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In this post, I will share some of the best Deep Learning courses available on Udemy for Programmers and Software Engineers. In the first section, you will find a quick list, and in the next section, each course is described in detail. Please have a look:
Are you interested in learning more about Deep Learning courses? Check out the details below to see which Deep Learning course is right for you.
Best Deep Learning Courses on Udemy
A deep understanding of deep learning (with Python intro)
★★★★★
$15.00 in stock
Udemy.com
as of July 17, 2024 1:13 pm
Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems.
Created by: Mike X Cohen
Neuroscientist, writer, professor
Neuroscientist, writer, professor
Rating:4.78 (1211reviews)
10958students enrolled
What Will I Learn?
- The theory and math underlying deep learning
- How to build artificial neural networks
- Architectures of feedforward and convolutional networks
- Building models in PyTorch
- The calculus and code of gradient descent
- Fine-tuning deep network models
- Learn Python from scratch (no prior coding experience necessary)
- How and why autoencoders work
- How to use transfer learning
- Improving model performance using regularization
- Optimizing weight initializations
- Understand image convolution using predefined and learned kernels
- Whether deep learning models are understandable or mysterious black-boxes!
- Using GPUs for deep learning (much faster than CPUs!)
Requirements
- Interest in learning about deep learning!
- Python/Pytorch skills are taught in the course
- A Google account (google-colab is used as the Python IDE)
Target audience
- Students in a deep learning course
- Machine-learning enthusiasts
- Anyone interested in mechanisms of AI (artificial intelligence)
- Data scientists who want to expand their library of skills
- Aspiring data scientists
- Scientists and researchers interested in deep learning
Recommender Systems and Deep Learning in Python
★★★★★
$17.00 in stock
Udemy.com
as of July 17, 2024 1:13 pm
The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques
Created by: Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Artificial intelligence and machine learning engineer
Rating:4.63 (4021reviews)
20832students enrolled
What Will I Learn?
- Understand and implement accurate recommendations for your users using simple and state-of-the-art algorithms
- Big data matrix factorization on Spark with an AWS EC2 cluster
- Matrix factorization / SVD in pure Numpy
- Matrix factorization in Keras
- Deep neural networks, residual networks, and autoencoder in Keras
- Restricted Boltzmann Machine in Tensorflow
Requirements
- For earlier sections, just know some basic arithmetic
- For advanced sections, know calculus, linear algebra, and probability for a deeper understanding
- Be proficient in Python and the Numpy stack (see my free course)
- For the deep learning section, know the basics of using Keras
Target audience
- Anyone who owns or operates an Internet business
- Students in machine learning, deep learning, artificial intelligence, and data science
- Professionals in machine learning, deep learning, artificial intelligence, and data science
PyTorch for Deep Learning with Python Bootcamp
★★★★★
$20.00 in stock
Udemy.com
as of July 17, 2024 1:13 pm
Learn how to create state of the art neural networks for deep learning with Facebook's PyTorch Deep Learning library!
Created by: Jose Portilla
Head of Data Science at Pierian Training
Head of Data Science at Pierian Training
Rating:4.63 (3552reviews)
23237students enrolled
What Will I Learn?
- Learn how to use NumPy to format data into arrays
- Use pandas for data manipulation and cleaning
- Learn classic machine learning theory principals
- Use PyTorch Deep Learning Library for image classification
- Use PyTorch with Recurrent Neural Networks for Sequence Time Series Data
- Create state of the art Deep Learning models to work with tabular data
Requirements
- Understanding of Python Basic Topics (data types,loops,functions) also Python OOP recommended
- Be able to work through basic derivative calculations
- Admin Permissions on your computer (ability to download our files)
Target audience
- Intermediate to Advanced Python Developers wanting to learn about Deep Learning with PyTorch
Complete Tensorflow 2 and Keras Deep Learning Bootcamp
★★★★★
$15.00 in stock
Udemy.com
as of July 17, 2024 1:13 pm
Learn to use Python for Deep Learning with Google's latest Tensorflow 2 library and Keras!
Created by: Jose Portilla
Head of Data Science at Pierian Training
Head of Data Science at Pierian Training
Rating:4.61 (6698reviews)
40124students enrolled
What Will I Learn?
