Finding the right machine learning course can feel like a daunting task.
With so many options on Coursera, it’s hard to know where to begin.
You want a course that’s comprehensive, engaging, and taught by experts, but you also want to make sure it fits your learning style and goals.
I’ve taken a lot of courses on the platform and compiled a list of the top 12 I recommend, including my top picks for beginners and experts alike.
For the best machine learning course overall, I recommend the legendary Machine Learning Specialization By Andrew Ng.
This specialization, taught by the renowned Andrew Ng, provides a comprehensive and practical introduction to machine learning.
It’s a renewed version of the original course now using Python as the primary programming language.
It covers supervised learning, unsupervised learning, and AI best practices, making it perfect for beginners with a solid foundation in Python and basic calculus.
If this isn’t quite the right fit, don’t worry!
You have plenty of other options to choose from.
Keep reading for more recommendations for beginners, intermediate learners, and experts, as well as courses focusing on specific deep learning techniques and MLOps.
These are my personal favorites, and if you decide to take them all, I recommend taking them in this order for the best learning experience.
Machine Learning Specialization By Andrew Ng
- Provider: DeepLearning.AI, Andrew Ng, Stanford University
- Price: $49/month
- Duration: Approx. 3 months if you study 9 hours per week
- Pre-requisites: basic knowledge of calculus, linear algebra, and Python.
This is the original and highly regarded course that has been taken by some of the best data scientists I know from Kaggle. It’s perfect for beginners.
I first enrolled in an early version of this course back in 2012, and it took me three attempts to complete it as I built up the necessary background knowledge.
The course has evolved into a specialization since its 2012 launch, now using Python as the primary programming language.
It covers supervised learning, unsupervised learning, and AI best practices, providing a well-structured and practical introduction to machine learning.
By the end of the specialization, you’ll have mastered key concepts and gained the know-how to apply machine learning to real-world problems.
With Andrew Ng as the instructor, complex machine learning concepts become easily understandable.
The specialization offers well-documented examples in Jupyter Notebooks, allowing for hands-on learning and real-life application.
Striking the right balance between theory and practice, it provides a solid understanding of the key principles and methods used in machine learning without diving too deep into the mathematics.
However, if you’re seeking a more in-depth treatment of the mathematical aspects of machine learning or lack a solid foundation in Python and basic calculus, this specialization might not be suitable for you.
The specialization focuses more on applying machine learning to real-world problems, giving a superficial understanding of the underlying math, and assumes a certain level of knowledge in those areas. The specialization consists of 3 courses:
Supervised Machine Learning: Regression and Classification
In the first course of the Machine Learning Specialization, you’ll explore supervised learning, the most common type of machine learning in real-world applications.
You’ll study regression and classification, delving into techniques such as linear regression for predicting numerical values and logistic regression for predicting categories.
Applications of supervised learning include spam detection, credit scoring, image recognition, and speech recognition.
As you progress, you’ll learn how to handle overfitting using regularization, ensuring your models generalize well to real-world data.
You’ll also dive into feature engineering, a crucial skill for creating and transforming variables to improve your model’s performance.
For example, in predicting house prices, you might create a new feature called “price per square foot” to better reflect the relationship between a house’s size and price.
By understanding regression, classification, overfitting, regularization, and feature engineering, you’ll be well-equipped to excel in machine learning competitions and tackle real-world problems.
Advanced Learning Algorithms
The second course of the Machine Learning Specialization delves into advanced learning algorithms, including neural networks, decision trees, and tree ensemble methods.
Neural networks, a core component in modern machine learning, excel in domains such as image recognition and natural language processing.
Companies like Meta, Google, and Amazon use neural networks to improve their search engines and recommendation systems.
Multiclass classification, an extension of binary classification, allows for classifying objects into multiple categories, such as identifying objects in images for self-driving cars. Decision trees, random forests, and XGBoost form the foundation for powerful models used in classification and regression tasks.
Approximately 50% of machine learning competitions are won by ensembles of decision trees, making them a popular choice for practitioners working with tabular data.
I used these algorithms to win Kaggle competitions and use them every day in real-world projects.
