The specialization consists of 3 courses series, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
The courses are taught through a combination of video lectures, quizzes, programming assignments, and a final project. Learners will use Python programming language and the open-source machine learning library, scikit-learn, to implement the algorithms and build predictive models.
The specialization is suitable for learners with some programming experience and a basic understanding of mathematics, including linear algebra and calculus. However, the courses are designed to be accessible to learners from a wide range of backgrounds and levels of experience.
By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
Course Content:
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
Course 1: Supervised Machine Learning: Regression and Classification
This course consists of 3 weeks, as follows:
Week 1: Introduction to Machine LearningWeek 3: Classification
12 videos (Total 140 min), 1 reading, 5 quizzes
Motivations 9m
Logistic regression 9m
Decision boundary 10m
Cost function for logistic regression 11m
Simplified Cost Function for Logistic Regression 5m
Gradient Descent Implementation 6m
The problem of overfitting 11m
Addressing overfitting 8m
Cost function with regularization 9m
Regularized linear regression 8m
Regularized logistic regression 5m
Andrew Ng and Fei-Fei Li on Human-Centered AI 41m
Acknowledgments 2m
Practice quiz: Classification with logistic regression 30m
Practice quiz: Cost function for logistic regression 30m
Practice quiz: Gradient descent for logistic regression 30m
Practice quiz: The problem of overfitting 30m
Course 2: Advanced Learning Algorithms
This course consists of 4 weeks, as follows:
Week 1: Neural Networks
Welcome! 2m
Neurons and the brain 10m
Demand Prediction 16m
Example: Recognizing Images 6m
Neural network layer 9m
More complex neural networks 8m
Inference: making predictions (forward propagation) 5m
Inference in Code 6m
Data in TensorFlow 11m
Building a neural network 8m
Forward prop in a single layer 5m
General implementation of forward propagation 7m
Is there a path to AGI? 10m
How neural networks are implemented efficiently 4m
Matrix multiplication 9m
Matrix multiplication rules 9m
Matrix multiplication code 6m
Practice quiz: Neural networks intuition 10m
Practice quiz: Neural network model 10m
Practice quiz: TensorFlow implementation 10m
Practice quiz: Neural network implementation in Python 10m
Week 2: Neural network training
This week, you'll learn how to train your model in TensorFlow, and also learn about other important activation functions (besides the sigmoid function), and where to use each type in a neural network. You'll also learn how to go beyond binary classification to multiclass classification (3 or more categories). Multiclass classification will introduce you to a new activation function and a new loss function. Optionally, you can also learn about the difference between multiclass classification and multi-label classification. You'll learn about the Adam optimizer, and why it's an improvement upon regular gradient descent for neural network training. Finally, you will get a brief introduction to other layer types besides the one you've seen thus far.
