Nowadays Best Deep Learning Online Courses has huge demand because this is widely used to solve the number of problems like computer vision, Pattern recognition, etc in industries. If you are looking for good career in deep learning, this is the Best place for you to select the right course. From deep learning you can learn about the different concepts which are very familiar to learn Deep learning like, Logistic Regression, Activation function, Artificial Neural Networks, Layer, Artificial Neuron or Unit, Convolutional Neural Network, Recurrent Neural Network etc. all these topics are covered in below courses. So, our Expert panel handpicked some of the Best Deep learning Courses for you and those are listed below. We hope you to go through the Below Courses.
Deep Learning nanodegree program was created by Udacity. Mat Leonard (program lead), Luis Serrano (curriculum lead), Alexis Cook, Ortal Alera and Arpan Chakraborty. You will understand about the development tools like jupyter notebook and anaconda and also understand that how to build the networks by using the numpy and Python. Instructors will explain that how to classify the images by using the convolutional neural networks. And you people will also understand that how to implement the deep convolutional GAN for the generation of realistic images. You can able to apply the deep neural networks for producing the amazing solutions for the important challenges.
- You people will improve your deep learning skills by building the projects like predicting bike sharing patterns, dog breed classifier, generates TV scripts, generate faces and deploy the sentiment analysis model.
- From predicting bike sharing pattern project students will learn the introduction of neural networks, neural networks training, implementing gradient descent, deep learning with the Python and sentiment analysis.
- From the dog breed classifier project instructors will explain about the convolutional neural networks concepts like weight initialization, auto encoders, cloud computing, convolutional neural networks, CNN in the PyTorch etc.
- From the Generate TV Scripts project you are able to understand the recurrent neural networks topics like hyper parameters, embeddings and word2vec, sentiment prediction RNN etc.
- From the Generate Faces project students will come to know the generative adversarial network topics such as deep convolutional GANs, implementation of GAN in the simple dataset etc.
- From the Deploy sentiment analysis model project instructors will teach the model deployment concepts like model monitoring, web hosting and custom models, introduction of deployment and so on.
Kian Kata Foroosh is head teaching assistant lecturer of computer science at Stanford university with the specialisation of deep learning and artificial intelligence. And Andrew ng is the co founder of deep learning and head of the baidu AI. and also one of the creator of deep learning specialisation is Younes Bensouda Mourri. He is also a teaching assistant of mathematical and computer science engineering in Stanford university. Here you will learn what exactly the deep learning is from these experts. They will teach you with real time examples and helps you to become good at deep learning specialisations. You can get knowledge in these technologies like, RNNs, LSTM, Adam, BatchNorm, Dropout, networks, Xavier etc.
- In the first course, you will learn basics of deep learning and you can easily understand how to build neural networks. And you can get knowledge in implementation of efficient neural networks.
- Neural Architecture has different Key parameters So you can understand easily from this Architecture.To learn course-1 it will take nearly four weeks.
- In the second course, they will teach you how to improve deep neural networks like hyperparameter tuning, optimization and regularization. It takes 12 days to finish the second course.
- In third course, you will understand how to build the structuring machine learning projects.
- In the fourth course, instructor will teach you about (CNN) convolutional neural networks. From this you will understand building process of convolutional neural networks. This course take nearly 4 weeks to learn.
- In the fifth course, you will learn about sequence models. From this you can understand how to build the sequence data, audio and other natural language. It takes 3 weeks to complete the course.
Applied AI with Deep Learning is the specialisation offered by IBM and this course is a part of the advanced data science. This course is included with the assignments, Practice Quizzes, Videos, Self-Paced Learning Option and this course also provides you the course completion certificate after the completion of the course. In this course nearly 23k+ students are enrolled. all the knowledge you need for this course is some skills on coding, python and also some basic knowledge on the maths like linear algebra. The syllabus that is confined in this course is Introduction to deep learning, deep learning frameworks, Deep Learning Applications, scaling and deployment etc.
- In this course the syllabus is made of the 4 modules and they are Introduction to deep learning, deep learning frameworks, scaling and the deployment, Deep Learning Applications.
- The 1st module is all about the introduction to the deep learning and in this you will learn about the Convolutional Neural Networks, Recurrent neural networks, Linear algebra, Methods for the neural network training and LSTMs.
- In the 2nd module you will be discussed with the frameworks of the deep learning in which the topics like Neural Network Debugging, Feed forward networks, PyTorch Packages, Computation Graph, Linear Model etc are included.
- Deep Learning Applications is the 3rd module and in this module you will gain knowledge on the How to implement the anomaly detector and How to deploy a real-time anomaly detector etc.
- The last module helps you in gaining the knowledge Computer Vision with the IBM Watson Visual Recognition, Text Classification with the IBM Watson Natural Language Classifier and about the Parallel Neural Network.
