21 Best Machine Learning Courses to Build New Skills in 2024

Machine learning is an essential competency for any tech leader today. But getting started can feel overwhelming: How do you move beyond the basics to tackle real-world challenges like overfitting models, deploying solutions at scale, or handling imbalanced datasets? Every professional working with machine learning eventually faces these hurdles, and finding the right learning resources is key to overcoming them.

Whether you’re looking to fine-tune your knowledge of neural networks, master deployment techniques for machine learning models, or understand best practices for dealing with data quality issues, a targeted course can make all the difference. In this guide, we’ll explore the best machine learning courses—carefully curated to help technical leaders navigate the complexities of machine learning and build actionable skills that lead to real-world success.

Here are some of the top machine learning courses that cover critical concepts, hands-on applications, and advanced strategies for anyone looking to deepen their expertise in artificial intelligence.

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Best Machine Learning Courses Shortlist

Here's my shortlist of the best machine learning courses I think are worth your time in 2024:

1. Machine Learning Specialization (Stanford University)
2. Data Science: Machine Learning (Harvard University)
3. Machine Learning for Everyone (DataCamp)
4. Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024] (Udemy)
5. Machine Learning in Business (MIT Professional Education)
6. Deep Learning Specialization (DeepLearning.AI)
7. NLP with Python for Machine Learning Essential Training (LinkedIn Learning)
8. Machine Learning on Google Cloud Specialization (Google Cloud)
9. Machine Learning with Python: Foundations (LinkedIn Learning)
10. Machine Learning Fundamentals in Python (DataCamp)
11. Machine Learning Algorithms (Great Learning)
12. Advanced Machine Learning on Google Cloud Specialization (Google Cloud) 
13. Building Recommender Systems with Machine Learning and AI (LinkedIn Learning) 
14. Fundamentals of Machine Learning (Microsoft Learn) 
15. Machine Learning & AI Foundations: Linear Regression (LinkedIn Learning)
16. Machine Learning: Fundamentals and Algorithms (CMU) 
17. Artificial Intelligence Foundations: Machine Learning (LinkedIn Learning) 
18. End-to-End Machine Learning with TensorFlow on GCP Course (Google Cloud) 
19. IBM Machine Learning Professional Certificate (IBM)
20. Machine Learning and AI with Python (Harvard University)
21. Google Cloud Big Data and Machine Learning Fundamentals (Google Cloud) 

Find more details about each course below.

Best Machine Learning Courses Overviews

1. Machine Learning Specialization (Stanford University)

This course provides a foundational introduction to machine learning through three courses designed to build real-world AI applications. Taught by Andrew Ng, it covers the essentials of supervised, unsupervised, and reinforcement learning with a focus on practical model development

• Who It’s For: Individuals interested in learning the fundamentals of machine learning
• Topics Covered:
  • Machine learning models using NumPy and scikit-learn
  • Supervised learning with linear and logistic regression
  • Neural networks and multi-class classification with TensorFlow
  • Decision trees and ensemble methods
  • Unsupervised learning, including clustering and anomaly detection
  • Recommender systems using collaborative filtering and deep learning
  • Deep reinforcement learning models 

• Online, In-Person, or Both?: Online
• Exam Required?: No
• Duration: 2 months 
• How Many Hours Of Instruction: 10 hours per week (self-paced)
• Eligibility Requirements: Knowledge of basic coding and high-school-level math
• Price: Enroll for free 
• Take The Course: Coursera

2. Data Science: Machine Learning (Harvard University)

This course is part of Harvard’s Professional Certificate in Data Science and focuses on machine learning techniques for building predictive models using datasets. It includes hands-on experience creating a movie recommendation system to understand key algorithms, regularization, and cross-validation.

