10 Best Deep Learning Python Courses, Certificates [2021]

  • Post last modified:21 September 2021
  • Reading time:18 mins read
  1. Introduction to Deep Learning in Python [DataCamp]

  2. PyTorch for Deep Learning with Python Bootcamp [Udemy]

  3. Data Science: Deep Learning in Python [Udemy]

  4. Complete Guide to Tensorflow for Deep Learning with Python [Udemy]

  5. Machine Learning with Python: From Linear Models to Deep Learning [edX]

  6. Deep Learning Prerequisites: The Numpy Stack in Python (V2+) [Udemy]

  7. Keras Tutorial: Deep Learning in Python [Datacamp]

  8. Introduction to Deep Learning with Python [SimpliLearn]

  9. Deep Learning Nanodegree Program [Udacity]

  10. Pytorch Essential Training: Deep Learning [Linkedin]

If you are searching online for how to learn deep learning with python and looking for the best deep learning python courses, you are in the right place. Artificial Intelligence and machine learning have become the future of businesses. It becomes crucial to learn deep learning techniques to understand how a machine can work faster than humans.

Earlier, I shared the Best Python Courses, you can check that list to study Python, If you are not familiar with the language. Today, I am going to share the Best Deep Learning Python Courses from popular sites like Udemy and Edx. Before going for any deep learning course, I would like to suggest you check some of the best machine learning courses and certifications to get an overview of machine learning and learn about it.

10 Best Deep Learning Python Courses & Certificates

Introduction to Deep Learning in Python [DataCamp]

The Datacamp Deep Learning course brings you closer to the most popular machine learning technique used for robotics, image recognition, and artificial intelligence. This online deep learning python course introduces you to the fundamental concepts and terminology of deep learning and neural networks. You will learn how to use Deep Learning with Keras 2.0 and understand why Deep Learning is so powerful today.

Dan Becker, a data scientist and contributor to Keras, created this Introduction to Deep Learning in Python Course to understand each key concept and build deep learning models using the latest cutting-edge library. Dan will teach you how to apply machine learning models to make real-world decisions.

Key Highlights & USPs

  • Learn Basics of Deep Learning and Neural Networks to build simple neural networks and generate predictions.

  • Understand how to optimize generated predictions and how the backward propagation method works.

  • Gain knowledge to build deep learning models using tools and Keras library for regression and classification.

  • Get to perform optimization on your built deep learning models and experiment with deeper neural networks.

  • Enhance practical skills by working on hands-on projects.

  • Unlimited access to 17 video lectures, 50 practice exercises, and 4-hour study materials.

Who is it for?

This Datacamp deep learning Program will take you through each course and answer queries regarding deep learning. It does require a prerequisite for supervised machine learning techniques with Scikit-learn. Before enrolling, you can go for an Introduction to machine learning course on Datacamp.

Rating: 4.3/5
Students Enrolled: 199,081
Duration: 4 hours

You can Sign up Here

PyTorch for Deep Learning with Python Bootcamp [Udemy]

If you are thinking of starting your journey in Deep Learning, this Udemy Deep Learning Certification is the perfect online package for you. You will gain the knowledge of creating art of the neural network with Facebook’s Pytorch library. Pytorch is an open-source platform that provides pathways from prototyping to production deployment. This popular framework integrated with Python supports computer vision, natural language processing, and artificial intelligence.

Jose Portilla designed this Deep learning with Python Bootcamp for students to understand the usage of Python tools and libraries like Pytorch. Jose shares his analyzing skill and knowledge of programming to teach data science. Upon completion, you get the ability to apply the basics of Deep Learning for real-life purposes.

Key Highlights & USPs

  • Learn to use the Pytorch library for writing neural networks.

  • Understand how to use NumPy to format data into arrays.

  • Develop knowledge of data manipulation and cleaning using Python’s Pandas Library.

  • How Pytorch works for Image classification and sequence time series data.

  • Creating the state of the deep learning networks with the tabular data.

  • Build deep learning models to solve real-world problems and practice hands-on exercises.

  • It provides full lifetime access to 2 articles, 2 downloadable resources, and 17 hours of on-demand video sessions

Who is it for?

This Deep Learning in Python course will teach everything you want to know about Deep Learning and neural networks. It is designed for intermediate-level data scientists or Python developers to learn about Deep Learning with Pytorch. In the end, you will earn a certificate of completion.