Unsupervised Learning, Recommenders, Reinforcement Learning
In the third course of the Machine Learning Specialization, you’ll explore unsupervised learning techniques, recommender systems, dimensionality reduction, and reinforcement learning.
Unsupervised learning techniques like clustering and anomaly detection enable you to analyze and understand large datasets without labeled data, making them particularly valuable when labeled data is scarce or costly to obtain.
Recommender systems, widely used in industries like e-commerce and entertainment, provide personalized recommendations based on user preferences and behavior.
You’ll learn collaborative filtering, which leverages user-item interactions, and content-based filtering, which uses item characteristics to make recommendations.
Dimensionality reduction techniques, such as PCA, are crucial for simplifying complex datasets by transforming them into lower-dimensional spaces, helping reduce noise, minimize computational complexity, and improve other machine learning algorithms’ performance.
Lastly, you’ll delve into reinforcement learning, where algorithms learn to make decisions through interaction with an environment and receiving feedback in the form of rewards or penalties.
An example of reinforcement learning is AlphaGo, the AI that defeated the world champion in the game of Go.
By the end of the course, you will have acquired the basics of reinforcement learning, setting you on the right path for further exploration in the field.
Deep Learning Specialization
- Provider: DeepLearning.AI, Andrew Ng
- Price: $49/month
- Duration: Approx. 5 months if you study 9 hours per week
- Pre-requisites: basic linear algebra, calculus, and intermediate Python skills
After you take the Machine Learning specialization, you can take this Deep Learning specialization.
Going deep into neural networks is a natural next step after learning about traditional machine learning.
This specialization offers a series of courses that cover a wide range of essential topics, from foundational concepts to advanced techniques in deep learning.
Goes deep into not only deep learning but also has an invaluable course on how to manage and structure machine learning projects effectively, which is critical for success in the AI industry.
Lastly, the specialization dives into specialized topics like Convolutional Neural Networks (CNNs) and Sequence Models, which have numerous practical applications, such as image recognition, natural language processing, and speech recognition.
Andrew’s exceptional presenting style ensures clarity and understanding, while practical coding exercises offer hands-on experience.
Though not ideal for those already well-versed in deep learning concepts, it’s still great for refreshing knowledge and staying updated with latest developments.
Note that the course is taught using TensorFlow, so if you’re seeking a course that uses PyTorch, this might not be the right fit.
However, knowing both frameworks is advantageous, as companies have varying preferences.
The specialization consists of 5 courses:
Neural Networks and Deep Learning
Beginning with the basics of neural networks, the course progresses to cover the essential concepts that form the foundation of deep learning applications.
If you took the Machine Learning specialization, you will find this course to be a great refresher and consolidation of the concepts you learned.
One of the critical skills taught in this course is vectorization, which allows for the efficient implementation of machine learning models.
By leveraging vectorization, engineers can speed up their models significantly, making them more computationally efficient and resource-friendly.
This is vital for processing large datasets quickly and efficiently.
The course covers essential aspects of neural networks, such as forward and backpropagation, activation functions, and the importance of non-linear activation functions.
Understanding these concepts will enable you to build and train various neural networks effectively, including shallow and deep networks.
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
This course builds on the concepts covered in the previous course and focuses on the practical aspects of deep learning.
In this course, you’ll explore algorithms such as Adam, RMSprop, and the learning rate decay technique, which are designed to speed up the training process and achieve better convergence.
By incorporating these optimization methods, you’ll be able to train your models more efficiently, making the most of your computational resources and time.
Hyperparameters are the adjustable settings that directly influence the performance of your model.
In this course, you’ll learn how to systematically tune and optimize these it to maximize your model’s effectiveness.
Structuring Machine Learning Projects
As a more experienced data scientist, this was the most valuable course in the specialization for me.
I wish I had taken this course before I started my first machine learning project.
You’ll learn how to set clear goals and implement strategic guidelines that will optimize your machine learning development process.
It teaches you the amazing error analysis technique, which I use all the time to improve my models.
You’ll learn a structured approach to error analysis, which involves reviewing misclassified examples and understanding the underlying patterns that cause these errors.
