Show notes: ocdevel.com/mlg/1. MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc. MLG,Resources Guide Dept Agency Gnothi (podcast project):website,Github What is this podcast? "Middle" level overview (deeper than a bird's eye view of machine learning; higher than math equations) No math/programming experience required Who is it for Anyone curious about machine learning fundamentals Aspiring machine learning developers Why audio? Supplementary content for commute/exercise/chores will help solidify your book/course-work What it's not News and Interviews: TWiML and AI, O'Reilly Data Show, Talking machines Misc Topics: Linear Digressions, Data Skeptic, Learning ma...
Show notes at ocdevel.com/mlg/2 Updated! Skip to[00:29:36]for Data Science (new content) if you've already heard this episode. What is artificial intelligence, machine learning, and data science? What are their differences? AI history. Hierarchical breakdown: DS(AI(ML)). Data science: any profession dealing with data (including AI & ML). Artificial intelligence is simulated intellectual tasks. Machine Learning is algorithms trained on data to learn patterns to make predictions. Artificial Intelligence (AI) -Wikipedia Oxford Languages: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AlphaGo Movie, very good! Sub-disciplines Reasoning, problem solving Knowledge representation Planning Learning Natural language processing Perception Motion and manipulation Social intelligence General intelligence Applications Autonomous vehicl...
Show notes at ocdevel.com/mlg/3. Why should you care about AI? Inspirational topics about economic revolution, the singularity, consciousness, and fear.
Overview of machine learning algorithms. Infer/predict, error/loss, train/learn. Supervised, unsupervised, reinforcement learning. ocdevel.com/mlg/4 for notes and resources
Introduction to the first machine-learning algorithm, the 'hello world' of supervised learning - Linear Regression ocdevel.com/mlg/5 for notes and resources
Discussion on certificates and degrees from Udacity to a Masters degree. ocdevel.com/mlg/6 for notes and resources
Your first classifier: Logistic Regression. That plus Linear Regression, and you're a 101 supervised learner! ocdevel.com/mlg/7 for notes and resources
Introduction to the branches of mathematics used in machine learning. Linear algebra, statistics, calculus. ocdevel.com/mlg/8 for notes and resources
Deep learning and neural networks. How to stack our logisitic regression units into a multi-layer perceptron. ocdevel.com/mlg/9 for notes and resources
Languages & frameworks comparison. Languages: Python, R, MATLAB/Octave, Julia, Java/Scala, C/C++. Frameworks: Hadoop/Spark, Deeplearning4J, Theano, Torch, TensorFlow. ocdevel.com/mlg/10 for notes and resources