About this episode
Feb 2017
MLG 005 Linear Regression
Linear regression is introduced as the foundational supervised learning algorithm for predicting continuous numeric values, using cost estimation of Portland houses as an example. The episode explains the three-step process of machine learning - prediction via a hypothesis functi ... Show More
35m 12s
Feb 2017
MLG 006 Certificates & Degrees
<div> <p>People interested in machine learning can choose between self-guided learning, online certification programs such as MOOCs, accredited university degrees, and doctoral research, with industry acceptance and personal goals influencing which path is most appropriate. Indus ... Show More
16m 28s
Feb 2017
MLG 007 Logistic Regression
<div> <p>The logistic regression algorithm is used for classification tasks in supervised machine learning, distinguishing items by class (such as "expensive" or "not expensive") rather than predicting continuous numerical values. Logistic regression applies a sigmoid or logistic ... Show More
35m 8s
Jun 2021
machine learning (noun) [Word Notes]
A programming technique where the developer doesn't specify each step of the algorithm in code, but instead teaches the algorithm to learn from the experience.
6m 16s
May 2021
474: The Machine Learning House
In this episode, I discuss the architecture of a “machine learning house”, representing the skills and learnings you can use as foundations to build your data science career.
Additional materials: www.superdatascience.com/474
5m 44s
Sep 2024
machine learning (noun) [Word Notes]
Enjoy this special encore episode. A programming technique where the developer doesn't specify each step of the algorithm in code, but instead teaches the algorithm to learn from the experience.
6m 16s
Jul 2022
Santiago Valderrama on Getting Smarter on Machine Learning, One Problem at a Time - Ep. 173
Want to learn about AI and machine learning? There are plenty of resources out there to help — blogs, podcasts, YouTube tutorials — perhaps too many. Machine learning engineer Santiago Valdarrama has taken a far more focused approach to helping us all get smarter about the field. ... Show More
27m 15s
May 8
Lesson 039. Computers and Internet.
Vocabulary and expressions in Mandarin Chinese related to using a computer and Internet. Working with files and folders, chatting in Chinese with your friends online. Your computer has crashed! Tell your Chinese online friend that you need to restart your computer! Please signup ... Show More
27m 11s
Machine learning consists of three steps: prediction, error evaluation, and learning, implemented by training algorithms on large datasets to build models that can make decisions or classifications. The primary categories of machine learning algorithms are supervised, unsupervised, and reinforcement learning, each with distinct methodologies for learning from data or experience.
Links
The Role of Machine Learning in Artificial Intelligence
- Artificial intelligence includes subfields such as reasoning, knowledge representation, search, planning, and learning.
- Learning connects to other AI subfields by enabling systems to improve from mistakes and past actions.
The Core Machine Learning Process
- The machine learning process follows three steps: prediction (or inference), error evaluation (or loss calculation), and training (or learning).
- In an example such as predicting chess moves, a move is made (prediction), the error or effectiveness of that move is measured (error function), and the underlying model is updated based on that error (learning).
- This process generalizes to real-world applications like predicting house prices, where a model is trained on a large dataset with many features.
Data, Features, and Models
- Datasets used for machine learning are typically structured as spreadsheets with rows as examples (e.g., individual houses) and columns as features (e.g., number of bedrooms, bathrooms, square footage).
- Features are variables used by algorithms to make predictions and can be numerical (such as square footage) or categorical (such as "is downtown" yes/no).
- The algorithm processes input data, learns the appropriate coefficients or weights for each feature through algebraic equations, and forms a model.
- The combination of the algorithm (such as code in Python or TensorFlow) and the learned weights forms the model, which is then used to make future predictions.
Online Learning and Model Updates
- After the initial training on a dataset, models can be updated incrementally with new data (called online learning).
- When new outcomes are observed that differ from predictions, this new information is used to further train and improve the model.
Categories of Machine Learning Algorithms
- Machine learning algorithms are broadly grouped into three categories: supervised, unsupervised, and reinforcement learning.
- Supervised learning uses labeled data, where the model is trained with known inputs and outputs, such as predicting prices (continuous values) or classes (like cat/dog/tree).
- Unsupervised learning finds similarities within data without labeled outcomes, often used for clustering or segmentation tasks such as organizing users for advertising.
- Reinforcement learning involves an agent taking actions in an environment to achieve a goal, receiving rewards or penalties, and learning the best strategies (policies) over time.
Examples and Mathematical Foundations
- Regression algorithms like linear regression are commonly used supervised learning techniques to predict numeric outcomes.
- The process is rooted in algebra and particularly linear algebra, where matrices represent datasets and the algorithm solves for optimal coefficient values.
- The model's equation generated during training is used for making future predictions, and errors from predictions guide further learning.
Recommended Resources
- MachineLearningMastery.com: Accessible articles on ML basics.
- Podcast's own curated learning paths: ocdevel.com/mlg/resources.
- The book "The Master Algorithm" offers an introductory and audio format overview of foundational machine learning algorithms and concepts.