15 videos (Total 140 min)
Training Details 13m
Alternatives to the sigmoid activation 5m
Choosing activation functions 8m
Why do we need activation functions? 5m
Multiclass 3m
Softmax 11m
Neural Network with Softmax output 7m
Improved implementation of softmax 9m
Classification with multiple outputs (Optional) 4m
Advanced Optimization 6m
Additional Layer Types 8m
What is a derivative? (Optional) 22m
Computation graph (Optional) 19m
Larger neural network example (Optional) 9m
Practice quiz: Neural Network Training 30m
Practice quiz: Activation Functions 30m
Practice quiz: Multiclass Classification 30m
Practice quiz: Additional Neural Network Concepts 30m
Week 3: Advice for applying machine learning
17 videos (Total 174 min)
Evaluating a model 10m
Model selection and training/cross validation/test sets 13m
Diagnosing bias and variance 11m
Regularization and bias/variance 10m
Establishing a baseline level of performance 9m
Learning curves 11m
Deciding what to try next revisited 8m
Bias/variance and neural networks 10m
Iterative loop of ML development 7m
Error analysis 8m
Adding data 14m
Transfer learning: using data from a different task 11m
Full cycle of a machine learning project 8m
Fairness, bias, and ethics 9m
Error metrics for skewed datasets 11m
Trading off precision and recall 11m
Practice quiz: Advice for applying machine learning 30m
Practice quiz: Bias and variance 30m
Practice quiz: Machine learning development process 30m
Week 4: Decision trees
14 videos (Total 144 min), 1 reading, 4 quizzes
Learning Process 11m
Measuring purity 7m
Choosing a split: Information Gain 11m
Putting it together 9m
Using one-hot encoding of categorical features 5m
Continuous valued features6mRegression Trees (optional) 9m
Using multiple decision trees 3m
Sampling with replacement 3m
Random forest algorithm 6m
XGBoost 6m
When to use decision trees 6m
Andrew Ng and Chris Manning on Natural Language Processing 47m
Acknowledgements 2m
Practice quiz: Decision trees 30m
Practice quiz: Decision tree learning 30m
Practice quiz: Tree ensembles 30m
Course 3: Unsupervised Learning, Recommenders, Reinforcement Learning
This course consists of 3 weeks, as follows:
Week 1: Unsupervised learning
13 videos (Total 120 min)
Welcome! 3m
What is clustering? 4m
K-means intuition 6m
K-means algorithm 9m
Optimization objective 11m
Initializing K-means 8m
Choosing the number of clusters 7m
Finding unusual events 11m
Gaussian (normal) distribution 10m
Anomaly detection algorithm 11m
Developing and evaluating an anomaly detection system 11m
Anomaly detection vs. supervised learning 8m
Choosing what features to use 14m
Clustering 30m
Anomaly detection 30m
Week 2: Recommender systems
15 videos (Total 150 min)
Using per-item features 11m
Collaborative filtering algorithm 13m
Binary labels: favs, likes and clicks 8m
Mean normalization 8m
TensorFlow implementation of collaborative filtering 11m
Finding related items 6m
Collaborative filtering vs Content-based filtering 9m
Deep learning for content-based filtering 9m
Recommending from a large catalogue 7m
Ethical use of recommender systems 10m
TensorFlow implementation of content-based filtering 4m
Reducing the number of features (optional) 12m
PCA algorithm (optional) 17m
PCA in code (optional) 11m
Collaborative Filtering 30m
Recommender systems implementation 30m
Content-based filtering 30m
Week 3: Reinforcement learning
What is Reinforcement Learning? 8m
Mars rover example 6m
The Return in reinforcement learning 10m
Making decisions: Policies in reinforcement learning 2m
Review of key concepts 5m
State-action value function definition 10m
State-action value function example 5m
Bellman Equation 12m
Random (stochastic) environment (Optional) 8m
Example of continuous state space applications 6m
Lunar lander 5m
Learning the state-value function 16m
Algorithm refinement: Improved neural network architecture 3m
Algorithm refinement: ϵ-greedy policy 8m
Algorithm refinement: Mini-batch and soft updates (optional) 11m
The state of reinforcement learning 2m
Summary and thank you 3m
Andrew Ng and Chelsea Finn on AI and Robotics 33m
Acknowledgments 2m
(Optional) Opportunity to Mentor Other Learners 1m
Reinforcement learning introduction 30m
State-action value function 30m
Continuous state spaces 30m
Reviews:
As a former learner of the Machine Learning Specialization on Coursera, I can attest to the high quality of the program. The specialization consists of 3 courses that cover a wide range of topics, from supervised and unsupervised learning to deep learning and neural networks. Each course is designed to build on the material covered in the previous course, providing learners with a comprehensive introduction to machine learning.
One of the strengths of the program is the quality of the teaching. Andrew Ng, the author of the course, is a renowned expert in the field of machine learning, and his lectures are engaging, clear, and informative. He has a talent for breaking down complex concepts into understandable parts, making the material accessible to learners with different levels of prior knowledge. In addition to the video lectures, the program also includes readings, quizzes, and programming assignments, which reinforce the concepts covered in the lectures and provide learners with hands-on experience working with real-world datasets.
