Starting Your AI Career Has Never Been Easier
#BreakIntoAI with the new Machine Learning Specialization, an updated foundational program for beginners created by Andrew Ng.
Machine Learning Specialization
About the original course
About the instructor.
A pioneer in the AI industry, Andrew Ng co-founded Google Brain and Coursera, led AI at Baidu, and has reached and impacted millions of learners with his machine learning courses.
How the Machine Learning Specialization can help you
Newly rebuilt and expanded into 3 courses, the updated Specialization teaches foundational AI concepts through an intuitive visual approach, before introducing the code needed to implement the algorithms and the underlying math.
I’m a complete beginner
- Doesn’t require prior math knowledge or a rigorous coding background
- Takes the core curriculum — vetted by millions of learners over the years — and makes it more approachable
- Each lesson begins with a visual representation of machine learning concepts, followed by the code, followed by optional videos explaining the underlying math
I enrolled in but didn’t complete the original Machine Learning course
- Balances intuition, code practice, and mathematical theory to create a simple and effective learning experience
- Includes new ungraded code notebooks with code samples and interactive graphs to help you complete graded assignments
I’ve already completed the original Machine Learning course
- Great way to refresh foundational ML concepts
- Assignments and lectures have been rebuilt to use Python rather than Octave
- The section on applying machine learning has been updated significantly based on emerging best practices from the last decade
- Not for you? Take the next step with the Deep Learning Specialization !
What Learners Are Saying
“As a Behavioral Scientist, I was able to adopt methods to understand my customers better, overcome the traditional ‘one-size-fits-all’ approach, and design interventions which account for personality and individual differences.” Luo Yuzheng Assistant Director, Monetary Authority of Singapore
“I gained confidence in my knowledge of machine learning. Since then, I’ve become a machine learning mentor, got a research paper published in IEEE, decided to pursue my Masters in Machine Learning, and was able to land a job at JP Morgan Chase.” Chirag Godawat Data Engineer, Vista
“The Machine Learning course became a guiding light. Andrew Ng explains concepts with simple visualizations and plots. I learned how to evaluate my training results and explain the outcomes to my colleagues, boss, and even the vice president of our company.” Hsin-Wen Chang Sr. C++ Developer, Zealogics
“The Machine Learning course helped develop my problem-solving skills, inspired an attitude of experimentation, and shaped a passion for machine learning. Since then, I have automated several investment processes and experimented with investment data.” Nicholas Muchinguri Senior Trader, Vanguard Australia
“The Machine Learning course by Andrew Ng expanded my knowledge, so I could write a research paper on Facial Emotion Recognition and land an internship at Morgan Stanley.” Aakash Saroop Student, K. J. Somaiya College of Engineering
“In 2017, the Machine Learning course helped me gain a deep insight into Natural Language Processing and got me thinking about a new ML project with a friend. We built an ML app with a cloud instance to enhance recorded autobiographies with NLP-generated insights!” Shahid Mahmood CTO & Co-Founder, How I Remember It, Inc.
“I’m a Computational Scientist with a Ph. D. in theoretical nuclear phsyics. I was working on a research project that involved archeological datasets that eventually led to a publication. The foundations of machine learning from Andrew’s class were essential in making it happen.” Chrysovalantis Constantinou Computational Scientist, Cyprus Institute
“Andrew’s teaching style helped me grasp foundational concepts. The newly acquired knowledge from the Machine Learning course helped me land a second postdoctoral position, run tutorials for two data science courses, and write my first machine learning paper!” Youness Boutaib Postdoctoral Researcher and Teaching assistant, RWTH Aachen University
“Enrolling in the Machine Learning course was the most consequential decision I’ve ever made. When I was facing unemployment, I turned things around by combining deep learning and Wall Street data to materialize my start-up dream!” Nektarios Kalogridis Senior Algo Trading Developer, HSBC
Created in collaboration with stanford online.
- > 2.5 months (5 hours/week)
Skills you will gain
- Linear Regression
- Logistic Regression
- Neural Networks
- Decision Trees
- Recommender Systems
- Supervised Learning
- Logistic Regression for Classification
- Gradient Descent
- Regularization to Avoid Overfitting
- Tree Ensembles
- Advice for Model Development
- Unsupervised Learning
- Anomaly Detection
- Collaborative Filtering
- Reinforcement Learning
If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new “superpower” that will let you build AI systems that just weren’t possible a few years ago.