Rating: 4.6/5
Students Enrolled: 16,428
Duration: 17 hours

You can Sign up Here

Data Science: Deep Learning in Python [Udemy]

Deep Learning in Python Programs available on Udemy provide a platform to learn in-depth theory of neural networks and understand how Python is used in Deep Learning. You will learn to build artificial neural networks using deep learning techniques. You get to use fundamentals for predicting actions and trends using given raw data from sources. It is one of the Best Deep Learning Udemy Courses to get a deep knowledge of predicting user actions, how many times they viewed the website, how long they stayed, and insights of returning visitors.

This Online Deep Learning Course for Data Science was created by Lazy Programmer, an AI and machine learning engineer to guide you with statistics, machine learning, algorithms, and more. You get to focus on Python’s libraries such as Google Tensorflow and Numpy in deep learning algorithms.

Key Highlights & USPs

  • Learn the Deep Learning training method called back propagation and how to code back propagation in Python.

  • Understand how deep learning works, not only diagrams and magical black box code.

  • Write programs for neural networks in NumPy.

  • Enable to describe different types of neural networks and for which problems you can use them.

  • Understand how to build multi-layer networks using neurons.

  • At the end of the course, get a project to perform deep learning techniques on facial expression recognition and predict emotions based on a face picture.

  • Get full lifetime access to all the study materials and assignments.

Who is it for?

Are you interested in how to build and understand machine learning models? Then, this course is perfect for you. It is suitable for both Beginners and Programmers who want to use neural networks in machine learning and data science pipeline. There are suggested prerequisites for this online python course: – basic concepts of math such as calculus, arithmetic, and probability, along with basic knowledge of python programming. In the end, grant a successful certificate of completion.

Rating: 4.6/5
Students Enrolled: 46,953
Duration: 11 hours

You can Sign up Here

Complete Guide to Tensorflow for Deep Learning with Python [Udemy]

This Deep Learning Udemy tutorial presents a complete guide to using Google’s Tensorflow framework for deep learning. It aims to teach how to create artificial neural networks using an open-source software library – Tensorflow for computational and mathematical operations. Google Machine Intelligence research organization developed TensorFlow to conduct machine learning and deep neural networks. This Machine Learning Software Library has adapted to a wide variety of companies such as IBM, Airbnb, Google, eBay, Twitter, and the list goes on.

Jose Portilla, the author of this Deep Learning with Python certification course, teaches you TensorFlow with Python to solve problems. You will learn the practical implementation of deep learning theory with Jupyter notebooks and guides full of code and notes. This Deep Learning syllabus covers a range of topics like ANN ( artificial neural networks), CNN ( convolutional neural networks), RNN ( Recurrent Neural Networks), and so on.

Key Highlights & USPs

  • Learn how neural networks work and build them from scratch using Python.

  • How to use TensorFlow for classification and regression tasks.

  • Develop knowledge of Recurrent Neural Networks to predict time series

  • Conduct reinforcement learning with Open AI Gym.

  • Classify Images with Convolutional Neural Networks using TensorFlow.

  • Solve unsupervised machine learning problems with Autoencoders.

  • Gain an understanding of creating Generative Adversarial Networks.

  • Full lifetime access to 7 articles, 4 downloadable resources, and code nodes for practice exercises.

Who is it for?

You can become a deep learning expert by taking this best deep learning course online. It is designed for Python students to upgrade their knowledge and learn the latest Deep Learning techniques with TensorFlow. After completing this intermediate-level deep learning program, you will be accredited with a certificate of completion.

Rating: 4.5/5
Students Enrolled: 90,000
Duration: 14 hours

You can Sign up Here

Machine Learning with Python: From Linear Models to Deep Learning [edX]

TEdx brings you a Machine Learning with Python tutorial to get an in-depth introduction of machine learning and get an overview of deep learning. You will learn machine learning methods used for designing and understanding computer programs from human experience. This machine learning online course provides a complete curriculum to understand key principles and algorithms for the purpose of predictions and controls.

This MIT machine learning course is designed to teach you from linear models to deep learning and reinforcement learning. It is a part of the MITx MicroMasters program in Statistics and Data Science that provides courses to master the skills as a data science expert.

Key Highlights & USPs

  • Learn principles behind machine learning problems such as logistic regressions, classification, clustering, regression and reinforcement learning.

  • How to implement and analyze machine learning models and neural networks.

  • Understand how to select linear or graphic models for different applications.

  • Get an overview of how to train, validate, check parameters, and organize  machine learning projects.

  • Gain a firm knowledge of writing algorithms for turning data into automated predictions.

  • Through hands-on projects, improve your practical skills and apply them to understand customer behaviour, predict trends or risk.

Who is it for?

This advanced-level deep learning course is right for college graduates and professionals who have basic prerequisites of python programming and intermediate maths. It can be enrolled in two ways, that’s free and paid. Any learner can go for this free machine learning course but they will have limited access to the course and in the paid verisor, you would receive a shareable certificate upon completion.