This step-by-step method will empower you to identify the most prominent issues in your model and prioritize improvements based on their potential impact.
To optimize the error analysis process, it’s crucial to build an initial system rapidly and then continuously iterate and refine it
I love Andrew’s advice to “start with a quick and dirty prototype” and then “iterate and improve it.” It served me extremely well in my career.
Convolutional Neural Networks
In this comprehensive course, you’ll dive into the fascinating world of convolutional neural networks (CNNs) and learn how they revolutionized the field of computer vision.
You’ll gain hands-on experience with techniques that are essential for machine learning engineers, as you explore the true potential of CNNs in solving complex image classification and object detection problems.
Even if you don’t plan to work in computer vision, CNNs are great when you have spatial or temporal data.
I use them all the time for time series forecasting.
You’ll learn how to build the core layers of CNNs, such as pooling and convolutions, and understand how to stack them effectively to create a powerful deep network
Then you will explore groundbreaking CNN architectures like ResNets, Inception Networks, and MobileNets.
You’ll learn powerful tricks and methods used in deep CNNs, and learn how to apply transfer learning to your own projects.
Transfer learning is an incredibly powerful technique that has become indispensable in the practice of machine learning.
One of the key benefits of transfer learning is its ability to overcome the limitations posed by small or scarce datasets.
When training a deep neural network from scratch, a vast amount of data is typically required to achieve satisfactory performance.
However, transfer learning enables you to bypass this obstacle by taking advantage of the general features learned by the pre-trained model, which can be fine-tuned to suit your particular task with a significantly smaller dataset.
Sequence Models
Mastering Recurrent Neural Networks (RNNs) and its variants, such as LSTMs and GRUs, will enable you to excel in a wide range of applications, including natural language processing, speech recognition, and time series analysis.
RNNs are particularly well-suited for tasks where the order of inputs matters.
Natural language processing (NLP) with deep learning has opened up new possibilities in understanding and generating human language.
By learning word embeddings and employing embedding layers, you’ll be able to train RNNs that deliver exceptional performance across various NLP tasks, such as sentiment analysis, named entity recognition, and neural machine translation.
You’ll gain insights into popular word embedding techniques like Word2Vec and GloVe, which capture the semantic relationships between words and enable powerful language models.
You’ll learn the attention mechanism, which is a game-changing addition to sequence models, allowing your model to dynamically focus on different parts of an input sequence.
This mechanism significantly improves the performance of tasks like neural machine translation and text generation, as it helps your model to selectively weigh the importance of various inputs.
Lastly, explore the cutting-edge Transformer network, which has revolutionized NLP and outperformed traditional RNNs in many tasks.
Transformers are the technology behind the crazy success of BERT, ChatGPT, and other NLP models.
By grasping the concepts of self-attention and multi-head attention, you’ll be well-equipped to implement Transformer networks.
Machine Learning Engineering for Production (MLOps) Specialization
- Provider: DeepLearning.AI, Andrew Ng
- Price: $49/month
- Duration: Approx. 4 months if you study 5 hours per week
- Pre-requisites: basic knowledge of machine learning, intermediate Python skills and experience with a deep learning framework like TensorFlow or PyTorch
Models are born in Jupyter notebooks, but that is not where they should live.
After taking the courses above, you will have a solid foundation in machine learning and deep learning, but you will still need to learn how to deploy your models into production.
This course will take you beyond Jupyter notebooks and into the real world.
You’ll gain in-depth knowledge and hands-on experience in deploying machine learning models and managing their entire lifecycle in production, setting you apart from the competition.
The number of data scientists that finish a bootcamp without knowing how to deploy their models into production is staggering. Knowing this is a real competitive advantage.
While this specialization does have a focus on TensorFlow tools, the skills you’ll acquire give you a well-rounded understanding of MLOps.
It’s ideal for those seeking a comprehensive and structured approach to understanding and tackling various situations in an ML project lifecycle.
The focus on TensorFlow Extended (TFX) and heavy emphasis on Google’s tools and deep learning limits the exploration of other tools or frameworks, and the optional hands-on notebooks and easy quizzes might not appeal to those seeking a more challenging experience.
