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417 Best + Free Machine Learning Courses & Certificates [2026]
Ranked by Michael Kuhlman, CourseDuck's founder, from student reviews across every provider. How we rank · Embed this list
Our guide: The 5 Best Machine Learning Courses in 2026
Our top picks
- Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R [Udemy]Best Paid Course
- The Data Science Course: Complete Data Science Bootcamp 2026 [Udemy]
- Python for Data Science and Machine Learning Bootcamp [Udemy]Editor's Choice
- Complete A.I. & Machine Learning, Data Science Bootcamp [Udemy]
- The Top 5 Machine Learning Libraries in Python [Udemy]
- Deep Learning A-Z [2026]: DL, AI in Python & AWS + LLM Prize [Udemy]
- The Complete Python Developer [Udemy]
- Machine Learning, Data Science & AI Engineering with Python [Udemy]
- Machine Learning for Absolute Beginners - Level 1 [Udemy]
- Complete Data Science,Machine Learning,DL,NLP Bootcamp [Udemy]
About the best Machine Learning courses
CourseDuck compares the 417 Machine Learning courses listed here from 26 providers, 178 of them free, and ranks them from the 1.2 million ratings and reviews their students have left, with the price, length and level of each one side by side. No course pays to be listed or ranked. As featured on Harvard EDU, Stackify and Inc. How we rank
Frequently asked questions
The most-reviewed Machine Learning course in this category is Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R by Kirill Eremenko, with about 206,000 reviews at 4.5 as of October 2026.
Machine learning is a branch of artificial intelligence in which computers learn patterns from data instead of following hand-written rules. It is used for classification, prediction, recommendation and language processing. Courses here cover machine learning with Python, data science, natural language processing, MLOps and model deployment.
- Average
- $157,676 a year
- Middle half earn
- $129,000 to $187,500 25th to 75th percentile
- Full range
- $108,000 to $196,000
Share of US job postings in each pay band; the dark bar holds the average, the striped bars the 25th and 75th percentiles. Source: ZipRecruiter salary data collected in 2020-21, when this FAQ was first written; pay has moved since, so treat it as a rough guide.
Paid Udemy video courses include a certificate of completion when you finish; free courses and practice-test-only courses do not. It shows you did the work, but it is not an accredited qualification. Coursera and edX usually charge for a certificate, issued with the university or company that made the course. LinkedIn Learning gives a certificate of completion for most of its courses as part of the subscription.
Provider
University
Tags
Rating
Duration
Difficulty
Publication Year
Language
417 Filtered Courses
![Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R](../../programming/machine-learning/images/950390_270f_7.jpg?v=1789255153)
Best Paid Course
1
Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R
4.5
A 49-hour survey of nearly every classic machine learning algorithm in Python and R, plus an AWS deployment track, taught intuition-first with diagrams rather than derivations.
Best for: Beginners with high school math who want a map of the whole ML field, with code templates in Python and R, before specialising.
Pros
Cons
-
- Theory lectures explain each algorithm with diagrams and intuition; reviewers say the concepts stick.
- Covers regression through neural networks, plus an AWS track on SageMaker, deployment and CI/CD.
- Python and R tracks can be taken separately, with code templates included for the models.
- Refreshed in June 2026 and backed by 1.2 million students and a 4.5 rating from 206,000 reviews.
-
- Coding videos are padded with long intros; one reviewer estimates a quarter of the runtime could be cut.
- Some Python snippets are deprecated and the graded labs fail on variable names, per recent reviews.
- Shallow on statistics; model assumptions are rarely checked, so it can feel like a coding tutorial.
2
The Data Science Course: Complete Data Science Bootcamp 2026
4.5
A 32-hour, theory-first path from probability and statistics through Python to regression, clustering and TensorFlow, aimed squarely at people starting from zero.
Best for: Beginners who want the math and statistics explained before touching a machine learning library.