• Who It’s For: Beginners interested in data science and machine learning
• Topics Covered:
  • Basics of machine learning
  • Popular machine learning algorithms
  • Cross-validation techniques
  • Importance of regularization
  • Building a recommendation system
• Online, In-Person, or Both?: Online
• Exam Required?: No
• Duration: 8 weeks
• How Many Hours Of Instruction: 2 to 4 hours per week (self-paced)
• Eligibility Requirements: None
• Price:
  • Without Certificate: Free
  • With Certificate: $149
• Take The Course: Harvard

3. Machine Learning for Everyone (DataCamp)

This course is a comprehensive introduction to machine learning and covers key areas such as supervised, unsupervised, and deep learning. It provides tutorials and hands-on exercises to understand concepts like clustering, neural networks, model evaluation, and insights into how machine learning is used in various industries. 

• Who It’s For: Individuals with no prior experience in machine learning
• Topics Covered:
  • Understanding machine learning
  • Introduction to Python and its functions
  • Supervised learning with scikit-learn
  • Introduction to deep learning with PyTorch
  • Reinforcement learning in Python 
• Online, In-Person, or Both?: Online
• Exam Required?: No
• Duration: 113 hours
• How Many Hours Of Instruction: Self-paced
• Eligibility Requirements: None
• Price: $13 for DataCamp individual subscription
• Take The Course: DataCamp

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4. Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024] (Udemy)

This course provides a comprehensive introduction to machine learning with step-by-step guidance on creating algorithms in both Python and R, integrating case studies for real-world applications. It includes downloadable code templates for each project, allowing learners to practice and build their models effectively.

• Who It’s For: Beginners to intermediate learners in machine learning
• Topics Covered:
  • Classification techniques
  • Clustering methods
  • Natural language processing
  • Dimensionality reduction
  • Model selection and boosting 
• Online, In-Person, or Both?: Online
• Exam Required?: No
• Duration: 42 hours and 48 minutes 
• How Many Hours Of Instruction: Self-paced
• Eligibility Requirements: High school-level math
• Price:
  • Udemy Subscription: $20 
  • One-time Payment: $149.99
• Take The Course: Udemy

5. Machine Learning in Business (MIT Professional Education)

This course explores the fundamentals of Machine Learning from a business perspective, equipping participants to understand its practical applications and strategic value. Developed by MIT’s Sloan School of Management and Computer Science and Artificial Intelligence Laboratory (CSAIL), it focuses on integrating Machine Learning insights for impactful decision-making.  

• Who It’s For: Business professionals interested in applying machine learning
• Topics Covered:
  • Machine Learning fundamentals for business applications
  • Understanding data-driven decision processes
  • Limitations and scope of Machine Learning in business
  • Integrating AI in business strategy
  • Case studies on Machine Learning’s impact in various industries
• Online, In-Person, or Both?: Online
• Exam Required?: No
• Duration: 6 weeks
• How Many Hours Of Instruction: 4-6 hours per week (self-paced)
• Eligibility Requirements: None
• Price: $3,500
• Take The Course: MIT Professional Education

6. Deep Learning Specialization (DeepLearning.AI)

This course covers essential architectures like CNNs, RNNs, LSTMs, and Transformers. It teaches theoretical and practical applications, such as speech recognition and machine recognition, through Python and TensorFlow and emphasizes best practices for training models and understanding complex machine learning setups. 

• Who It’s For: Those interested in deep learning and neural networks
• Topics Covered:
  • Fundamentals of neural networks
  • Best practices for developing test sets
  • Diagnosing errors in systems
  • Sequence models 
• Online, In-Person, or Both?: Online
• Exam Required?: No
• Duration: 3 months
• How Many Hours Of Instruction: 10 hours per week (self-paced)
• Eligibility Requirements:
  • Intermediate Python skills
  • Basic knowledge of algebra and ML
• Price: Free
• Take The Course: Coursera

7. NLP with Python for Machine Learning Essential Training (LinkedIn Learning)

This course equips learners with practical skills in cleaning, processing, and analyzing unstructured text data. It provides foundational NLP concepts, advanced text cleaning, and vectorization techniques, leading to the development of machine learning classifiers. It also explores building and evaluating two types of machine learning models and learning how to test model variation effectively. 