National Research University Higher School of Economics offered this Introduction to Deep Learning course and this course is a part of the Advanced Machine Learning Specialization. This course is included with the Self-Paced Learning Option, Practice Quizzes, assignments, Shareable Certificates etc. the enrollment has been started already with more than 42 k+ students and if you are interested you can start enrolling. By the end of the course you will gain the course completion certificate and in this course there are 6 topics and they are introduction to optimisation, neural networks, deep learning for neural networks, images, unsupervised representation and a final project.
- As there are 6 topics in this course and in the 1st topic i.e., Introduction to optimization you will learn about the stochastic optimization methods and Linear models and you will learn that with 9 videos and 2 practice exercises.
- The 2nd topic is about the deep neural network and and these are thought with 9 videos and 2 practice exercises
- And in the 3rd topic you will gain knowledge on how to build the blocks of the deep learning and Convolutional Neural Networks with the help of 6 videos and 1 practice exercise.
- Autoencoder applications, Generative Adversarial Networks, Natural language processing primer are learnt by you in the 4th course and in the 5th course you will get to know about several Recurrent Neural Network architectures and how the deep learning is used in several sequences like audio, text and video etc.
- In the 6th topic i.e., final project you will know how to apply all the skills you learnt about the neural networks for texts and images and also how to solve the task of the generating descriptions
An Introduction to Practical Deep Learning is offered by the intel and this is taught by the well experienced instructors such as Andres Rodriguez, Nikhil Murthy, Hanlin Tang etc. In this course More than 7k+ students are enrolled. This course will help you in learning the most important concepts of the deep learning, train deep networks using the Intel Nervana Neon and how to apply deep learning to various applications and how to explore the emerging and new deep learning topics. There are 6 topics that you will learn from the syllabus of this course and they are Deep Learning Basics and its introduction, Fine-Tuning, Convolutional Neural Networks and also Detection, Recurrent Neural Networks, Multi node Distributed Training and Training Tips, Intel’s Roadmap and Hot Research etc.
- In the 1st module of this course i.e Introduction to the Deep Learning and Deep Learning Basics you will learn about the introduction to the deep learning and also the basics of the deep learning with the help of 4 videos and 2 exercises.
- The Convolutional Neural Networks (CNN), Fine-Tuning and Detection is the 2nd module in which you will get to know deeply about the Convolutional Neural Networks, Detection and the Fine-Tuning
- The 3rd module is all about the Recurrent Neural Networks by the end of this module you will get a clear understanding on what exactly the Recurrent Neural Networks are and this module is taught with 2 videos and 1 quiz.
- The 4th module is based on the Training Tips, Multi node Distributed Training and this module helps you in gaining the complete knowledge on the training tips and also the multi node distributed training.
- Hot Research and Intel’s Roadmap is the 5th module and in this module you will learn about the Reinforcement Learning, Intel’s Roadmap and hot research and this module is taught to you by the instructors using 3 videos.
Kirill Eremenko is a data science management consultant with five years of experience. He has experience in the fields of finance, transport and other industries. He is very passionate about public speaking, industry events and regularly present on Big data at leading Australian universities. Kirill has academic background in physics and mathematics. Data science is a blend of developing algorithms, and it is the technology that solves the analytical complex problems. In this he will teach you data science by step by step process with real analytic examples and tableau visualisation, data mining and modelling. In this course Kirill offers 21 hours of Video Lectures and 3 articles with Lifetime Course access
- You don’t need any prerequisites for this course, any interested people can learn this course.
- Here you can learn How to Create Basic tableau Visualisation and on top of that learn how you perform data mining in tableau
- They will teach you how to create MLR and SLR (Multiple Linear Regression and Simple Linear regression) and How to Interpret Coefficients of Multiple Linear Regression
- Learn Installation Process and Navigation steps of SQL Server , Microsoft Visual Studio Shell
- In this course you will learn how to model and curve fit of your data. After this course you will perfect in Developing of Tableau , Gret, SQL and SSI.
- You know how to apply the neural networks, artificial neural networks and self organised maps etc.
This course is created by Jong-Moon Chung. He is the professor in the School of Electrical & Electronic Engineering. And also he is the director of communications and Networking Laboratory. He will explain this course in three parts. In the first part he will explain about machine learning and Deep learning technologies for improving the efficiency and productivity of your Business. The second part is, he will explain about the core technologies which includes Convolutional Neural Network, Recurrent Neural Network, and Neural Networks. And in the third part, he explains about the powerful application called Tensorflow Playground. This course will take nearly two comple to complete the course and Jong-Moon offers many Online Videos with full time life access.
- In this course, you will learn about deep learning products and services and how to use these technologies for your future Business strategies.
- He will teach the Business strategies of Deep learning and Machine learning. You will get success in your business by using this strategies in your business.