Pros
Cons
-
- Probability and statistics get about seven hours of real teaching before any Python appears
- 274 quizzes and 130 coding exercises; reviewers say the quizzes make the material stick
- Pace is praised as neither too fast nor too slow for absolute beginners
- Updated September 2026; an earlier CourseDuck review found Q&A responses fast and reliable
-
- Long and theory-heavy up front; several low-star reviews lost interest before the practical parts
- One reviewer says the coding exercises are strict about exact answers and don't match the video examples
- Machine learning sections lean on running code without the math depth the statistics sections had
Editor's Choice
3
Python for Data Science and Machine Learning Bootcamp (2020)
4.5
Udemy's Bestseller-badged Python data science bootcamp, taking you from pandas through scikit-learn to a first neural net in about 25 hours, if you can live with 2020-era tooling.
Best for: People who already code a little and want one course covering pandas, plotting and every classic ML algorithm.
Pros
Cons
-
- A code notebook for every lecture, so students run the code rather than just watch it
- Pandas section and the visualization libraries are singled out by reviewers as highlights
- Covers regression, trees, SVMs, k-means, NLP, neural nets and Spark in one place
- Q&A section that reviewers call very useful when they get stuck
-
- Last updated May 2020; Plotly/Cufflinks, PySpark and some Seaborn calls no longer work as shown
- Long on videos, short on practice: one quiz and no coding exercises across 165 lectures
- Decision trees and the deep learning section feel rushed to several reviewers
4
Complete A.I. & Machine Learning, Data Science Bootcamp (2026)
4.7
Andrei Neagoie and Daniel Bourke's 44 hour ramp from Python basics to TensorFlow 2, built around two milestone projects and holding a 4.7 rating from about 31,000 students.
Best for: Complete beginners, no math or coding required, who want one long guided path to a first deep learning project.
Pros
Cons
-
- Two milestone projects, a classification task and a time series model, with all notebooks provided on GitHub
- Two separate Python sections so non-programmers can start from zero and coders can skip straight to pandas
- Refreshed in February 2026, with 384 lectures and 61 articles spread across 20 sections
- Reviewers call the teaching engaging and fun to watch, with a new-hire framing that keeps lessons focused
-
- Unsupervised learning is in the blurb, but one low-star reviewer says it is missing and the course feels incomplete
- Only two quizzes and one coding exercise across 44 hours, so practice is mostly watch and retype
- Informal chatter and lightly explained concepts frustrate reviewers who wanted more depth
5
The Top 5 Machine Learning Libraries in Python (2025)
4.6
A free, 100-minute tour of pandas, NumPy, scikit-learn, matplotlib and NLTK that shows what each library is for before anyone commits to a long machine learning bootcamp.
Best for: Total beginners who want to know what each Python ML library does before paying for a full bootcamp.
Pros
Cons
-
- Free and only 100 minutes long, so it fits in a single evening
- Opening terminology section that reviewers say clears up most of the early confusion
- Annotated Jupyter notebook included so students can run the code while watching
- Refreshed in April 2025, unusual for a free course first published in 2017
-
- Surface level only; three-star reviewers say nobody could build a model from this alone
- Some reviewers say it jumps between topics rather than teaching one library properly
- No coding exercises, and the final bonus lecture is a pitch for the paid course
![Deep Learning A-Z [2026]: DL, AI in Python & AWS + LLM Prize](../../programming/deep-learning/images/1151632_de9b_3.jpg?v=1789256617)
6
Deep Learning A-Z [2026]: DL, AI in Python & AWS + LLM Prize
4.6
Six-part tour of neural networks, CNNs, RNNs, self organizing maps, Boltzmann machines and autoencoders that leads with intuition and has drawn over 400,000 students.
Best for: Beginners who want to understand why each network works before writing code, across six different architectures.
Pros
Cons
-
- Intuition lectures before every coding section, which reviewers call clear and lucid
- Covers six architectures including SOMs, Boltzmann machines and autoencoders
- Updated June 2026, with AWS sections and an annex on regression and preprocessing
- Links to further reading and code templates included
-
- Coding sections reportedly lean on copy and paste, with some keywords left unexplained
- Datasets must be requested from an outside site, which one reviewer found slow
- No transformers in the syllabus, and reviewers call parts outdated or too basic
7
The Complete Python Developer (2026)
4.6
A 31-hour beginner Python course that goes from basics into scripting, scraping, web development and a short machine learning section, all taught by one instructor.