Rating: 4.5/5
Students Enrolled: 135, 274
Duration: 4 months, 10 hours/week

You can Sign up Here

Deep Learning Prerequisites: The Numpy Stack in Python (V2+) [Udemy]

Udemy has become a hot place to learn data science and deep learning. This Deep Learning Tutorial prepares you for the future of AI and machine learning. You will learn about Python libraries and packages such as NumPy, Scipy, Pandas, and Matplotlib. This Udemy Online Deep Learning Program teaches you to read the code and turn those algorithms into actual running code.

Lazy programmer Inc. created this Numpy stack in Python course for learners to understand Numpy array and how it optimizes the speed of operations than Python, how Pandas ease the loading of datasets, and how to show images using Matplotlib.

Key Highlights & USPs

  • Learn NumPy to do vector operations such as addition, subtraction, and multiplication and perform matrix operations like determinants, inverses, products, and solving linear systems.

  • Gain knowledge of Python’s Pandas library to evaluate the data and visualize them.

  • Matplotlib describes plots such as histogram, scatter plot, and line chart.

  • Perform statistical calculations and testing using Scipy.

  • Understand supervised and unsupervised machine learning with real-world examples using Scikit Learn.

  • Determine the pros and cons of machine learning models, including decision trees, Random forest, linear regression.

Who is it for?

It is an advanced-level neural networks and deep learning python course for students and professionals who have little Numpy experience and plan to build a career in data science and machine learning. Upon completion, you will receive a certificate to add as an impressive skill to your job profile.

Rating: 4.6/5
Students Enrolled: 242,241
Duration: 12 hours

You can Sign up Here

Keras Tutorial: Deep Learning in Python [Datacamp]

Are you looking for a Deep Learning in Python tutorial online? This Datacamp online tutorial is an absolute choice for you. It is a step-by -step guide to use Python and its libraries to understand, analyze and visualize the data. Deep learning is one of the cutting-edge technologies in data science that is mainly used for robotics, image recognition and more AI applications. You will learn the most powerful Python library, Keras for building and evaluating deep learning models.

This Datacamp Keras Tutorial was created by Karlijn Willems to introduce you to the basics of Python Deep Learning and developing neural networks in an easy way. You get to focus on deep learning and how its set of instructions is used to automate real-world problems.

Key Highlights & USPs

  • Learn what are the Artificial Neural Networks and how to use them as  powerful modeling tools.

  • How to preprocess data by splitting them in datasets to train, test and finally, standardize for building networks.

  • Build up multi-layer perceptrons or neurons for doing complicated tasks.

  • Compile and fit data into built models and use data for predictions.

  • Evaluate the performance and validations of models.

  • Enable to build up models for regression tasks and fine-tune them.

  • Perform code exercises to enhance practical knowledge.

Who is it for?

It gives a jumpstart to start your deep learning journey with Python and Keras. This Deep learning in Python tutorial is for beginners who  are interested in strengthening knowledge in Python. It is a free Datacamp Deep neural network tutorial  available for everyone.

Rating: 4/5
Duration: 3 hours

You can Sign up Here

Introduction to Deep Learning with Python [SimpliLearn]

Simplilearn provides a chance to learn one of the hottest fields in data science to prepare yourself for a promising career in deep learning. This Deep Learning Tutorial for Beginners introduces how deep learning works and automates the systems without the intervention of human programming. This online course has a list of tutorials as a step-by-step guide to teach deep learning methods and techniques. Deep learning is a subset of machine learning that uses complex programs and neural networks to train models.dy skills to get an entry-level job.

It is the Best Deep Learning Tutorial on Simplilearn, created for non-experienced learners to learn about some of the top deep learning applications. Deep Learning is widely used for weather predictions, content recommendations, and machines to comprehend speech to provide the desired output.

Key Highlights & USPs

  • Learn about how deep learning established its importance across industries.

  • Give an overview of Artificial networks and the advantages of building robotics and pattern recognition systems.

  • Understand how Deep Learning with Python works and use its libraries.

  • Know basics of open-source library Tensor Flow to develop applications for solving large numerical computations.

  • Learn about Recurrent Neural networks to predict the next word in a sentence like Google Search.

  • It gives the best introductory tutorial about and its advantages. 

Who is it for?

This beginner-level deep learning course is a must-learn opportunity for Python programmers and students. Having in-depth knowledge of machine learning would be beneficial. You can upgrade your career with this free deep learning course and showcase your learned skills during the course to prospective employers.