- From this course, the trainer teach you about the Deep learning computing systems and software. Here you can learn about the most popular deep learning software characteristics like Cognitive toolkit, Keras, Theano, Tensorflow etc.
- From this course you can easily understand the Basics of Deep learning Neural Networks and also the differences between the Deep learning, Machine Learning and Artificial Intelligence.
- He will teach you about the Deep learning with Convolutional Neural Network and Recurrent Neural Networks. He introduces the latest Model of recurrent Neural Networks called Fully Recurrent Neural Network.
Deep learning in python is created by lazy Programmer Inc. he is a data scientist, full stack engineer and big data engineer and also he received his masters degree with the specialisation of machine learning and deep learning in computer science engineering. He is expert in web programming and he taught data science, machine learning, calculus, computer graphics and algorithms for graduates and undergraduate students.he used some technologies like bootstrap, php, python, angular etc. deep learning is also called machine learning techniques concerned with algorithms inspired by structure and function of the brain called artificial Neural Network. Here he will explain in detail about Neural Network technology and also how to code with tensorflow and python.
- Here you can learn the installation process of tensorflow and how to code with tensor and with pure python.
- In this course you need to learn how to build and understand the Artificial Neural Networks using deep learning techniques.
- You can to learn some basic models of linear regression and logistic regression.
- One of the important training method they derived called “Backpropagation”. It shows to how to code Backpropagation in numpy, from the numpy features like first ”the slow way” and then the “fastest way”.
- Using Google’s tensorflow, you can learn how to code a neural networks.
- You will learn the concepts like softmax, introduction of backpropagation, recursiveness of backpropagation, neural network for regression, cross validation, hyperparameters etc.
Jose Portilla is creator of this course deep learning. He is a great data scientist. He received bachelor’s degree and Master’s degree in Santa Clara university. He has good experience in data science and programming. He is a professional instructor and trainer to teach deep learning from zero. Francesco Mosconi is also a lead instructor trainer and consultant for deep learning. And also he earned Phd in biophysics at University of Padua. These two instructors will helps you to learn about deep learning. They will teach you how to build and understand about deep learning models for images, sound, text by using python and keras. Nearly 11k+ students are enrolled for this course. In this course the instructors will offer 9 hours video and 6 articles with lifetime course access.
- They will teach you the complete introduction of deep learning so that you can understand how simple the deep learning is and also you can learn how to build deep learning models.
- They will train you about convolutional and recurrent neural networks and also how to run the models in cloud using a GPU.
- You will be able to solve the supervised and unsupervised learning problems involving sound, text, images etc by applying deep learning.
- They will teach you to learn deep learning internally without intimidation.
- In this course you will get knowledge in python, linear algebra and use of bash shell.
- here learn how to install and how to use the python and keras to build deep learning Models.
Lazy programmer Incorporation. Is the creator of deep learning and convolutional neural network. Here Instructor is a big data scientist, full stack software engineer, and big data engineer. He received his Master degree in the field of computer science engineering with the specialisation of pattern recognition and machine learning. He taught data science, machine learning, algorithms etc courses for graduate and undergraduate students. He frequently uses some of the big data technologies like, hadoop, pig,spark, hive, etc. the team of Lazy programmer Inc. will helps you to become expert in deep learning and convolutional neural networks in python in theano and tensorflow. Nearly 14k+ students are enrolled for this course.
- In this course they will teach you the complete introduction of Deep learning and explains how to use deep learning for computer vision by using convolutional neural networks.
- They will train you to write code in Theano and Tensorflow and also how to run the Code using GPU.
- They will teach you what is convolution so that, You can easily understand how convolution is applied in to image effects and audio effects
- In this course, you can learn the implementation of Convolutional Neural networks in Tensorflow and Theano.
- In this course, you can know the Architecture of CNN (Convolutional Neural Networks).
- They will teach you about edge detection in code and Gaussian blur. You will also learn CNN architecture, 3D images convolution, CNN tracking shapes and you will also understand the components of CNN.
Lazy programmer Inc. is the course creator of linear regression in python. He is a big data scientist, big data engineer and full stack software engineer. He received his Master degree with the specialisation of pattern recognition and machine learning in the field of computer science engineering. He frequently uses some of the big data technologies like, hadoop, pig,spark, hive, etc. He taught data science, machine learning, algorithms etc courses for graduate and undergraduate students. This team will teach you the data science linear regression in python makes you to become a good data scientists. Nearly 160k+ students are enrolled for this course. The instructors will offers you the 5.5 hours on-demand video with full lifetime access.
- In this course you can learn the python Linear regression and you can understand how to build working program in python for data analysis.
- They will teach you how to apply the data science problems and to solve the linear regression model.
- In this course, They will explain with the real time examples of data science so that you can understand easily and you can program by your own version of linear regression Model in python.