In the first course of the Machine Learning Specialization, you will:
- 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
- Learn the difference between supervised and unsupervised learning and regression and classification tasks.
- Build a linear regression model.
- Implement and understand the purpose of a cost function.
- Implement and understand how gradient descent is used to train a machine learning model.
- Build and train a regression model that takes multiple features as input (multiple linear regression).
- Implement and understand the cost function and gradient descent for multiple linear regression.
- Implement and understand methods for improving machine learning models by choosing the learning rate, plotting the learning curve, performing feature engineering, and applying polynomial regression.
- Implement and understand the logistic regression model for classification.
- Learn why logistic regression is better suited for classification tasks than the linear regression model is.
- Implement and understand the cost function and gradient descent for logistic regression.
- Understand the problem of “overfitting” and improve model performance using regularization.
- Implement regularization to improve both regression and classification models.
In the second course of the Machine Learning Specialization, you will: 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…
In the second course of the Machine Learning Specialization, you will:
- 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.
Week 1 Learning Objective
- Build a neural network for binary classification of handwritten digits using TensorFlow.
- Gain a deeper understanding by implementing a neural network in Python from scratch.
- Optionally learn how neural network computations are “vectorized” to use parallel processing for faster training and prediction.
Week 2 Learning Objective
- Build a neural network to perform multi-class classification of handwritten digits in TensorFlow, using categorical cross-entropy loss functions and the softmax activation.
- Learn where to use different activation functions (ReLu, linear, sigmoid, softmax) in a neural network, depending on the task you want your model to perform.
- Use the advanced “Adam optimizer” to train your model more efficiently.
Week 3 Learning Objective
- Discover the value of separating your data set into training, cross-validation, and test sets.
- Choose from various versions of your model using a cross-validation dataset, and evaluate its ability to generalize to real-world data using a test dataset.
- Use “learning curves” to determine if your model is experiencing high bias or high variance (or both), and learn which techniques to apply (regularization, adding more data, adding or removing input features) to improve your model’s performance.
- Learn how the “bias-variance trade-off” is different in the age of deep learning, and apply Andrew Ng’s advice for handling bias and variance when training neural networks.
- Learn to apply the “iterative loop” of machine learning development to train, evaluate, and tune your model.
- Apply “data-centric AI” to not only tune your model but tune your data (using data synthesis or data augmentation) to improve your model’s performance.
Week 4 Learning Objective
- Build decision trees and tree ensembles, such as random forest and XGBoost (boosted trees) to make predictions.
- Learn when to use neural network or tree ensemble models for your task, as these are the two most commonly used supervised learning models in practice today.
In the third course of the Machine Learning Specialization, you will: 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…
In the third course of the Machine Learning Specialization, you will:
- 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.
- Implement K-mean clustering.
- Implement anomaly detection.
- Learn how to choose between supervised learning or anomaly detection to solve certain tasks.
- Build a recommender system using collaborative filtering.
- Build a recommender system using a content-based deep learning method.
- Build a deep reinforcement learning model (Deep Q Network).
Course 1 : Supervised Machine Learning: Regression and Classification
Course 2 : advanced learning algorithms, course 3 : unsupervised learning and recommender systems, course slides.
You can download the annotated version of the course slides below.
Frequently Asked Questions
Is Coursera's Machine Learning Specialization Worth It In 2024? Review
- What You Will Learn
- How Long Does It Take To Complete The Machine Learning Specialization?
- How Much Does The Machine Learning Specialization Cost?
- Is The Machine Learning Specialization Part Of Coursera Plus?
- What Is The Difference Between A Coursera Course And A Specialization?
- How To Get The Machine Learning Specialization For Free?
- Provider : DeepLearning.AI, Stanford University
- Teacher : Andrew Y. Ng
- Price : $49/month with a 7-day free trial
- Duration : Approx. 3 months if you study 9 hours per week
- Pre-requisites : basic Python programming skills, high school math
- Level : Beginner
- Certificate : Yes
Stepping into the world of machine learning can be daunting, especially when you’re trying to decipher complex topics like regression models, learning algorithms and more.
You could spend hours sifting through information online, but how can you know what’s reliable and what’s not?
