, you will receive a certificate that you can share on your LinkedIn profile.
Conclusion
Here we reach the end of our list of the best online deep learning courses to master deep learning and build deep neural networks. These courses will make you familiar with TensorFlow, Keras, Pandas, Scikit-Learn, Numpy, and other machine learning and deep learning tools and frameworks.
Since all the aforementioned courses are available online, you can learn them at your own pace and from anywhere. Moreover, they offer a certification that increases your credibility and boosts your chances of getting hired by top employers.
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