- If you have some knowledge in basic python programming, and probability then you can understand this course in better way.
- You will also learn the demonstration of moore’s law in the code and you know how to use the matrices to solve the multiple linear regressions.
Frank Kane is the Creator of the Data science and Machine learning with python. He is the founder of Sundog education. He has experience in Big data and machine learning as a trainer. In 2012 Frank started his own company which focuses on data analysis, machine learning and Virtual reality environment Technology. Frank holds the 17 patent rights in the fields of Data Mining, Machine learning and distributed computing. He teaches you the hands-on python code examples, data science, machine learning, Artificial Intelligence, keras, tensorflow and Neural Network techniques etc. nearly 66k students are enrolled to learn this course. In this course he will offers you 12 hours on-demand video and 3 articles with full time access.
- In this course, you can understand how to use item-based and user-based collaborative filtering to build a movie recommender system.
- In this course, he will teach you how to develop data science machine learning with ipython notebooks.
- This course will helps you to learn about reinforcement learning and you can able to build a Pac-man Bot.
- Here you may learn how to make predictions using Linear regression, multivariate regression and polynomial regression.
- And also you can understand complex multilevel models,statistical measures such as standard deviation.
- By using T-tests and P values you can easily design and evaluate the A/B tests.
- You can learn the implementation of clustering, machine learning, and TF/IDF at massive scale with Apache Spark’s MLLib
The instructor of Tensorflow for deep learning is Jose Marcial Portilla. He received Bachelor’s degree and Master’s degree in mechanical engineering in the field of Data science and programming at Santa Clara University. He is experienced as a professional instructor in the field of Data science and python programming. He has patents in various fields like Microfluidics, Data science and Microscience. At present he is working as a head of Data Science for Pierian Data Inc. and provides data science and python programming training to the employees working in top most companies. In this course he will explain about how to learn Google’s Deep learning Framework and complete information about Tensorflow for deep learning with python and makes you can Become a Deep learning Guru. nearly 42k+ students are enrolled to learn this course. Jose Marcial Portilla offers 21 hours demand-video and 7 articles with full lifetime access.
- In this course, he explains about the basics of Neural Networks and Tensorflow and also you can understand how the Neural Networks are working.
- He will teach you how to use tensorflow for the classification and regression tasks and also for the classification of image with Convolutional Neural Networks.
- You can also learn how to use tensorflow for time series analysis with recurrent Neural Networks. And also tensorflow is used to solve the unsupervised problems with autoencoders.
- From this course, you can understand how to Create Generative Adversarial Networks.
- If you have some basic knowledge in math and python programming then, then it is an advantage for you to learn this Course quickly.
Lazy programmer Incorporation is the course creator of recurrent neural network in python. He is a big data scientist, full stack software engineer, and big data engineer. He received his Master degree in the field of computer science engineering with the specialisation of pattern recognition and machine learning. He taught data science, machine learning, algorithms etc courses for graduate and undergraduate students. He frequently uses some of the big data technologies like, hadoop, pig,spark, hive, etc. This team will explains you about long short-term memory unit(LSTM), gated recurrent unit (GRU), deep learning, data science and machine learning.13k+ students are enrolled to learn this course created by the Lazy programmer Inc. team. Lazy programmer Incorporation team offers 6.5 hours On-demand video with full time access.
- In this course, you will understand the concepts of Simple Recurrent Unit and Gated Recurrent Unit.
- You will understand about LSTM, backpropagation, and you can write the different recurrent Networks units in Theano.
- From this course you can understand how to use Recurrent Neural Network to generate the text.
- They will explain you to write a neural network code in Theano and Tensorflow. In this course, they will explain how to reduce the gradient problems.
- You can get the knowledge in python programming, probability, and deep learning.
- From this course you can able to solve the XOR operations and Parity checker problems from the Recurrent Neural networks.
Deep Learning is easy to learn. Above we’ve shown you some of the Online Courses. If you are interested in this, then you can choose any course which is suitable for you. After the completion of the course, you will get the Certificate with your name. By learning this course you will get jobs like deep learning trainer , machine learning engineer, data scientist, business analyst, artificial intelligence architect and more. And you can add this Certification in your resume that will help you for your Bright career. If you like this Article, please share this with your friends and Social Media. For any queries or doubts about this article you can ask in comment Section.
Best Deep Learning Books
#1 Deep Learning by Ian Goodfellow
#2 Deep Learning with Python 1st Edition by Francois Chollet
#3 Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms 1st Edition by Nikhil Buduma
#4 Deep Learning: A Practitioner’s Approach 1st Edition by Josh Patterson
#5 Deep Learning Fundamentals: An Introduction for Beginners by Chao Pan
#6 TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning 1st Edition by
Best Deep Learning Online Courses