The “Machine Learning Specialization” is an evolution of the OG Machine Learning course by Andrew Ng, which I and many of the best data scientists I know have taken to kickstart our careers.
These courses have proven to be a great asset for beginners and professionals alike, breaking down complex topics into manageable chunks.
While some might argue that the courses could offer a bit more depth in certain areas, the clear and comprehensive teaching style of Professor Andrew Ng combined with hands-on practice and interactive elements results in a highly engaging and effective learning experience.
These courses are not just about acquiring technical knowledge, they also focus on industry best practices and applied learning - a critical factor for those looking to break into the field of machine learning.
A Coursera Specialization is a series of courses that helps you master a skill.
Let’s dive deeper into the courses and see what they have to offer.
Buying a course through these links won’t cost you extra, but it’ll help keep our site ad-free. Thanks for the support!
Course 1: Supervised Machine Learning: Regression and Classification #
- Rating : 4.9/5 with 11.493 ratings
- Duration : Approx. 33 hours
I really liked this course and think it’s a great way for anyone to start learning about this topic.
Even if you’re not good at math or computer science, the course makes the tough topics easier to understand.
If you know a bit about calculus and Python, that could help you get more out of the course.
The teacher, Professor Andrew Ng, does a great job breaking down hard ideas into smaller parts that are easier to understand.
The course teaches you both the ideas behind machine learning and gives you hands-on practice to apply what you learn.
You can use Jupyter Notebooks to see examples and write your own code, which can help you really understand the topic.
The course gives you the important info you need without wasting your time, which I think is great.
But it doesn’t go into a lot of detail on some things, like why you would pick a certain loss function for logistic regression.
If you’re just starting out, you might need to look up more info about these topics.
The assignments and labs in the course might not be super challenging, but they help you practice what you’re learning.
Also, even though the course says the labs are optional, you do have to do some coding assignments. This might be a bit confusing at first.
All in all, this course is a really good way to start learning about machine learning.
If you’re interested in machine learning, I’d definitely recommend checking out this course.
What You Will Learn #
- Introduction to machine learning, including its applications and differences between supervised and unsupervised learning.
- Detailed understanding of linear regression models, including the cost function formula and intuition behind it.
- Practical experience with implementing gradient descent and understanding the learning rate.
- Extension of linear regression to handle multiple input features and methods for improving model’s training and performance.
- Incorporation of vectorization, feature scaling, feature engineering and polynomial regression for advanced model building.
- Introduction to another type of supervised learning, classification using logistic regression model.
- Understanding of the overfitting problem in machine learning models and methods to handle it using regularization.
- Practical implementation of logistic regression with regularization to tackle overfitting.
Course 2: Advanced Learning Algorithms #
- Rating : 4.9/5 with 2.880 ratings
- Duration : Approx. 34 hours
I found this course to be quite insightful. Despite its name, the course is surprisingly beginner-friendly.
Andrew Ng’s teaching style is both intuitive and engaging, making it easier for newcomers to grasp complex concepts.
He effectively demystifies machine learning concepts, making the learning process much less daunting than you’d expect.
The course provides a comprehensive overview of theory content.
It covers principal supervised learning algorithms and even includes hands-on coding components, giving you a taste of practical application.
The mathematical concepts tied to machine learning are often quite intricate, but this course does an excellent job of explaining them through exercises that help you visualize and understand them.
Furthermore, the course touches on real-world applications and industry-related machine learning libraries, providing you with knowledge that’s not just academic, but also practical.
There are, however, a few areas the course could improve on.
Although the assignments help to reinforce what you learn, some students find them too easy.
The course might benefit from more challenging assignments that require a deeper application of the concepts learned.
In addition, while the course does a great job of simplifying complex concepts, some students feel that a bit more mathematical detail, such as calculations for backpropagation, would further enhance their understanding.
But overall, as a seasoned machine learning expert, I would highly recommend this course to beginners interested in the field.
- Understand the structure and function of artificial neural networks and how to use them for classification tasks, including coding your own neural network in Python.
- Learn to use the TensorFlow framework for building artificial neural networks and implementing efficient computations using parallel processing.
- Gain knowledge on how to train models in TensorFlow and explore alternative activation functions beyond the sigmoid function.