The specialization consists of 4 courses:
Introduction to Machine Learning in Production
In Week 1, you’ll get a solid overview of the ML lifecycle and deployment, focusing on the requirements and challenges of machine learning production systems.
This is crucial because it helps you understand the importance of robust deployments when dealing with constantly changing data, setting the foundation for your work in the field.
Week 2 is all about selecting and training a model.
You’ll learn about error analysis, strategies for working with different data types, and how to handle class imbalance and highly skewed datasets.
These techniques are essential for building effective and accurate models, giving you the ability to optimize your model’s performance, even with challenging datasets.
In Week 3, the course dives into data definition and establishing a performance baseline.
A performance baseline is a critical component of any machine learning project, as it enables you to measure the impact of your changes and track your progress.
Throughout the course, you’ll also learn about data-centric AI development, data augmentation, adding features, and experiment tracking.
Machine Learning Data Lifecycle in Production
In Week 1, the course delves into collecting, labeling, and validating data using the TensorFlow Extended (TFX) library.
This is essential as it teaches you how to prepare data to be production-ready, which is a critical skill when developing and deploying machine learning models.
Week 2 is all about feature engineering, transformation, and selection.
You’ll learn to encode structured and unstructured data types and handle class imbalances using TensorFlow Extended.
Mastering these techniques will enable you to create better features and optimize your models’ performance.
In Week 3, you’ll explore the data journey and data storage in a production system’s lifecycle.
By leveraging ML metadata and enterprise schemas, you’ll be able to adapt quickly to evolving data.
Week 4 is optional, but I highly recommend doing it, as it covers advanced labeling, augmentation, and data preprocessing techniques.
You’ll learn about semi-supervised learning, active learning, weak supervision, and data augmentation.
Active learning is one of the most fascinating and useful tools you can have in your day to day work.
These techniques will enable you to combine labeled and unlabeled data to improve your model’s accuracy and diversify your training set.
In addition, you’ll dive into working with time series data and sensor signals, enhancing your ability to handle various data types in your projects.
Machine Learning Modeling Pipelines in Production
Week 1 is about Neural Architecture Search.
You’ll learn how to effectively search for the best model architecture and hyperparameters that scales for various serving needs while constraining model complexity and hardware requirements.
This is crucial for optimizing your models to perform efficiently in production environments.
In Week 2, the focus is on Model Resource Management Techniques.
You’ll learn how to optimize and manage the compute, storage, and I/O resources your model needs in production during its entire lifecycle.
Mastering these techniques will enable you to deploy models that perform well without consuming excessive resources.
Week 3 covers High-Performance Modeling.
You’ll implement distributed processing and parallelism techniques to make the most of your computational resources for training your models efficiently.
This knowledge will help you speed up your model training process and achieve better results in less time.
In Week 4, the course dives into Model Analysis.
You’ll learn how to use model performance analysis to debug your model and measure its robustness, fairness, and stability.
This is essential for ensuring that your models are reliable, accurate, and free from biases.
Finally, Week 5 is about Interpretability.
You’ll explore model interpretability, which is the key to explaining your model’s inner workings to both laypeople and expert audiences.
It helps address regulatory and legal requirements for different use cases.
Deploying Machine Learning Models in Production
In Week 1, you’ll learn the basics of Model Serving.
Find out how to make your ML models available to users and improve the inference process.
This knowledge is key to deploying your models in the real world and making sure they work fast and accurately.
Week 2 takes you into Patterns and Infrastructure.
You’ll learn how to serve models and provide both batch and real-time results by building strong and flexible infrastructure.
In Week 3, you’ll get into Model Management and Delivery.
Learn how to set up ML processes, pipelines, and workflow automation that follow modern MLOps practices.
This skill will let you manage and check your projects throughout their entire life, making sure they are high-quality and follow the rules.
Lastly, in Week 4, you’ll learn about Model Monitoring and Logging.
Discover how to find model decay and prevent reduced accuracy in a system that is always running.
This is important because machine learning models are living creatures that need to be updated through time with new data or they start underperforming.
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