

Looking to grow your skills and build a career in AI?
Join 1 million+ learners and #BeADeepLearner with the Deep Learning Specialization, a foundational online program by machine learning pioneer Andrew Ng.
You might be wondering if this is the right program for you, worried that you don’t have the time, or afraid that you won’t be able to keep up?
We understand that it can be daunting to start something new.
The Deep Learning Specialization
- Has clear, concise modules that allow for self-paced learning.
- Introduces practical techniques to help you get started on your AI projects and develop an industry portfolio.
- Has a 1 million-strong learner community that will support and guide you.
- Breaks down foundational concepts into easy-to-understand lectures and engaging assignments.
- Is up-to-date with the leading-edge in machine learning.
- Is rated 4.9 out of 5 by 120K+ learners and is among the most popular data science programs on Coursera
What Learners Are Saying
“After completing the Deep Learning Specialization, I got two promotions and an award and was able to work with the R&D team at work. I also got the opportunity to teach undergrad engineering students. These experiences, starting with DLS, have molded my career.”
“I decided to try to understand this thing called AI that everyone was talking about and ended up doing the Deep Learning Specialization. I truly believe that this program should be given to senior students at universities as they’d get a valuable picture.”
“The Deep Learning Specialization helped me build the fundamental knowledge as well as practical applications of deep learning. I think the Deep Learning Specialization is a great starting point if someone wants to get into the field.”
“The introductions to Convolutional Neural Networks, Yolo, NLP, among others, really helped me hit the ground running when I got on the job market. As I developed more experience, I transitioned from being a multi-project consultant to being the lead scientist of a startup.”
“When my role as a software engineer at a big company started feeling claustrophobic, I quit without having another job in hand and enrolled in the Deep Learning Specialization. This fueled my knowledge appetite, and today, I work as a Machine Learning Engineer at Carted.”
“After the Deep Learning Specialization, I realized that deep learning isn’t just for those with a math background and decided to become a machine learning engineer. The knowledge I’d gained helped me transition from analytics to an AI researcher role in an NLP research lab.”
“The skills I acquired after completing the Deep Learning Specialization helped me get a better job. The insights it provided into the subject matter enabled me to develop new and innovative solutions to problems at work.”
“The Deep Learning Specialization allowed me to understand diverse approaches to solve problems and helped by providing deeper insight into the field. After completing the program, I understood foundational principles better and was able to feel much more in control.”
“Within a few minutes and a couple slides, I had the feeling that I could learn any concept. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward.”
“The whole specialization was like a one-stop-shop for me to decode neural networks and understand the math and logic behind every variation of it. I can say neural networks are less of a black box for a lot of us after taking the course.”
“During my Amazon interview, I was able to describe, in detail, how a prediction model works, how to select the data, how to train the model, and the use cases in which this model could add value to the customer.”
“After completing the Deep Learning Specialization, I got two promotions and an award and was able to work with the R&D team at work. I also got the opportunity to teach undergrad engineering students. These experiences, starting with DLS, have molded my career.”
“I decided to try to understand this thing called AI that everyone was talking about and ended up doing the Deep Learning Specialization. I truly believe that this program should be given to senior students at universities as they’d get a valuable picture.”
“The Deep Learning Specialization helped me build the fundamental knowledge as well as practical applications of deep learning. I think the Deep Learning Specialization is a great starting point if someone wants to get into the field.”
“The introductions to Convolutional Neural Networks, Yolo, NLP, among others, really helped me hit the ground running when I got on the job market. As I developed more experience, I transitioned from being a multi-project consultant to being the lead scientist of a startup.”
“When my role as a software engineer at a big company started feeling claustrophobic, I quit without having another job in hand and enrolled in the Deep Learning Specialization. This fueled my knowledge appetite, and today, I work as a Machine Learning Engineer at Carted.”
“After the Deep Learning Specialization, I realized that deep learning isn’t just for those with a math background and decided to become a machine learning engineer. The knowledge I’d gained helped me transition from analytics to an AI researcher role in an NLP research lab.”
“The skills I acquired after completing the Deep Learning Specialization helped me get a better job. The insights it provided into the subject matter enabled me to develop new and innovative solutions to problems at work.”
“The Deep Learning Specialization allowed me to understand diverse approaches to solve problems and helped by providing deeper insight into the field. After completing the program, I understood foundational principles better and was able to feel much more in control.”
“Within a few minutes and a couple slides, I had the feeling that I could learn any concept. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward.”
“The whole specialization was like a one-stop-shop for me to decode neural networks and understand the math and logic behind every variation of it. I can say neural networks are less of a black box for a lot of us after taking the course.”
“During my Amazon interview, I was able to describe, in detail, how a prediction model works, how to select the data, how to train the model, and the use cases in which this model could add value to the customer.”
“After completing the Deep Learning Specialization, I got two promotions and an award and was able to work with the R&D team at work. I also got the opportunity to teach undergrad engineering students. These experiences, starting with DLS, have molded my career.”
“I decided to try to understand this thing called AI that everyone was talking about and ended up doing the Deep Learning Specialization. I truly believe that this program should be given to senior students at universities as they’d get a valuable picture.”