- Learn to use TensorFlow 2.0 for Deep Learning
- Leverage the Keras API to quickly build models that run on Tensorflow 2
- Perform Image Classification with Convolutional Neural Networks
- Use Deep Learning for medical imaging
- Forecast Time Series data with Recurrent Neural Networks
- Use Generative Adversarial Networks (GANs) to generate images
- Use deep learning for style transfer
- Generate text with RNNs and Natural Language Processing
- Serve Tensorflow Models through an API
- Use GPUs for accelerated deep learning
Requirements
- Know how to code in Python
- Some math basics such as derivatives
Target audience
- Python developers interested in learning about TensorFlow 2 for deep learning and artificial intelligence
Machine Learning, Data Science and Deep Learning with Python
★★★★★
$20.00 in stock
Udemy.com
as of July 17, 2024 1:13 pm
Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks
Created by: Sundog Education by Frank Kane
Founder, Sundog Education. Machine Learning Pro
Founder, Sundog Education. Machine Learning Pro
Created by: Frank Kane
Founder, Sundog Education
Founder, Sundog Education
Created by: Sundog Education Team
Sundog Education Team
Sundog Education Team
Rating:4.52 (27957reviews)
168596students enrolled
What Will I Learn?
- Build artificial neural networks with Tensorflow and Keras
- Implement machine learning at massive scale with Apache Spark's MLLib
- Classify images, data, and sentiments using deep learning
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Data Visualization with MatPlotLib and Seaborn
- Understand reinforcement learning - and how to build a Pac-Man bot
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
- Use train/test and K-Fold cross validation to choose and tune your models
- Build a movie recommender system using item-based and user-based collaborative filtering
- Clean your input data to remove outliers
- Design and evaluate A/B tests using T-Tests and P-Values
Requirements
- You'll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will walk you through installing the necessary free software.
- Some prior coding or scripting experience is required.
- At least high school level math skills will be required.
Target audience
- Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.
- Technologists curious about how deep learning really works
- Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful.
- If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first.
Python for Computer Vision with OpenCV and Deep Learning
★★★★★
$16.00 in stock
Udemy.com
as of July 17, 2024 1:13 pm
Learn the latest techniques in computer vision with Python , OpenCV , and Deep Learning!
Created by: Jose Portilla
Head of Data Science at Pierian Training
Head of Data Science at Pierian Training
Rating:4.62 (8583reviews)
46092students enrolled
What Will I Learn?
- Understand basics of NumPy
- Manipulate and open Images with NumPy
- Use OpenCV to work with image files
- Use Python and OpenCV to draw shapes on images and videos
- Perform image manipulation with OpenCV, including smoothing, blurring, thresholding, and morphological operations.
- Create Color Histograms with OpenCV
- Open and Stream video with Python and OpenCV
- Detect Objects, including corner, edge, and grid detection techniques with OpenCV and Python
- Create Face Detection Software
- Segment Images with the Watershed Algorithm
- Track Objects in Video
- Use Python and Deep Learning to build image classifiers
- Work with Tensorflow, Keras, and Python to train on your own custom images.
Requirements
- Must have clear understanding of Python Basics
- Windows 10 or MacOS or Ubuntu
- Must have Install Permissions on Computer
- WebCam if you want to learn the video streaming content
Target audience
- Python Developers interested in Computer Vision and Deep Learning. This course is not for complete python beginners.
Deep Learning A-Z™: Hands-On Artificial Neural Networks
★★★★★
$19.00 in stock
Udemy.com
as of July 17, 2024 1:13 pm
Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.
Created by: Kirill Eremenko
Data Scientist
Data Scientist
Created by: Hadelin de Ponteves
AI Entrepreneur
AI Entrepreneur
Created by: Ligency I Team
Helping Data Scientists Succeed
Helping Data Scientists Succeed
Created by: Ligency Team
Helping Data Scientists Succeed
Helping Data Scientists Succeed
Rating:4.54 (41285reviews)
342076students enrolled
What Will I Learn?
- Understand the intuition behind Artificial Neural Networks
- Apply Artificial Neural Networks in practice
- Understand the intuition behind Convolutional Neural Networks
- Apply Convolutional Neural Networks in practice
- Understand the intuition behind Recurrent Neural Networks
- Apply Recurrent Neural Networks in practice
- Understand the intuition behind Self-Organizing Maps
- Apply Self-Organizing Maps in practice
- Understand the intuition behind Boltzmann Machines
- Apply Boltzmann Machines in practice
- Understand the intuition behind AutoEncoders
- Apply AutoEncoders in practice
Requirements
- High school mathematics level
- Basic Python programming knowledge
Target audience
- Anyone interested in Deep Learning
- Students who have at least high school knowledge in math and who want to start learning Deep Learning
- Any intermediate level people who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning
- Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets
- Any students in college who want to start a career in Data Science
- Any data analysts who want to level up in Deep Learning
- Any people who are not satisfied with their job and who want to become a Data Scientist
- Any people who want to create added value to their business by using powerful Deep Learning tools
- Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business
- Any Entrepreneur who wants to create disruption in an industry using the most cutting edge Deep Learning algorithms
Tensorflow 2.0: Deep Learning and Artificial Intelligence
★★★★★
$23.00 in stock
Udemy.com
as of July 17, 2024 1:13 pm
Machine Learning & Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More!