• Who It’s For: Developers and data scientists interested in NLP
• Topics Covered:
  • Basics of NLP
  • Advanced data cleaning techniques
  • Text vectorization methods
  • Assessing model accuracy and effectiveness 
• Online, In-Person, or Both?: Online
• Exam Required?: No
• Duration: 4 hours and 14 minutes 
• How Many Hours Of Instruction: Self-paced
• Eligibility Requirements: None
• Price:
  • Career: $39.99 
  • Learning for Teams: $31.67 per user
• Take The Course: LinkedIn Learning

8. Machine Learning on Google Cloud Specialization (Google Cloud)

This course discusses machine learning fundamentals and teaches how to build, train, and deploy models on Google Cloud using Vertex AI without coding. It covers building AutoML models, creating BigQuery ML models with SQL, and managing data with Feature Store. 

• Who It’s For: Individuals interested in cloud-based machine learning
• Topics Covered:
  • Google’s machine-learning approach
  • Understanding data to AI lifecycle
  • Data preparation and exploration techniques
  • Fundamentals of TensorFlow
  • Hyperparameter tuning using Vertex Vizier 
• Online, In-Person, or Both?: Online
• Exam Required?: No
• Duration: 1 month
• How Many Hours Of Instruction: 10 hours per week (self-paced)
• Eligibility Requirements: None
• Price: Free
• Take The Course: Coursera

9. Machine Learning with Python: Foundations (LinkedIn Learning)

This course covers machine learning fundamentals using Python. It discusses how machines learn, the types of learning, and the steps to collect, understand, and prepare data for analysis. It includes guided Python examples for each step and building and interpreting a machine-learning model. 

• Who It’s For: Beginners in machine learning using Python
• Topics Covered:
  • Introduction to machine learning and various types
  • Techniques for data collection and preparation
  • Analyzing data for pattern and insights
  • Developing machine learning models 
• Online, In-Person, or Both?: Online
• Exam Required?: No
• Duration: 1 hour and 54 minutes
• How Many Hours Of Instruction: Self-paced
• Eligibility Requirements: None
• Price:
  • Career: $39.99 
  • Learning for Teams: $31.67 per user 
• Take The Course: LinkedIn Learning

10. Machine Learning Fundamentals in Python (DataCamp)

This course introduces machine learning fundamentals using Python and covers essential concepts in neural networks and deep learning with PyTorch. It discusses supervised learning via scikit-learn and unsupervised learning to cluster and visualize data using scipy and reinforcement learning applications. 

• Who It’s For: Individuals new to machine learning with Python
• Topics Covered:
  • Supervised and unsupervised learning
  • Introduction to deep learning with PyTorch
  • Reinforcement learning with gymnasium in Python
• Online, In-Person, or Both?: Online
• Exam Required?: No
• Duration: 16 hours 
• How Many Hours Of Instruction: Self-paced
• Eligibility Requirements: None
• Price: $13 for DataCamp individual subscription
• Take The Course: DataCamp

11. Machine Learning Algorithms (Great Learning)

This course introduces fundamental machine learning algorithms, providing both a theoretical and practical understanding of key techniques. It includes Python-based demonstrations to solidify concepts and practical skills in supervised and unsupervised learning.

• Who It’s For: Individuals interested in understanding machine learning algorithms
• Topics Covered:
  • Introduction to Machine Learning
  • Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
  • Linear Regression
  • Naive Bayes Algorithm
  • K-Nearest Neighbors (KNN)
  • Support Vector Machines (SVM)
  • Random Forest Algorithm
• Online, In-Person, or Both? Online
• Exam Required? No
• Duration: 1 hour and 30 minutes 
• How Many Hours Of Instruction: 1 hour and 30 minutes 
• Eligibility Requirements: Basic computer literacy; familiarity with Python and math
• Price: Free
• Take The Course: Great Learning

12. Advanced Machine Learning on Google Cloud Specialization (Google Cloud)

This specialization focuses on advanced machine learning topics, teaching learners how to optimize, deploy, and scale production-ready models for structured data, images, and natural language. It integrates hands-on labs through Qwiklabs, where learners can apply concepts using Google Cloud tools. 