- Discover how to extend binary classification to multiclass classification and differentiate between multiclass and multi-label classification.
- Investigate the Adam optimizer and its improvement upon regular gradient descent for neural network training .
- Get introduced to various layer types and understand their different utilities.
- Learn best practices for training and evaluating learning algorithms to improve performance and understand the lifecycle of a machine learning model.
- Develop skills to diagnose bias and variance and learn techniques for model selection and performance evaluation.
- Discover decision trees as a practical and commonly used learning algorithm, along with its variations like random forests and boosted trees (XGBoost).
- Understand when and how to use decision trees and their applications.
Course 3: Unsupervised Learning, Recommenders, Reinforcement Learning #
- Rating : 4.9/5 with 1.413 ratings
- Duration : Approx. 27 hours
This course offers an excellent introduction to advanced topics that manages to be both comprehensive yet accessible to beginners.
The video explanations are detailed and well-prepared, providing broad exposure to a variety of machine learning problems and potential solutions.
It’s truly a general introduction that doesn’t overwhelm but still manages to be thorough.
One aspect of this course that truly stands out is the interactive element.
Quizzes, hands-on exercises, and discussion forums really promote dynamic learning.
Although the math part can be a bit challenging, the course makes it fun and intuitive to learn about machine learning.
It’s not just about technical knowledge; the course also introduces industry practices and an applied approach which I believe is critical for anyone looking to work in the field of machine learning.
That being said, there are a few areas that could use some improvement.
The unit tests in practice exercises can be frustrating and there is a lack of depth in certain areas.
It’s definitely a course I’d recommend for both seasoned professionals and beginners alike.
- Understand the concept of clustering and anomaly detection in unsupervised learning.
- Learn about the K-means intuition and K-means algorithm along with its optimization objective.
- Study how to initialize K-means and choose the correct number of clusters.
- Understand the Gaussian (normal) distribution and its role in anomaly detection.
- Learn to develop and evaluate an anomaly detection system.
- Gain knowledge on making recommendations using per-item features and collaborative filtering algorithms.
- Discover the difference between collaborative filtering and content-based filtering.
- Learn about reinforcement learning and its application in landing a virtual lunar lander on Mars.
- Understand the role of the state-action value function and the Bellman Equation in reinforcement learning.
- Apply your learnings by refining neural network architecture and implementing ε-greedy policy in reinforcement learning.
Frequently Asked Questions #
How long does it take to complete the machine learning specialization #.
It’s estimated to take approximately 3 months to complete if you study around 9 hours per week.
This means the total duration of the specialization is roughly 108 hours.
However, the actual time it takes can vary depending on your prior knowledge, learning pace, and the amount of time you can dedicate to the courses each week.
How Much Does The Machine Learning Specialization Cost? #
The Machine Learning Specialization on Coursera costs $49 per month.
This fee gives you access to all the course materials, graded assignments, and a certificate upon completion.
The specialization also offers a 7-day free trial, allowing you to explore the course content before committing to the monthly fee.
Please note that prices may vary depending on your location and any promotions Coursera may be running at the time.
Is The Machine Learning Specialization Part Of Coursera Plus? #
No. Coursera Plus subscribers do not have access to the Machine Learning Specialization.
What Is The Difference Between A Coursera Course And A Specialization? #
A Coursera course is a single, standalone module that focuses on a specific topic. It includes video lectures, readings, quizzes, and sometimes a final project or exam.
Once you complete a course, you receive a certificate of completion.
On the other hand, a Coursera Specialization is a series of related courses designed to help you master a specific topic.
Specializations are structured to provide a deeper, more comprehensive understanding of a subject area.
They often culminate in a capstone project that allows you to apply what you’ve learned throughout the courses.
Once you complete all the courses in a Specialization, you receive a Specialization certificate.
So, the main difference is that a course is a single module on a specific topic, while a Specialization is a series of related courses designed to provide a comprehensive understanding of a broader subject area.
How To Get The Machine Learning Specialization For Free? #
Coursera offers financial aid for students who cannot afford the fee.
You can apply for financial aid by clicking on the “Financial aid available” link next to the “Enroll” button on the course page.
You’ll need to fill out an application and wait for approval, which can take up to 15 days.
Also, Coursera occasionally offers promotions or discounts, so it’s worth checking their website regularly.