Best for: Complete beginners who want one course that samples scripting, scraping, web apps and a little machine learning.
Pros
Cons
-
- Reviewers call the explanations clear and easy to follow for a first language
- One reviewer says it explains why Python behaves as it does, not just syntax
- After the core language, it samples scripting, scraping, web and machine learning
- Refreshed in August 2026, and the description targets Python 3.13
-
- Some reviewers say tooling and practices feel dated, for example no uv
- At least one student hit errors following along on a newer Python version
- A few reviewers say it stays high level and is thin on fundamentals depth
8
Machine Learning, Data Science & AI Engineering with Python (2026)
4.6
Frank Kane's 20-hour survey runs from a Python and stats refresher through classical ML to GPT, RAG and LLM agents, refreshed August 2026 and rated 4.6 by nearly 37,000 students.
Best for: Working programmers who want one broad, current tour of machine learning plus generative AI before picking a specialty.
Pros
Cons
-
- Covers GPT, the OpenAI API, RAG and LLM agents, with the content refreshed in August 2026
- Reviewers say the instructor speaks slowly and clearly and the setup steps and notebooks just work
- About 20 hours across 148 lectures, from a stats refresher to Spark MLlib and deep learning
- 4.6 stars from nearly 37,000 reviews, roughly 90 percent of them 4 or 5 stars
-
- Several reviewers say the instructor reads from notes rather than building intuition
- A long statistics and probability stretch comes before the real projects begin
- Some download links have gone stale, and there is no full LLM or agents project
9
Machine Learning for Absolute Beginners - Level 1 (2026)
4.5
A short, no-code tour of machine learning and generative AI, built for people who want the vocabulary and big ideas before touching any code.
Best for: Managers, product people and curious beginners who want AI terms explained without code or math.
Pros
Cons
-
- Reviewers say the explanations are easy to follow and the visuals help
- Needs no math or programming, which reviewers count as a plus
- Generative AI sections explain how models treat prompts and training data
- Short and structured: 59 lectures in eight sections with quizzes
-
- All theory. No coding exercises, so nothing gets built or practiced
- Some reviewers found the examples watered down and light on how AI works inside
- A reviewer wanted a cleaner transcript, and another found it dry
10
Complete Data Science,Machine Learning,DL,NLP Bootcamp (2026)
4.5
A 100 hour path from Python basics to MLOps with Krish Naik: statistics, classic ML algorithms, two deployment projects and an NLP finish, with plenty of math.
Best for: Python-literate beginners who want one long path from statistics and ML math to Docker and cloud deployment.
Pros
Cons
-
- Explains why each algorithm exists and the math behind it, reviewers say
- Two long end to end projects cover Docker, AWS, Azure, MLflow and ETL pipelines
- Updated in August 2026, with 436 lectures and 20 coding exercises
- Reviewers say lectures stay easy to follow even at 1.75x or 2x speed
-
- Some topics feel rushed, and libraries get less explanation than the math
- No real deep learning section in the syllabus, and nothing practical after transformers
- True beginners report feeling lost in the statistics and hyperplane material
11
Artificial Intelligence & Machine Learning for Business (2025)
4.5
A six-hour, no-code overview of AI and machine learning for managers and executives, covering industry uses, team building and the steps of an AI project.
Best for: Managers and executives who need AI vocabulary and a project-process overview, with no coding.