• Who It’s For: Professionals seeking advanced knowledge in machine learning on Google Cloud
• Topics Covered:
  • Designing scalable machine learning systems
  • Computer vision fundamentals with Google Cloud 
  • Natural language processing on Google Cloud
  • Components of recommendation systems
• Online, In-Person, or Both? Online
• Exam Required? None
• Duration: 2 months 
• How Many Hours Of Instruction: 10 hours per week (self-paced)
• Eligibility Requirements: Designed for industry professionals 
• Price: Free
• Take The Course: Coursera

13. Building Recommender Systems with Machine Learning and AI (LinkedIn Learning)

This course provides hands-on training in designing recommender systems, covering key techniques like collaborative filtering, matrix factorization, and deep learning. It offers practical experience using frameworks and tools such as TensorFlow and AWS SageMaker to build scalable recommendation models. 

• Who It’s For: Developers and data scientists interested in recommender systems
• Topics Covered:
  • Introduction to recommender systems
  • Collaborative filtering techniques
  • Matrix factorization methods
  • Approaches to deep learning
  • Manage large-scale data 
• Online, In-Person, or Both? Online
• Exam Required? No
• Duration: 9 hours and 5 minutes
• How Many Hours Of Instruction: Self-paced
• Eligibility Requirements: None
• Price:
  • Career: $39.99 
  • Learning for Teams: $31.67 per user 
• Take The Course: LinkedIn Learning

14. Fundamentals of Machine Learning (Microsoft Learn)

This course introduces the foundational concepts of machine learning, focusing on key principles and model evaluation methods. It includes hands-on practice with automated machine learning using the Azure Machine Learning service.

• Who It’s For: Beginners wanting to learn machine learning fundamentals
• Topics Covered:
  • Core concepts of machine learning
  • Types of machine learning
  • Training and evaluating machine learning models
  • Introduction to deep learning
  • Automated machine learning with Azure 

• Online, In-Person, or Both? Online
• Exam Required? No
• Duration: 1 hour and 56 minutes 
• How Many Hours Of Instruction: 1 hour and 56 minutes 
• Eligibility Requirements:
  • Basic knowledge of mathematics
  • Familiarity with Microsoft Azure and cloud computing
• Price: Free
• Take The Course: Microsoft Learn

15. Machine Learning & AI Foundations: Linear Regression (LinkedIn Learning)

This course provides a comprehensive overview of linear regressions, focused on real-world applications such as predicting housing values, customer spending, and stock prices. It covers essential techniques in simple and multiple linear regressions and understanding regression concepts rather than software mechanics with SPSS insights. 

• Who It’s For: Anyone interested in understanding linear regression
• Topics Covered:
  • Introduction to linear regression
  • Building and interpreting scatter plots
  • Approaches to building regression models
  • Alternatives to regression
• Online, In-Person, or Both? Online
• Exam Required? No
• Duration: 4 hours and 5 minutes 
• How Many Hours Of Instruction: Self-paced
• Eligibility Requirements: None
• Price:
  • Career: $39.99 
  • Learning for Teams: $31.67 per user 
• Take The Course: LinkedIn Learning

16. Machine Learning: Fundamentals and Algorithms (CMU)

This course offers in-depth instruction on the technical foundations and algorithms of machine learning, focusing on prediction, classification, and optimization techniques. It combines mathematical theory with practical coding exercises to build skills applicable to healthcare and data analysis.

• Who It’s For: Professionals with Python experience who want to deepen their understanding of machine learning and its mathematical foundations
• Topics Covered:
  • Fundamentals of machine learning methods
  • Prediction and classification models
  • Regression and clustering algorithms
  • Probability, statistics, and optimization techniques
  • Applications in healthcare and data-driven analysis
• Online, In-Person, or Both? Online
• Exam Required? Yes
• Duration: 10 weeks 
• How Many Hours Of Instruction: 5 to 10 hours per week 
• Eligibility Requirements:
  • Experience with Python programming 
  • Knowledge of high-school-level linear algebra, calculus, probability and statistics
• Price: $2,500
• Take The Course: CMU

17. Artificial Intelligence Foundations: Machine Learning (LinkedIn Learning)

This course introduces the machine learning lifecycle, guiding learners through sourcing and preparing data, selecting algorithms, and training models. It details key machine learning methods and emphasizes practical skills in evaluating model performance using standard metrics without any required prerequisites. 

