Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence
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Learn how to create an expert level artificial intelligence to play Connect Four using Python. We start out with a very simple implementation of just dropping a piece randomly and then progress to choosing a column based on score and then finally implementing the minimax algorithm with alpha beta pruning.
Guido van Rossum is the creator of Python, one of the most popular and impactful programming languages in the world. This conversation is part of the Artificial Intelligence podcast and the MIT course 6.S099: Artificial General Intelligence. The conversation and lectures are free and open to everyone. Audio podcast version is available on https://lexfridman.com/ai/
FREE AI SUMMER SCHOOL FOR KIDS – WATCH A FUTURE AI TALENT SPEAK ON HER EXPERIENCE.
Africa’s AI future is great! We are excited to host amazing kids at our AI Summer School. They learnt Introductory Python programming, they applied codes in interacting with robotic objects (e.g. drones) and did intro Machine Learning with y=mx+c linear equation for prediction. Amazing how they grasped the concept of regression/dependent and independent variables in prediction.Listen to future AI superstar, Victory Yinka-Banjo as she shares her experience.Batch 2 will run 13-17 August. Selection has been sent to all the participants.
How does an autoencoder work? Autoencoders are a type of neural network that reconstructs the input data its given. But we don’t care about the output, we care about the hidden representation its learned. Its a lower dimensional compression of the input that preserves its features. We can use this learned representation for tasks like image colorization, dialogue generation, and anomaly detection.
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In this video, we will see what neural networks are, why are they named this way, and how do they work.
• Explain that neural networks are a kind of classification technique
• Explain that neural networks were designed to be analogous to brain neurons
• Learn that a neural network has multiple layers whose weights are trained over several epochs
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Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends.
This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You’ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.
In this series, we’ll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.
For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we’ll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we’ll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are.
In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. If you have a basic understanding of Python, and the willingness to learn/ask questions, you will be able to follow along here with no issues. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets. Math involved is typically linear algebra, but I will do my best to still explain all of the math. If you are confused/lost/curious about anything, ask in the comments section on YouTube, the community here, or by emailing me. You will also need Scikit-Learn and Pandas installed, along with others that we’ll grab along the way.
Machine learning was defined in 1959 by Arthur Samuel as the “field of study that gives computers the ability to learn without being explicitly programmed.” This means imbuing knowledge to machines without hard-coding it.