Pros
Cons
-
- No coding or prior AI knowledge needed, so it works for managers coming in cold
- Covers the practical side: building an AI team, setting up a practice and the steps of a project
- Well-divided sections and 13 quizzes help reinforce each topic
- Industry and function examples show where AI is used, from sales and marketing to finance and HR
-
- Reviewers call it high-level and theory-heavy, with few real-world cases or frameworks
- One reviewer says many lectures are from 2020 or earlier and need updating
- Slow introductions drew complaints, and one narrator's pronunciation was hard to follow
Show All
All 417 Machine Learning courses
- Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R
- The Data Science Course: Complete Data Science Bootcamp 2026
- Python for Data Science and Machine Learning Bootcamp
- Learn Python Programming Masterclass
- The Complete Python Developer
- Deep Learning A-Z [2026]: DL, AI in Python & AWS + LLM Prize
- Machine Learning, Data Science & AI Engineering with Python
- Machine Learning for Absolute Beginners - Level 1
- Complete A.I. & Machine Learning, Data Science Bootcamp
- Complete Data Science,Machine Learning,DL,NLP Bootcamp
- Artificial Intelligence & Machine Learning for Business
- Spark and Python for Big Data with PySpark
- Python for Machine Learning & Data Science Masterclass
- Data Science and Machine Learning Bootcamp with R
- Introduction to Machine Learning for Data Science
- Artificial Intelligence: Reinforcement Learning in Python
- MLOps Fundamentals - Learn MLOps Concepts with Azure demo
- Azure Machine Learning & MLOps : Beginner to Advance
- Intro to Data Science: QuickStart Guide + AI & ChatGPT Prize
- DP-100: A-Z Machine Learning using Azure Machine Learning
- The Complete Machine Learning Course with Python
- Bayesian Machine Learning in Python: A/B Testing
- [2026] Machine Learning: Natural Language Processing (V2)
- Python + SQL + Tableau: Integrating Python, SQL, and Tableau
- Deployment of Machine Learning Models
- Complete 2022 Data Science & Machine Learning Bootcamp
- Machine Learning & Deep Learning in Python & R
- Scala and Spark for Big Data and Machine Learning
- QC101 Quantum Computing & Intro to Quantum Machine Learning
- An Introduction to Machine Learning for Data Engineers
- Cluster Analysis and Unsupervised Machine Learning in Python
- Python & Machine Learning for Financial Analysis
- The Top 5 Machine Learning Libraries in Python
- Unsupervised Machine Learning Hidden Markov Models in Python
- Algorithmic Trading A-Z with Python, Machine Learning & AWS
- AWS Machine Learning with SageMaker: Hands-On
- ChatGPT for Data Science and Machine Learning
- Building Recommender Systems with Machine Learning and AI
- Welcome to Artificial Intelligence !
- Data Science : Complete Data Science & Machine Learning
- Feature Engineering for Machine Learning
- Python-Introduction to Data Science and Machine learning A-Z
- No-Code Machine Learning: Practical Guide to Modern ML Tools
- Data Science: Supervised Machine Learning in Python
- Machine Learning with Javascript
- Data Science, Machine Learning, Data Analysis, Python & R
- Machine Learning Practical: 6 Real-World Applications
- Machine Learning Nanodegree
- Time Series Analysis, Forecasting, and Machine Learning
- Master statistics & machine learning: intuition, math, code
- Machine Learning for Absolute Beginners - Level 2
- Complete Machine Learning with R Studio - ML for 2026
- Ensemble Machine Learning in Python: Random Forest, AdaBoost
- 机器学习 A-Z (Machine Learning A-Z in Chinese)
- Feature Selection for Machine Learning
- Machine Learning- From Basics to Advanced
- Deploying AI & Machine Learning Models for Business | Python
- AI & Machine Learning Course 2026: Beginner to Advanced
- Complete Data Science Training with Python for Data Analysis
- Machine Learning Practical Workout | 8 Real-World Projects
- A Beginner's Guide To Machine Learning with Unity
- DeepFakes & Voice Cloning: Machine Learning The Easy Way
- Introduction to Machine Learning Models (AI) Testing
- AWS Certified Machine Learning Specialty: 3 PRACTICE EXAMS
- Machine Learning and AI: Support Vector Machines in Python
- Python für Data Science, Machine Learning & Visualization
- Learn Python for Data Science & Machine Learning from A-Z
- Full stack web development and AI with Python (Django)
- 【世界で91万人が受講】基礎から理解し、Pythonで実装！機械学習26のアルゴリズムを理論と実践を通じてマスターしよう
- Machine Learning and Deep Learning Bootcamp in Python
- Data Science and Machine Learning Fundamentals [2026]
- Reinforcement Learning beginner to master - AI in Python
- Introduction au Machine Learning
- Complete Linear Regression Analysis in Python
- AWS Machine Learning Certification Exam | Complete Guide
- Machine Learning and Business Intelligence Masterclass
- Python for Machine Learning: The Complete Beginner's Course
- Data Science for Business Leaders: ML Fundamentals
- Machine Learning using Python