Created by: Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Artificial intelligence and machine learning engineer
Created by: Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Artificial Intelligence and Machine Learning Engineer
Rating:4.73 (7920reviews)
39149students enrolled
What Will I Learn?
- Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
- Predict Stock Returns
- Time Series Forecasting
- Computer Vision
- How to build a Deep Reinforcement Learning Stock Trading Bot
- GANs (Generative Adversarial Networks)
- Recommender Systems
- Image Recognition
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Use Tensorflow Serving to serve your model using a RESTful API
- Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices
- Use Tensorflow's Distribution Strategies to parallelize learning
- Low-level Tensorflow, gradient tape, and how to build your own custom models
- Natural Language Processing (NLP) with Deep Learning
- Demonstrate Moore's Law using Code
- Transfer Learning to create state-of-the-art image classifiers
Requirements
- Know how to code in Python and Numpy
- For the theoretical parts (optional), understand derivatives and probability
Target audience
- Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0
TensorFlow Developer Certificate in 2022: Zero to Mastery
★★★★★
$17.00 in stock
Udemy.com
as of July 17, 2024 1:13 pm
Pass the TensorFlow Developer Certification Exam by Google. Become an AI, Machine Learning, and Deep Learning expert!
Created by: Andrei Neagoie
Founder of zerotomastery.io
Founder of zerotomastery.io
Created by: Daniel Bourke
Machine Learning Engineer/Writer/Video maker
Machine Learning Engineer/Writer/Video maker
Created by: Zero To Mastery
Learn In-Demand Skills. Get Hired.
Learn In-Demand Skills. Get Hired.
Rating:4.66 (5132reviews)
37825students enrolled
What Will I Learn?
- Learn to pass Google's official TensorFlow Developer Certificate exam (and add it to your resume)
- Build TensorFlow models using Computer Vision, Convolutional Neural Networks and Natural Language Processing
- Complete access to ALL interactive notebooks and ALL course slides as downloadable guides
- Increase your skills in Machine Learning and Deep Learning, to test your abilities with the TensorFlow assessment exam
- Understand how to integrate Machine Learning into tools and applications
- Learn to build all types of Machine Learning Models using the latest TensorFlow 2
- Build image recognition, text recognition algorithms with deep neural networks and convolutional neural networks
- Using real world images to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy
- Applying Deep Learning for Time Series Forecasting
- Gain the skills you need to become a TensorFlow Certified Developer
- Be recognized as a top candidate for recruiters seeking TensorFlow developers
Requirements
- Mac / Windows / Linux - all operating systems work with this course!
- No previous TensorFlow knowledge required. Basic understanding of Machine Learning is helpful
Target audience
- Anyone who wants to pass the TensorFlow Developer exam so they can join Google's Certificate Network and display their certificate and badges on their resume, GitHub, and social media platforms including LinkedIn, making it easy to share their level of TensorFlow expertise with the world
- Students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow
- Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning
- Anyone looking to master building ML models with the latest version of TensorFlow
Modern Deep Learning in Python
★★★★★
$18.00 in stock
Udemy.com
as of July 17, 2024 1:13 pm
Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.
Created by: Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Artificial intelligence and machine learning engineer
Rating:4.78 (2944reviews)
31770students enrolled
What Will I Learn?
- Apply momentum to backpropagation to train neural networks
- Apply adaptive learning rate procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks
- Understand the basic building blocks of TensorFlow
- Build a neural network in TensorFlow
- Write a neural network using Keras
- Write a neural network using PyTorch
- Understand the difference between full gradient descent, batch gradient descent, and stochastic gradient descent
- Understand and implement dropout regularization
- Understand and implement batch normalization
- Understand the basic building blocks of Theano
- Build a neural network in Theano
- Write a neural network using CNTK
- Write a neural network using MXNet
Requirements
- Be comfortable with Python, Numpy, and Matplotlib
- If you do not yet know about gradient descent, backprop, and softmax, take my earlier course, Deep Learning in Python, and then return to this course.
Target audience
- Students and professionals who want to deepen their machine learning knowledge
- Data scientists who want to learn more about deep learning
- Data scientists who already know about backpropagation and gradient descent and want to improve it with stochastic batch training, momentum, and adaptive learning rate procedures like RMSprop
- Those who do not yet know about backpropagation or softmax should take my earlier course, deep learning in Python, first
Price Statistics
- All prices mentioned above are in United States dollar.
- This product is available at Udemy.
- At udemy.com you can purchase A deep understanding of deep learning (with Python intro) for only $15.00
- The lowest price of Tensorflow 2.0: Deep Learning and Artificial Intelligence was obtained on July 17, 2024 1:13 pm.