The programming assignments and projects are a standout feature of the program. They are challenging and require learners to apply the concepts they have learned to solve real problems. The projects are particularly valuable, as they give learners the opportunity to work on larger-scale problems and apply multiple techniques and algorithms to real datasets. The feedback on assignments and projects is also very helpful, providing learners with guidance on how to improve their code and approach to problem-solving.
Another strength of the program is the community. The specialization has a large and active community of learners, with discussion forums where learners can ask questions, share their work, and collaborate on projects. The community is a valuable resource for learners, providing them with the opportunity to connect with others who are interested in machine learning and learn from their experiences.
Overall, I found the Machine Learning Specialization to be an outstanding program that provided me with a strong foundation in machine learning. The program is rigorous but accessible, and the quality of the teaching and material is exceptional. I would highly recommend this program to anyone interested in learning about machine learning, whether they are just starting out or looking to deepen their knowledge.
At this time, the course has an average rating of 4.9 out of 5 stars based on over 10,499 ratings.
What you'll learn:
Upon completion of the Machine Learning Specialization on Coursera, learners will have gained the following skills:
• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
• Build and train a neural network with TensorFlow to perform multi-class classification.
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees.
• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
• Build a deep reinforcement learning model.
Author:
Andrew Ng is a renowned computer scientist, entrepreneur, and educator who has made significant contributions to the field of artificial intelligence and machine learning.
He is the founder of the Google Brain project and co-founder of Coursera, an online education platform that offers courses and specializations in a wide range of subjects. He has also held positions as a professor of computer science at Stanford University, a research scientist at Google, and the chief scientist at Baidu, the leading Chinese search engine.
Andrew Ng is widely recognized as one of the most influential figures in the field of machine learning. He has authored numerous research papers on topics such as deep learning, computer vision, and natural language processing, and his work has been cited thousands of times in the academic literature.
In addition to his research contributions, Andrew Ng is also a highly effective educator. He has created a number of popular online courses and specializations in machine learning, including the Machine Learning Specialization on Coursera. His teaching style is highly engaging and accessible, and he has a talent for explaining complex technical concepts in a way that is easy to understand.
Overall, Andrew Ng is a highly respected figure in the field of artificial intelligence and machine learning. His contributions to research, education, and entrepreneurship have had a significant impact on the development of these fields, and his work continues to inspire and inform researchers and practitioners around the world.
Requirements:
The Machine Learning Specialization on Coursera, created by Andrew Ng, requires learners to have a solid foundation in mathematics, particularly in linear algebra, calculus, and probability theory. In addition, learners should have some experience with programming, preferably in Python.
Here are the specific requirements for the Machine Learning Specialization:
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Basic knowledge of linear algebra, calculus, and probability theory: Learners should have a good understanding of these mathematical concepts, as they form the basis for many of the algorithms and techniques used in machine learning.
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Proficiency in programming, preferably in Python: Learners should be comfortable with programming in a high-level language such as Python, as the majority of the programming assignments in the specialization are done using Python and the scikit-learn library.
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Familiarity with basic data structures and algorithms: Learners should have some knowledge of basic data structures and algorithms, such as arrays, linked lists, and sorting algorithms.
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Access to a computer with an internet connection: The specialization is entirely online, so learners will need access to a computer and a reliable internet connection.
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Time commitment: The specialization consists of five courses, each of which takes approximately four to six weeks to complete. Learners should be prepared to dedicate several hours per week to watching lectures, completing assignments, and working on projects.
Overall, the Machine Learning Specialization is a rigorous program that requires a solid foundation in mathematics and programming. However, it is designed to be accessible to learners with a wide range of backgrounds, and the online format makes it easy for learners to work at their own pace and on their own schedule.
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