- AWS Data Architect Bootcamp - 43 Services 500 FAQs 20+ Tools
- Mathematical Foundation For Machine Learning and AI
- Learn Machine learning & AI [Beginner to advanced content]
- Machine Learning & Data Science A-Z: Hands-on Python
- Data Science : Master Machine Learning Without Coding
- Machine Learning Classification Bootcamp in Python
- Machine Learning Foundations: A Case Study Approach
- Machine Learning: Build neural networks in 77 lines of code
- Machine Learning by Stanford
- Logistic Regression in Python
- Machine Learning From Basic to Advanced
- From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase
- Machine Learning 101 with Scikit-learn and StatsModels
- Machine Learning Regression Masterclass in Python
- Machine Learning Essentials - Master core ML concepts
- Машинное обучение в Python: Machine Learning & Data Science
- Machine Learning in JavaScript with TensorFlow.js
- The Complete Python 3 Masterclass - From Beginner To Pro
- Neural Networks and Deep Learning
- Sequence Models
- Machine Learning & Data Science: The Complete Visual Guide
- Species Distribution Models with GIS & Machine Learning in R
- Convolutional Neural Networks
- Machine Learning Applied to Stock & Crypto Trading - Python
- Build Your own Self Driving Car | Deep Learning, OpenCV, C++
- Projects in Machine Learning : Beginner To Professional
- Practical Machine Learning by Example in Python
- Python and Machine Learning for Complete Beginners
- Machine Learning with Python from Scratch
- Machine Learning: Aplicado a Python y Data Science
- Complete Machine Learning & Data Science with Python | A-Z
- Regression Analysis for Statistics & Machine Learning in R
- Support Vector Machines in Python: SVM Concepts & Code
- Data Science and Machine Learning using Python - A Bootcamp
- Complete Machine Learning 2026 A-Z™: 10 Real World Projects
- Face Recognition with Machine Learning + Deploy Flask App
- AI For Everyone
- Clustering & Classification With Machine Learning In Python
- Azure Machine Learning using Cognitive Services
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- Machine Learning A-Z: Become Kaggle Master
- The Complete Python Course for Machine Learning Engineers
- How Google does Machine Learning
- Mathematics for Machine Learning: Linear Algebra
- Comprehensive AI & Machine Learning Bootcamp
- Applied Plotting, Charting & Data Representation in Python
- Machine Learning with Python by IBM
- Artificial Intelligence (AI) in Software Testing
- Data Science:Data Mining & Natural Language Processing in R
- Practical Machine Learning
- Machine Learning: Classification
- iOS Machine Learning with Core ML, Swift 6, and SwiftUI
- Applied Text Mining in Python
- Aprenda Machine Learning em Python com Scikit-learn
- Machine Learning Optimization Using Genetic Algorithm
- Machine Learning : A Beginner's Basic Introduction
- Machine Learning : Complete Maths for Machine Learning
- 40 Real World Data Science, Machine Learning Projects 2025
- Mathematics for Machine Learning: Multivariate Calculus
- 【強化学習】ロボットの最適ルートを決める実装を通じ、強化学習のアルゴリズム（Q学習）を理解しよう
- Complete iOS Machine Learning Masterclass
- TensorFlow and the Google Cloud ML Engine for Deep Learning
- Speaker Recognition | By Award Winning Textbook Author
- Applied Machine Learning in R
- Machine Learning: Clustering & Retrieval
- Serverless Data Analysis with Google BigQuery and Cloud Dataflow
- Practical Introduction to Machine Learning with Python
- Statistics & Linear Algebra for Machine Learning
- 2 in 1: Python Machine Learning PLUS 30 Hour Python Bootcamp
- Data Science and Machine Learning Masterclass with R
- Machine Learning for Apps
- Introduction To Data Science
- Machine Learning In The Cloud With Azure Machine Learning
- Introduction to Deep Learning
- Python Regression Analysis: Statistics & Machine Learning
- Convolutional Neural Networks in TensorFlow
- Clustering & Classification With Machine Learning In R
- Android P& Kotlin -Complete Android App Development Bootcamp
- Machine Learning using Python: A Comprehensive Course
- Probabilistic Graphical Models 1: Representation
- How to Become A Data Scientist Using Azure Machine Learning
- Applied Social Network Analysis in Python
- Artificial Intelligence Blueprint™: Machine Learning
- Artificial Intelligence for beginners: Neural Networks
- Machine Learning and Statistical Modeling with R Examples
- Hands-On Machine Learning for .NET Developers
- Learning Path: R: Complete Machine Learning & Deep Learning
- Data Visualization in Python for Machine Learning Engineers
- Machine Learning Model Deployment with Flask, React & NodeJS
- Intro to TensorFlow
- Machine Learning & Training Neural Network in MATLAB
- Complete Machine Learning & Reinforcement learning 2023
- Natural Language Processing in TensorFlow
- Beginning with Machine Learning, Data Science and Python
- A to Z (NLP) Machine Learning Model building and Deployment.
