Machine Learning
What does it mean for machines to learn? In this section, learn how computers can utilize a mathematical model and a set of sample data to make informed decisions about new incoming data. This very popular topic is in high demand from employers these days, and the applications for machine learning continue to mulitply.
What is Machine Learning?
Machine learning is a type of artificial intelligence that allows computers to learn from data without being explicitly programmed.
Machine learning algorithms build models based on data that can be used to make predictions or recommendations.
There are different types of machine learning, including supervised, unsupervised, and reinforcement learning.
Supervised learning algorithms require labeled data, while unsupervised learning algorithms work with unlabeled data.
Reinforcement learning algorithms learn from their environment by trial and error.
Machine learning is used in many different fields, such as finance, healthcare, and transportation.
Machine learning is a relatively new field, and there is still a lot of research being done in order to improve the accuracy of predictions made by machine learning algorithms.
Machine Learning Resources
Data Mining: Practical Machine Learning Tools and Techniques
Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the in...
Introduction to Data Science - Codecademy
Explore data science fundamentals including data analysis, visualization, statistics, and Python tools through Codecademy's interactive platform.
Machine Learning for Everyone
An introduction to machine learning with no coding involved.
Introduction to AI/ML Toolkits with Kubeflow (LFS147)
Explore the origins, deployment options, individual components and common integrations of Kubeflow.
Machine Learning - Data Scientist Path
This path is designed for learners skilled in math, statistics, and analysis who want become machine learning (ML) subject matter experts within their organization. Progress through fundamental, intermediate, and advanced courses to learn how machine...
Learn ML
To become an expert in machine learning, you first need a strong foundation in four learning areas : coding, math, ML theory, and how to build your own ML project from start to finish. Begin with TensorFlow's curated curriculums to improve these fou...
Machine Learning for Data Analysis
Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself w...
Practical Machine Learning
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The...
Machine Learning: Recommender Systems & Dimensionality Reduction
About this Course Case Study: Recommending Products How does Amazon recommend products you might be interested in purchasing? How does Netflix decide which movies or TV shows you might want to watch? What if you are a new user, should Netflix just r...
Machine Learning Foundations: A Case Study Approach
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classifi...
Machine Learning - Developer Path
This path is designed for builders and software developers who want to use machine learning (ML) and artificial intelligence (AI) to better partner with data scientists and innovate with machine learning technologies. Progress from fundamental to adv...
Machine Learning: Classification
Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...