• Who It’s For: Individuals new to AI and machine learning
• Topics Covered:
  • Introduction to machine learning
  • Types of machine learning
  • Machine learning algorithms
  • Data preparation and feature engineering
  • Ethical considerations in machine learning 
• Online, In-Person, or Both? Online
• Exam Required? No
• Duration: 1 hour and 50 minutes 
• How Many Hours Of Instruction: 1 hour and 50 minutes 
• Eligibility Requirements: None
• Price:
  • Career: $39.99 
  • Learning for Teams: $31.67 per user 
• Take The Course: LinkedIn Learning

18. End-to-End Machine Learning with TensorFlow on GCP Course (Google Cloud)

This course provides an interactive, hands-on approach to building an end-to-end machine learning pipeline with TensorFlow on the Google Cloud Platform. It guides participants from data exploration through model deployment to real-time prediction generation.

• Who It’s For: Developers and machine learning engineers
• Topics Covered:
  • Data exploration and preprocessing
  • Model training with TensorFlow
  • Model deployment on Google Cloud Platform
  • Real-time prediction setup
  • Workflow automation for ML models
• Online, In-Person, or Both? Online
• Exam Required? No
• Duration: 3 hours and 15 minutes 
• How Many Hours Of Instruction: Self-paced
• Eligibility Requirements: Basic knowledge of TensorFlow
• Price: $29 for Pluralsight subscription 
• Take The Course: Pluralsight

19. IBM Machine Learning Professional Certificate (IBM)

This course provides training in machine learning using open-source tools and libraries, covering a comprehensive range of algorithms and techniques for real-world applications. It includes guided labs, projects, and a capstone to build job-ready skills in machine learning. 

• Who It’s For: Aspiring machine learning professionals
• Topics Covered:
  • Supervised and unsupervised learning
  • Regression and classification models
  • Clustering techniques
  • Decision trees and ensemble learning
  • Dimensionality reduction
  • Deep learning and reinforcement learning
  • Exploratory data analysis and feature engineering
• Online, In-Person, or Both? Online
• Exam Required? Yes
• Duration: 3 months 
• How Many Hours Of Instruction: 10 hours per week (self-paced)
• Eligibility Requirements: None
• Price: Enroll for Free 
• Take The Course: Coursera

20. Machine Learning and AI with Python (Harvard University)

This course features foundational skills that expand into advanced algorithms like bagging, random forests, and gradient boosting. Through real-world data application, it guides learners to analyze processes, evaluate results, and measure the effectiveness of machine learning techniques. Additionally, it emphasizes testing predictions and analyzing outcomes to prevent overfitting. 

• Who It’s For: Individuals interested in leveraging Python for machine learning
• Topics Covered:
  • Understanding decision tree algorithms
  • Exploring bagging and random forests
  • Techniques for predictive performance
  • Model evaluation and mitigating data bias 
• Online, In-Person, or Both? Online
• Exam Required? No
• Duration: 6 weeks 
• How Many Hours Of Instruction: 4 to 5 hours per week (self-paced)
• Eligibility Requirements: Knowledge of Python
• Price: Free
• Take The Course: Harvard University

21. Google Cloud Big Data and Machine Learning Fundamentals (Google Cloud)

This course introduces essential concepts and tools for managing big data and machine learning on Google Cloud. It guides learners through using Google Cloud’s big data and ML capabilities for data processing and analysis.

• Who It’s For: Data engineers and analysts looking to use Google Cloud
• Topics Covered:
  • Google Cloud Big Data and ML Ecosystem
  • Data processing with BigQuery and Dataflow
  • Data storage options on Google Cloud
  • Using AI Platform for Machine Learning
  • Hands-on labs with Google Cloud tools

• Online, In-Person, or Both? Online
• Exam Required? No
• Duration: 9 hours 
• How Many Hours Of Instruction: Self-paced
• Eligibility Requirements: None
• Price: Free
• Take The Course: Coursera

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