- Fundamentals of Scalable Data Science
- Automated Machine Learning with AutoGluon Library in Python
- How to Win a Data Science Competition: Learn from Top Kagglers
- Complete Machine Learning and Deep Learning With H2O in R
- SVM for Beginners: Support Vector Machines in R Studio
- Applied Machine Learning For Healthcare
- 5 Courses - Master AWS, Analytics, Machine Learning, Bigdata
- Unleash Machine Learning: Build Artificial Neuron in Python
- The Fun and Easy Guide to Machine Learning using Keras
- Build Your own Self Driving Car [Course -2]
- Byte-Sized-Chunks: Recommendation Systems
- Building Resilient Streaming Systems on Google Cloud Platform
- Artificial Intelligence: Practical Essentials for Management
- Artificial Intelligence and Machine Learning Fundamentals
- Machine Learning & Tensorflow - Google Cloud Approach
- Create a Neural Network in Java
- Data Wrangling in Pandas for Machine Learning Engineers
- Sequences, Time Series and Prediction
- Natural Language Processing
- Fundamentals of Reinforcement Learning
- Feature Engineering
- Big Data Essentials: HDFS, MapReduce and Spark RDD
- Practical Project Management for Machine Learning Projects
- Machine learning with Scikit-learn
- Neural Networks for Machine Learning From Scratch
- Data Science: Machine Learning and Deep Learning with Python
- Machine Learning from the scratch using Python
- Complete Data Science & Machine Learning Bootcamp in Python
- Applied Machine Learning with R (Trading Use Case) - 2020
- Applied AI with DeepLearning
- Getting Started with AI using IBM Watson
- AI 2025 Datascience Foundational 44 projects Zero To Pro
- Machine Learning in Python: From Zero to Hero in 10 Hours
- Art and Science of Machine Learning
- Machine Learning & Data Science Masterclass in Python and R
- Machine Learning with Python: Data Science for Beginners
- Machine Learning for beginners
- Advanced Machine Learning and Signal Processing
- Data Science: Bitcoin Data Visualization & Price Prediction
- Predictive Modeling and Analytics
- AI & ML w/ Python-2022-Practical Hands On with Minimum Maths
- Machine Learning for Algorithmic Trading Bots with Python
- Probabilistic Graphical Models 2: Inference
- Machine Learning with AWS
- Practical Predictive Analytics: Models and Methods
- Data Science Statistics A-Z : Python
- SciKit-Learn in Python for Machine Learning Engineers
- Supervised Machine Learning for beginners
- Production Machine Learning Systems
- Data Science and Machine Learning in Python
- Getting Started with AWS Machine Learning
- Machine Learning for Data Analysis
- Document classification using Machine Learning
- AWS Certified Machine Learning – Specialty
- Machine Learning With Python: Predicting Customer Churn
- Advanced Machine Learning with Python: Master the Algorithms
- Machine Learning and Big Data Analytics with AWS
- Machine Learning in the Elastic Search
- ML Ops: Beginner
- Text Classification with fastText and machine learning
- Fundamentals of Machine Learning in Finance
- Probabilistic Graphical Models 3: Learning
- Communicating Data Science Results
- Artificial Intelligence: Genetic Machine Learning Algorithms
- Machine Learning with Java and Weka
- Sample-based Learning Methods
- Big Data Analysis: Hive, Spark SQL, DataFrames and GraphFrames
- Image Understanding with TensorFlow on GCP
- Introduction to Applied Machine Learning
- Artificial Intelligence - TensorFlow Machine Learning
- Practical Machine Learning with Python
- The Ultimate App Making Course! Master Many Coding Skills!
