Machine Learning Guide
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Machine Learning Guide Teaches the high level fundamentals of machine learning and artificial intelligence. Join Tyler Renelle as he teaches the fundamentals and concepts of Machine Learning. Audio may seem an inferior medium, but it's a great supplement during exercise/commute/chores and through audio, Tyler will guide you through what Machine Learning is.
Where your other resources provide the machine learning trees, Tyler provides the forest. Consider this your Machine Learning syllabus.
In this course you'll learn:
Basic intuition
Algorithms, and math.
Explore languages and frameworks such as NLP, deep learning, Monte Carlo models and more.
Special thanks to:
Yair Bashan for the audio effects
Syllabus
What exactly is machine learning? In this course, Machine Learning Guide, Tyler Renelle, developer and machine learning engineer, teaches high level fundamentals of machine learning and artificial intelligence.
This course will be a “middle” level overview: you’ll be able to learn about big ideas and take an audio peek at some of the details. There’s no math or programming experience needed—so long as machine learning is of interest to you, this class is for you!
For those who are excited to take a deep dive into the field shortly after this course, or while listening to it, supplementary resources such as articles and books are available for your viewing pleasure.
Learn More >
This course will be a “middle” level overview: you’ll be able to learn about big ideas and take an audio peek at some of the details. There’s no math or programming experience needed—so long as machine learning is of interest to you, this class is for you!
For those who are excited to take a deep dive into the field shortly after this course, or while listening to it, supplementary resources such as articles and books are available for your viewing pleasure.
Learn More >
What is artificial intelligence, and how does it relate to machine learning? These two phrases are often thrown about, and sometimes seem to be used interchangeably. In this lesson, you will learn:
• The different sub fields and applications of artificial intelligence (AI), and
• How the technologies that apply artificial intelligence techniques change how we perceive AI.
You’ll also get a brief background on why great thinkers and computer experts started thinking about this field in the first place.
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• The different sub fields and applications of artificial intelligence (AI), and
• How the technologies that apply artificial intelligence techniques change how we perceive AI.
You’ll also get a brief background on why great thinkers and computer experts started thinking about this field in the first place.
Learn More >
Ready to be inspired? The advent of technology has always left some afraid for their jobs and others excited about the future. But how is AI different from other developing technologies? In this lesson, you will learn about artificial intelligence in the context of the larger economy and the future of human development. We will cover:
• the AI-induced economic revolution,
• Singularity,
• AI consciousness, and
• the Scare.
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• the AI-induced economic revolution,
• Singularity,
• AI consciousness, and
• the Scare.
Learn More >
How does machine learning actually facilitate a machine’s learning? What do these algorithms have in common? In this lesson, we’ll dive deeper into the three computational steps we discussed last lesson (infer/predict, error/loss, and train/learn) with an example and learn about three types of machine learning:
• Supervised learning
• Unsupervised learning
• Reinforced learning
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• Supervised learning
• Unsupervised learning
• Reinforced learning
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What are the actual nuts and bolts of a machine learning algorithm? In this lesson, we’ll learn about the most fundamental algorithmic example—linear regression—against an example of a housing market dataset.
We’ll take a deeper look at:
• Hypothesis functions
• Cost functions
• Gradient descents
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We’ll take a deeper look at:
• Hypothesis functions
• Cost functions
• Gradient descents
Learn More >
So you’re interested in studying more about machine learning. What’s next? In this lesson, we’ll discuss strategies for deeper exploration in the field that suits hobbyists and professionals alike:
• Certificates and online courses
• Side projects
• Accredited degrees
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• Certificates and online courses
• Side projects
• Accredited degrees
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Your first classifier: Logistic Regression. That plus Linear Regression, and you're a 101 supervised learner!
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Introduction to the branches of mathematics used in machine learning. Linear algebra, statistics, calculus.
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Deep learning and neural networks. How to stack our logisitic regression units into a multi-layer perceptron.
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Languages & frameworks comparison. Languages: Python, R, MATLAB/Octave, Julia, Java/Scala, C/C++. Frameworks: Hadoop/Spark, Deeplearning4J, Theano, Torch, TensorFlow.
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Speed-run of some shallow algorithms: K Nearest Neighbors (KNN); K-means; Apriori; PCA; Decision Trees
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Speed run of Anomaly Detection, Recommenders(Content Filtering vs Collaborative Filtering), and Markov Chain Monte Carlo (MCMC).
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