Rating: 4.6/5
Students Enrolled: 20K
Duration: 7 hours

You can Sign up Here

Deep Learning Nanodegree Program [Udacity]

Udacity Deep Learning Course brings you closer to world-changing technologies and becomes a neural networks expert. You will learn to implement neural networks using a deep learning framework Pytorch. You will build deep learning models for predictions and classifications that sound like complicated tasks and can’t be done with no intervention of humans. This best deep learning course gives you a taste of deep learning methods and Python libraries to build your first network.

This Deep Learning with Pytorch Course was designed by more than 5+ professional instructors from various fields and machine learning engineers. The complete Deep Learning Nanodegree Program focuses on developing practical experience and work on real-world challenges like image generations, time-series predictions, etc.

Key Highlights & USPs

  • Learn to build multi-layer neural networks using the modern framework Pytorch and analyze real-world data.

  • Gain experience of using Anaconda and Jupyter Notebooks as development tools.

  • Build convolutional networks and use them for classifying images based on patterns and objects appearing in them.

  • Perform analysis on scripts using recurrent neural networks to generate new texts.

  • Understand how GAN ( Generative Adversarial Networks) works for generating realistic images.

  • Train your model to deploy and create a gateway for accessing it from a website.

  • After each topic, a practice learned deep learning concepts on real-world projects from industry experts.

Who is it for?

This Udacity Nanodegree program has been specifically created for students who have intermediate experience with Python and are interested in learning AI and machine learning. If you have no prior experience in python programming, It’s okay. Before enrolling in this course, you can learn Python language from the Best Udacity Best Python Course online.

Rating: 4.5/5
Students Enrolled: 24,682
Duration: 4 months

You can Sign up Here

Pytorch Essential Training: Deep Learning [Linkedin]

The Linkedin Learning platform has launched a Deep Learning Pytorch Training Program to learn about the deep integration of Pytorch in Deep Learning. This modern deep learning framework gained popularity among industry leaders as it gets easily integrated with top cloud platforms such as Google cloud platform and Amazon Sagemaker.

Jonathan Fernandes created this deep learning syllabus to dive into the pool of deep learning concepts and work on image recognition models to gain experience with neural networks. He focused mainly on hot subjects such as data science, AI, and big data.

Key Highlights & USPs

  • Get to use Google Colaboratory to run Pytorch.

  • Learn how deep integration with Python works to organize datasets and build neural networks with data inputs.

  • How to work with tensors and classes to train the network.

  • Understand the work of loss functions, autograd, and optimizers for troubleshooting a Pytorch network.

  • Gain a must-have deep learning skill in the artificial intelligence tool kit.

  • Sharpen your knowledge by trying your hands on Future Project Ideas.

Who is it for?

This Intermediate – level Deep Learning Course requires a little bit of experience with the Python programming language. You will grant a printable certificate of completion to showcase on your LinkedIn profile and demonstrate your skill to get an entry-level job as a data scientist.

Rating: 4.4/5
Students Enrolled: 20K
Duration: 1 hours

You can Sign up Here


What is deep learning in Python?

Deep learning is a powerful machine learning technique that works with artificial neural networks to imitate human thinking and learn from it. Deep learning in Python means the Python programming language used to write reliable systems to solve machine learning problems.

Python’s code simplicity and consistency require less time to understand and make it easy for developers to focus on building neural networks. It has many libraries to build prototypes quickly and test your models for deep learning purposes.

Is Python good for deep learning?

Yes, Python is a good language for deep learning. Its easy-to-read code offers the ability to implement complex algorithms and versatile workflows behind Deep Learning and AI. It eases the pain of wasting time on understanding the programming language. It provides many frameworks and libraries that reduce development time and cost for the programmers. An extensive set of libraries is available for artificial intelligence and machine learning like Keras, Numpy, Scikit Learn, Pandas, Tensorflow, and more.

Should I learn Python before Deep Learning?

Yes, You should learn Python or have in-depth knowledge along with algebra, probability, and programming experience. Once you have done it, you can take any online course from the above list of best deep learning online courses. You can also start by taking machine learning courses and work your way up.

How long does it take to learn Python?

It can take 5 to 10 weeks to learn a general-purpose programming language that covers basic concepts such as data types, loops, functions, and more.

Is Python better than Matlab for deep learning?

Yes, Indeed. It is way better than Matlab based on performance and also how easily accessible and readable Python is.

I have researched and prepared the above-mentioned list of all the Best 10 Deep Learning Python Courses and Certifications for you. If there’s any course left and you think it should be part of the list, please write down in the comments below. Hope this article will benefit you. Stick to the Geektonight to know more about machine learning, Python, Data Science, and the latest updates about these technologies.

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