- Beginning Machine Learning with AWS
- Deep Learning by MIT Press
- Python: Build Machine Learning Models in 6 Hours
- Learn Machine Learning with Weka
- Practical Deep Learning for Coders, v3
- Introduction to Computer Vision with Watson and OpenCV
- Machine Learning With TensorFlow: The Practical Guide
- TensorFlow: Artificial Intelligence with TensorFlow: 3-in-1
- Big Data Applications: Machine Learning at Scale
- An Introduction to Practical Deep Learning
- Machine Learning with Python by Sentdex
- Scalable Machine Learning on Big Data using Apache Spark
- Machine Learning: Data Analysis 2017
- Identify Problems with Artificial Intelligence - Case Study
- MIT Deep Learning for Self-Driving Cars
- Machine Learning Mastery (Integrated Theory+Practical HW)
- Machine Learning with Python: The Complete Guide
- Basic Data Processing and Visualization
- Deep Learning Nanodegree
- Application of AI, InsurTech, and Real Estate Technology
- Google's Machine Learning Crash Course
- Prediction and Control with Function Approximation
- Machine Learning by Columbia University and edX
- Introduction to Portfolio Construction and Analysis with Python
- Foundations of Data Science: K-Means Clustering in Python
- MongoDB Aggregation Framework
- Deep Learning and the Game of Go
- A Complete Reinforcement Learning System (Capstone)
- Addressing Large Hadron Collider Challenges by Machine Learning
- Deep Learning with PyTorch
- Machine Learning with R
- Machine Learning Algorithms: Supervised Learning Tip to Tail
- Designing the Future of Work
- Code Free Data Science
- Business Application of Machine Learning and Artificial Intelligence in Healthcare
- Deploying Machine Learning Models
- Data Science at Scale - Capstone Project
- Machine Learning
- Data Science: Machine Learning
- Step-by-Step Machine Learning with Python
- Data Science - Python for Machine Learning
- Learn Machine Learning By Building Projects
- Artificial Intelligence: A Modern Approach
- Natural Language Processing in Action - Understanding, analyzing, and generating text with Python
- Deep Learning Tutorial
- Natural Language Processing
- Introduction to Artificial Intelligence
- AI as a Service
- Architects of Intelligence
- Hands-On Artificial Intelligence for Beginners
- Uncertain Knowledge and Reasoning in Artificial Intelligence
- Artificial Intelligence for Big Data
- Artificial Intelligence with Python - Deep Neural Networks [Video]
- Machine Learning Andrew Ng, Stanford University [FULL COURSE]
- Neural Networks and Deep Learning
- Artificial Intelligence for Humans (3 book series)
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Intro to Machine Learning
- Undergraduate machine learning at UBC
- Machine Learning with Python
- Beginner Introduction to Neural Networks
- Fundamentals of Deep Learning for Computer Vision
- Machine Learning for Musicians and Artists
- Machine Learning Recipes with Josh Gordon
- Grokking Deep Learning in Motion
- Machine Learning Bootcamp
- Deep Learning with Python
- Neural networks
- Introduction to Machine Learning for Coders
- Machine Learning, Carnegie Mellon University
- Programming Collective Intelligence: Building Smart Web 2.0 Applications
- Python Machine Learning
- Machine Learning in Action
- Elements of Machine Learning (Morgan Kaufmann Series in Machine Learning)
- Machine Learning for Predictive Analytics
- Machine Learning Tutorial Videos
- Machine Learning Tutorial in Python
- Machine Learning
- Machine Learning: IIT Lectures/Tutorial/Course for Beginners
- Machine Learning Tutorial Python
- Machine Learning
- Machine Learning Training and Certification
- AWS Machine Learning by Example
- Mathematica 11 Machine Learning
- Introduction to Machine Learning with KNIME
