Best Machine Learning Course

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The best machine learning course is offered by Andrew Ng at Stanford University. This free Machine Learning course was created in collaboration with DeepLearning.AI and Stanford University. This ML course teaches you the fundamentals of ML/AI and how to use it to develop real-world artificial intelligence applications. The instructor Andrew Ng is an artificial intelligence expert and Founder of DeepLearning.AI, join his course below! Also, make sure to check out Machine Learning for Absolute Beginners.

Free Machine Learning Course Bundle

⭑⭑⭑⭑⭑ 4.9/5.0 – 3,115 ratings – 61,055 students

Best Machine Learning Course

Machine Learning Course Overview

This specialization will help you learn the fundamentals of machine learning so that you can apply it in your own work. You’ll gain an understanding of how the process works and get hands-on experience by completing projects and assignments.

The first course in this specialization covers how to use Python for machine learning, including an overview of classical statistical methods, linear algebra, and probability theory. The second course focuses on learning algorithms. Finally, you’ll learn about unsupervised models.

You’ll learn from experts in the ML field who’ve worked for companies like Google Brain, NASA JPL, Amazon AI Labs, IBM Watson Health Analytics Lab, and many more! You’ll also have access to forums where you can ask questions about any topics covered throughout these courses.

Coursera’s #1 Best Machine Learning Online Course

Coursera is a popular MOOC (massive open online course) provider that offers a course in machine learning. The Machine Learning Specialization is a series of courses that provide an introduction to machine learning. It covers the foundations of machine learning, including supervised and unsupervised learning, and introduces you to the tools used in machine learning.

You will learn about many of the algorithms used in machine learning, including decision trees, logistic regression, support vector machines, Naive Bayes classifiers, topic models, and more. You will also learn about how to evaluate the performance of machine learning algorithms, as well as how to use them for prediction problems like recommendation engines or online advertisement targeting. This ML course bundle aims to provide students with enough knowledge so that they can understand other courses in machine learning and apply those concepts back to their own projects.

The first module introduces the fundamental concepts behind machine learning and categorizes the different types of algorithms. The second module covers supervised learning, which is used to predict outcomes based on labeled data. The third module focuses on unsupervised learning techniques, which are used to group items into categories or clusters without labels. The fourth module covers reinforcement learning, which uses rewards to train an agent to perform a task. The final module covers advanced topics in machine learning and deep learning.

Why Learn Machine Learning?

Machine learning is a field of computer science that is concerned with algorithms that learn from data. These algorithms are often used for predictive modeling and data mining, but they also have many other applications. You will learn about the various models used in machine learning, supervised learning methods (such as classification and regression), unsupervised learning methods (such as clustering), reinforcement learning methods (such as Markov Decision Processes), and deep learning (such as explainable AI).

Machine learning’s focus is on algorithms that allow computers to learn from data without being programmed. Machine learning is used in computer vision, natural language processing and speech recognition, decision-making, robotics, and more. Machine learning focuses on many different approaches, included:

  • Supervised learning: a machine learns from examples it’s been given by a human or another machine
  • Unsupervised learning: a machine learns from data without being given any labels or feedback about what it’s looking for
  • Reinforcement learning: a machine learns from its own experience
  • Explainable AI: designing artificial intelligence systems so that humans can understand what they’re doing

Best Machine Learning Course Content

This program includes three ML courses. It includes an updated version of Andrew’s top-rated original Machine Learning course, rated 4.9 out of 5 and taken by over 5 million learners! The three-course bundle includes:

  1. Supervised Machine Learning: Regression and Classification
  2. Advanced Learning Algorithms
  3. Unsupervised Learning, Recommenders, Reinforcement Learning
Preview the Best Artificial Intelligence and Machine Learning Course Free Lectures
Lecture 1.1 — What Is Machine Learning — [ Machine Learning | Andrew Ng ]
Lecture 1.2 — Supervised Learning — [ Machine Learning | Andrew Ng ]
Lecture 1.3 — Unsupervised Learning — [ Machine Learning | Andrew Ng]
Lecture 2.1 — Linear Regression With One Variable | Model Representation — Andrew Ng
Lecture 2.2 — Linear Regression With One Variable | CostFunction — Andrew Ng
Lecture 2.3 — Linear Regression With One Variable | Cost Function Intuition #1 | Andrew Ng
Lecture 2.4 — Linear Regression With One Variable | Cost Function Intuition #2 | Andrew Ng
Lecture 2.5 — Linear Regression With One Variable | Gradient Descent — [ Andrew Ng]
Lecture 2.6 — Linear Regression With One Variable | Gradient Descent Intuition — [ Andrew Ng]
Lecture 2.7 — Linear Regression With One Variable | Gradient Descent For Linear Regression
Lecture 2.8 — What’s Next — [ ML | Andrew Ng | Stanford University]
Lecture 3.1 — Linear Algebra Review | Matrices And Vectors — [ ML | Andrew Ng]
Lecture 3.2 — Linear Algebra Review | Addition And Scalar Multiplication — [Andrew Ng]
Lecture 3.3 — Linear Algebra Review | Matrix Vector Multiplication — [ ML | Andrew Ng]
Lecture 3.4 — Linear Algebra Review | Matrix-Matrix Multiplication — [ Andrew Ng ]
Lecture 3.5 — Linear Algebra Review | Matrix Multiplication Properties — [ Andrew Ng ]
Lecture 3.6 — Linear Algebra Review | Inverse And Transpose — [ ML | Andrew Ng]
Lecture 4.1 — Linear Regression With Multiple Variables – (Multiple Features) — [ Andrew Ng]
Lecture 4.2 — Linear Regression With Multiple Variables — (Gradient Descent For Multiple Variables)
Lecture 4.3 — Linear Regression With Multiple Variables | Gradient In PracticeaI Feature Scaling
Lecture 4.4 — Linear Regression With Multiple Variables | Gradient In PracticeaI | Learning Rate
Lecture 4.5 — Linear Regression With Multiple Variables | Features And Polynomial Regression
Lecture 4.6 — Linear Regression With Multiple Variables | Normal Equation — [ Andrew Ng]
Lecture 4.7 — Linear Regression With Multiple Variables | Normal Equation Non Invertibility

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