“The Deep Learning Specialization helped me build the fundamental knowledge as well as practical applications of deep learning. I think the Deep Learning Specialization is a great starting point if someone wants to get into the field.”
“The introductions to Convolutional Neural Networks, Yolo, NLP, among others, really helped me hit the ground running when I got on the job market. As I developed more experience, I transitioned from being a multi-project consultant to being the lead scientist of a startup.”
“When my role as a software engineer at a big company started feeling claustrophobic, I quit without having another job in hand and enrolled in the Deep Learning Specialization. This fueled my knowledge appetite, and today, I work as a Machine Learning Engineer at Carted.”
“After the Deep Learning Specialization, I realized that deep learning isn’t just for those with a math background and decided to become a machine learning engineer. The knowledge I’d gained helped me transition from analytics to an AI researcher role in an NLP research lab.”
“The skills I acquired after completing the Deep Learning Specialization helped me get a better job. The insights it provided into the subject matter enabled me to develop new and innovative solutions to problems at work.”
“The Deep Learning Specialization allowed me to understand diverse approaches to solve problems and helped by providing deeper insight into the field. After completing the program, I understood foundational principles better and was able to feel much more in control.”
“Within a few minutes and a couple slides, I had the feeling that I could learn any concept. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward.”
“The whole specialization was like a one-stop-shop for me to decode neural networks and understand the math and logic behind every variation of it. I can say neural networks are less of a black box for a lot of us after taking the course.”
“During my Amazon interview, I was able to describe, in detail, how a prediction model works, how to select the data, how to train the model, and the use cases in which this model could add value to the customer.”
Don’t Let the Machine Learning Revolution Pass You By
#BeADeepLearner with the
Deep Learning Specialization.
- 5 Courses
- 6 months (5 hours/week)
- Intermediate
Skills you will gain
- Tensorflow
- Artificial Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Transformers
- Python Programming
- Deep Learning
- Backpropagation
- Optimization
- Hyperparameter Tuning
- Machine Learning
- Transfer Learning
- Multi-Task Learning
- Object Detection and Segmentation
- Facial Recognition System
- Gated Recurrent Unit (GRU)
- Long Short Term Memory (LSTM)
- Attention Models
- Natural Language Processing
Syllabus
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.
Week 1: Introduction to Deep Learning
Understand the significant technological trends driving deep learning development and where and how it’s applied.
Week 2: Neural Networks Basics
Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models.
Week 3: Shallow Neural Networks
Build a neural network with one hidden layer using forward propagation and backpropagation.
Week 4: Deep Neural Networks
Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply them to computer vision.
In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.
By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply various optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop, and Adam, and check for their convergence; and implement a neural network in TensorFlow.
Week 1: Practical Aspects of Deep Learning
Discover and experiment with various initialization methods, apply L2 regularization and dropout to avoid model overfitting, and use gradient checking to identify errors in a fraud detection model.
Week 2: Optimization Algorithms
Develop your deep learning toolbox by adding more advanced optimizations, random mini-batching, and learning rate decay scheduling to speed up your models.
Week 3: Hyperparameter tuning, Batch Normalization, and Programming Frameworks
Explore TensorFlow, a deep learning framework that allows you to build neural networks quickly and easily and train a neural network on a TensorFlow dataset.
In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.
By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.
This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the “industry experience” that you might otherwise get only after years of ML work experience.
Week 1: ML Strategy (1)
Use a machine learning flight simulator to learn how machine learning achieves human-level performance.
Week 2: ML Strategy (2)
Become familiar with the concepts of end-to-end learning, transfer learning, and multi-task learning.
In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.
Week 1: Foundations of Convolutional Neural Networks
Implement the foundational layers of CNNs (pooling, convolutions) and stack them properly in a deep network to solve multi-class image classification problems.
Week 2: Deep Convolutional Models: Case Studies
Discover practical techniques and methods used in research papers to apply transfer learning to your own deep CNN.
Week 3: Object Detection
Apply your knowledge of CNNs to computer vision: object detection and semantic segmentation using self-driving car datasets.
Week 4: Special Applications: Face Recognition and Neural Style Transfer
Discover how CNNs can be applied to multiple fields, including art generation and face recognition, and implement your own algorithm to generate art and recognize faces.
In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.
By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering.
Week 1: Recurrent Neural Networks
Discover recurrent neural networks (RNNs) and several of their variants, including LSTMs, GRUs and Bidirectional RNNs, all models that perform exceptionally well on temporal data.
Week 2: Natural Language Processing and Word Embeddings
Use word vector representations and embedding layers to train recurrent neural networks with an outstanding performance across a wide variety of applications, including sentiment analysis, named entity recognition, and neural machine translation.
Week 3: Sequence Models and the Attention Mechanism
Augment your sequence models using an attention mechanism, an algorithm that helps your model decide where to focus its attention given a sequence of inputs, explore speech recognition and how to deal with audio data, and improve your sequence models with the attention mechanism.
Week 4: Transformers
Build the transformer architecture and tackle natural language processing (NLP) tasks such as attention models, named entity recognition (NER) and Question Answering (QA).
Course Slides
You can download the annotated version of the course slides below.