- Applied Machine Learning: Algorithms
- Applied Machine Learning: Foundations
- Spark for Machine Learning & AI
- Building Recommender Systems with Machine Learning and AI
- Google Cloud Platform for Machine Learning Essential Training
- Machine Learning for Marketing: Essential Training
- Amazon Web Services Machine Learning Essential Training
- NLP with Python for Machine Learning Essential Training
- Deploying Scalable Machine Learning for Data Science
- Machine Learning and AI Foundations: Classification Modeling
- Machine Learning and AI Foundations: Clustering and Association
- Machine Learning for iOS Developers
- Artificial Intelligence Foundations: Machine Learning
- Machine Learning and AI Foundations: Recommendations
- Machine Learning and AI Foundations: Value Estimations
- Machine Learning and AI Foundations: Decision Trees
- Azure Machine Learning Development: 2 Learning ML Studio
- Machine Learning and AI Foundations: Predictive Modeling Strategy at Scale
- Azure Machine Learning Development: 3 Deploying and Managing Models
- Accelerating TensorFlow with the Google Machine Learning Engine
- Machine Learning & AI Foundations: Linear Regression
- Building a Recommendation System with Python Machine Learning & AI
- Machine Learning & AI: Advanced Decision Trees
- Learn System Center 2016: Virtual Machine Manager
- Learning Amazon SageMaker
- Data Science Foundations: Fundamentals
- Microsoft SQL Server 2019: New Features
- Learning VPN
- AWS Quick Tips for Developers
- The Psychology of Living In A Data-Driven World
- Google Cloud Platform Essential Training
- Learning G Suite Administration
- Learning XAI: Explainable Artificial Intelligence
- Azure Administration: Deploy and Manage Virtual Machines
- Learning Cyber Incident Response and Digital Forensics
- Apache Spark Deep Learning Essential Training
- Learning the Adobe Creative Cloud
- Learning Digital Business Analysis
- Introducing Jupyter
- Learning Lean IT
- Matrix Methods
- Project: Basic Image Classification with TensorFlow
- Distributed Machine Learning with Apache Spark
- Introduction to Artificial Intelligence (AI)
- Principles of Machine Learning: Python Edition
- Essential Math for Machine Learning: R Edition
- Essential Math for Machine Learning: Python Edition
- Amazon SageMaker: Simplifying Machine Learning Application Development
- Data Science and Machine Learning Capstone Project
- Machine Learning with Python: A Practical Introduction
- Principles of Machine Learning: R Edition
- Developing Big Data Solutions with Azure Machine Learning
- Microsoft Professional Capstone : Data Science
- Data Science: Machine Learning
- Deep Learning with Tensorflow
- Aprendizaje automatico y ciencia de datos
- Predictive Analytics for IoT Solutions
- Analyzing Data with Python
- Predictive Analytics using Machine Learning
- Machine Learning
- Machine Learning with Python: from Linear Models to Deep Learning
- Machine Learning Fundamentals
- Quantum Machine Learning
- Machine Learning
- Implementing Predictive Analytics with Spark in Azure HDInsight
- Computing for Data Analysis
- Become a Machine Learning Engineer
- Become a Self-Driving Car Engineer
- Intro to Self-Driving Cars
- Become a Computer Vision Expert
- Become a Natural Language Processing Expert
- Become a Deep Reinforcement Learning Expert
- AWS DeepRacer
- Spark
- Intro to Artificial Intelligence
- Machine Learning Interview Preparation - Career Services
- Core ML: Machine Learning for iOS
- Eigenvectors and Eigenvalues
- Intro to Data Science
- Machine Learning for Trading
- Reinforcement Learning
- Machine Learning: Unsupervised Learning
- Big Data Analytics in Healthcare
- Introduction to Computer Vision
- Intro to Descriptive Statistics
- Segmentation and Clustering