Please note that while you may be able to access some course materials for free during a trial period, you typically need to pay for the courses to receive a certificate.
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Programming assignments and quizzes from all courses within the Machine Learning Engineering for Production (MLOps) specialization offered by deeplearning.ai
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Machine Learning Engineering for Production (MLOps) Specialization on Coursera (offered by deeplearning.ai)
Programming assignments from all courses in the Coursera Machine Learning Engineering for Production (MLOps) Specialization offered by deeplearning.ai .
The GAN Specialization on Coursera contains three courses:
Course 1: Introduction to Machine Learning in Production
Course 2: machine learning data lifecycle in production, course 3: machine learning modeling pipelines in production.
Course 4: Deploying Machine Learning Models in Production
Why this Specialization?
Become a Machine Learning expert. Productionize your machine learning knowledge and expand your production engineering capabilities.
Skills: Managing Machine Learning Production Systems, Deployment Pipelines, Model Pipelines, Data Pipelines, Machine Learning Engineering for Production, Human-level Performance (HLP), Concept Drift, Model Baseline, Project Scoping and Design, ML Deployment Challenges, ML Metadata, Convolutional Neural Network
- Some knowledge of AI / deep learning
- Intermediate skills in Python
- Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
About this Specialization
Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well.
Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles.
The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.
In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.
Applied Learning Project
By the end, you'll be ready to:
- Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements
- Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application
- Build data pipelines by gathering, cleaning, and validating datasets
- Implement feature engineering, transformation, and selection with TensorFlow Extended
- Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas
- Apply techniques to manage modeling resources and best serve offline/online inference requests
- Use analytics to address model fairness, explainability issues, and mitigate bottlenecks
- Deliver deployment pipelines for model serving that require different infrastructures
- Apply best practices and progressive delivery techniques to maintain a continuously operating production system
- The Machine Learning Project Lifecycle
- Selecting and Training a Model
- Modeling challenges
- A journey through Data
- Data Definition and Baseline
- Data Labeling , PDF
- TFDV Exercise , PDF
- Data Validation , PDF
- Simple Feature Engineering , PDF
- Feature Engineering Pipeline , PDF
- Feature Selection , PDF
- Feature Engineering
- ML Metadata , PDF
- Iterative Schema , PDF
- Data Pipeline Components for Production ML
- Feature Engineering with Weather Data
- Feature Engineering with Accelerometer Data , PDF
- Feature Engineering with Images , PDF
- Hyperparameter Tuning and Neural Architecture Search
- Intro to Keras Tuner
- Hyperparameter tuning and model training with TFX
- Dimensionality Reduction
- Quantization and Purning
- Manual Dimensionality
- Quantization and Pruning
- Distributed Strategies with TF and Keras
- Knowledge Distillation
- High-Performance Modeling
- TensorFlow Model Analysis
- Model Analysis with TFX Evaluator
- Fairness Indicators
- Model Analysis
- Model Analysis and Debugging
- Continuous Evaluation and Monitoring
- Shapley Values
- Permutation Feature Importance
- Explainable AI
- Understanding Model Predictions
Course 4: Welcome to Deploying Machine Learning Models in Production
- Labs - Intro to Docker and installation - First look at Tensorflow Serving with Docker - Serve a model with TensorFlow Serving
- Quiz - Introduction to Model Serving - Introduction to Model Serving Infrastructure - TensorFlow Serving
- Model serving architecture
- Scaling Infrastructure
- Online Inference
- Data Preprocessing
- Batch inference scenarios
- Batch Processing with ETL
- FastAPI Docker
- Intro to Kubernetes
- Latency Test Compose
- Intro to KFP
- TFX Custom Components
- TFS Model Versioning
- Github Actions
- ML Experiments Management and Workflow Automation
- MLOps Methodology.
- Model Management and Deployment Infrastructure
- Model Monitoring and Logging
- Model Decay
- GDPR and Privacy
I recognize the hard time people spend on building intuition, understanding new concepts and debugging assignments. The solutions uploaded here are only for reference . They are meant to unblock you if you get stuck somewhere. Please do not copy any part of the code as-is (the programming assignments are fairly easy if you read the instructions carefully). Similarly, try out the quizzes yourself before you refer to the quiz solutions.
- Jupyter Notebook 100.0%