Part 1: Why Machine Learning Matters. The big picture of artificial intelligence and machine learning — past, present, and future.Part 2.1: Supervised Learning. Learning with an answer key. Introducing linear regression, loss functions, overfitting, and gradient descent.Part 2.2: Supervised Learning II. Two methods of classification: logistic regression and SVMs.Part 2.3: Supervised Learning III. Non-parametric learners: k-nearest neighbors, decision trees, random forests. Introducing cross-validation, hyperparameter tuning, and ensemble models.Part 3: Unsupervised Learning. Clustering: k-means, hierarchical. Dimensionality reduction: principal components analysis (PCA), singular value decomposition (SVD).Part 4: Neural Networks & Deep Learning. Why, where, and how deep learning works. Drawing inspiration from the brain. Convolutional neural networks (CNNs), recurrent neural networks (RNNs). Real-world applications.Part 5: Reinforcement Learning. Exploration and exploitation. Markov decision processes. Q-learning, policy learning, and deep reinforcement learning. The value learning problem.Appendix: The Best Machine Learning Resources. A curated list of resources for creating your machine learning curriculum.This guide is intended to be accessible to anyone. Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn’t necessary to have prior knowledge of them to gain value from this series.Artificial intelligence will shape our future more powerfully than any other innovation this century. Anyone who does not understand it will soon find themselves feeling left behind, waking up in a world full of technology that feels more and more like magic.The rate of acceleration is already astounding. After a couple of AI winters and periods of false hope over the past four decades, rapid advances in data storage and computer processing power have dramatically changed the game in recent years.In 2015, Google trained a conversational agent (AI) that could not only convincingly interact with humans as a tech support helpdesk, but also discuss morality, express opinions, and answer general facts-based questions.The same year, DeepMind developed an agent that surpassed human-level performance at 49 Atari games, receiving only the pixels and game score as inputs. Soon after, in 2016, DeepMind obsoleted their own achievement by releasing a new state-of-the-art gameplay method called A3C.Meanwhile, AlphaGo defeated one of the best human players at Go — an extraordinary achievement in a game dominated by humans for two decades after machines first conquered chess. Many masters could not fathom how it would be possible for a machine to grasp the full nuance and complexity of this ancient Chinese war strategy game, with its 10170 possible board positions (there are only 1080atoms in the universe).In March 2017, OpenAI created agents that invented their own language to cooperate and more effectively achieve their goal. Soon after, Facebook reportedly successfully training agents to negotiate and even lie.Just a few days ago (as of this writing), on August 11, 2017, OpenAI reached yet another incredible milestone by defeating the world’s top professionals in 1v1 matches of the online multiplayer game Dota 2.Much of our day-to-day technology is powered by artificial intelligence. Point your camera at the menu during your next trip to Taiwan and the restaurant’s selections will magically appear in English via the Google Translate app.Today AI is used to design evidence-based treatment plans for cancer patients, instantly analyze results from medical tests to escalate to the appropriate specialist immediately, and conduct scientific research for drug discovery.In everyday life, it’s increasingly commonplace to discover machines in roles traditionally occupied by humans. Really, don’t be surprised if a little housekeeping delivery bot shows up instead of a human next time you call the hotel desk to send up some toothpaste.In this series, we’ll explore the core machine learning concepts behind these technologies. By the end, you should be able to describe how they work at a conceptual level and be equipped with the tools to start building similar applications yourself.Artificial intelligence is the study of agents that perceive the world around them, form plans, and make decisions to achieve their goals. Its foundations include mathematics, logic, philosophy, probability, linguistics, neuroscience, and decision theory. Many fields fall under the umbrella of AI, such as computer vision, robotics, machine learning, and natural language processing.Machine learning is a subfield of artificial intelligence. Its goal is to enable computers to learn on their own. A machine’s learning algorithm enables it to identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models.The technologies discussed above are examples of artificial narrow intelligence (ANI), which can effectively perform a narrowly defined task.Meanwhile, we’re continuing to make foundational advances towards human-level artificial general intelligence (AGI), also known as strong AI. The definition of an AGI is an artificial intelligence that can successfully perform any intellectual task that a human being can, including learning, planning and decision-making under uncertainty, communicating in natural language, making jokes, manipulating people, trading stocks, or... reprogramming itself.And this last one is a big deal. Once we create an AI that can improve itself, it will unlock a cycle of recursive self-improvement that could lead to an intelligence explosion over some unknown time period, ranging from many decades to a single day.You may have heard this point referred to as the singularity. The term is borrowed from the gravitational singularity that occurs at the center of a black hole, an infinitely dense one-dimensional point where the laws of physics as we understand them start to break down.A recent report by the Future of Humanity Institute surveyed a panel of AI researchers on timelines for AGI, and found that “researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years” (Grace et al, 2017). We’ve personally spoken with a number of sane and reasonable AI practitioners who predict much longer timelines (the upper limit being “never”), and others whose timelines are alarmingly short — as little as a few years.The advent of greater-than-human-level artificial superintelligence (ASI) could be one of the best or worst things to happen to our species. It carries with it the immense challenge of specifying what AIs will want in a way that is friendly to humans.While it’s impossible to say what the future holds, one thing is certain: 2017 is a good time to start understanding how machines think. To go beyond the abstractions of a philosopher in an armchair and intelligently shape our roadmaps and policies with respect to AI, we must engage with the details of how machines see the world — what they “want”, their potential biases and failure modes, their temperamental quirks — just as we study psychology and neuroscience to understand how humans learn, decide, act, and feel.Machine learning is at the core of our journey towards artificial general intelligence, and in the meantime, it will change every industry and have a massive impact on our day-to-day lives. That’s why we believe it’s worth understanding machine learning, at least at a conceptual level — and we designed this series to be the best place to start.You don’t necessarily need to read the series cover-to-cover to get value out of it. Here are three suggestions on how to approach it, depending on your interests and how much time you have:Vishal most recently led growth at Upstart, a lending platform that utilizes machine learning to price credit, automate the borrowing process, and acquire users. He spends his time thinking about startups, applied cognitive science, moral philosophy, and the ethics of artificial intelligence.Samer is a Master’s student in Computer Science and Engineering at UCSD and co-founder of Conigo Labs. Prior to grad school, he founded TableScribe, a business intelligence tool for SMBs, and spent two years advising Fortune 100 companies at McKinsey. Samer previously studied Computer Science and Ethics, Politics, and Economics at Yale.Most of this series was written during a 10-day trip to the United Kingdom in a frantic blur of trains, planes, cafes, pubs and wherever else we could find a dry place to sit. Our aim was to solidify our own understanding of artificial intelligence, machine learning, and how the methods therein fit together — and hopefully create something worth sharing in the process.And now, without further ado, let’s dive into machine learning with Part 2.1: Supervised Learning!More from Machine Learning for Humans 🤖👶A special thanks to Jonathan Eng, Edoardo Conti, Grant Schneider, Sunny Kumar, Stephanie He, Tarun Wadhwa, and Sachin Maini (series editor) for their significant contributions and feedback.From a quick cheer to a standing ovation, clap to show how much you enjoyed this story.Research comms @DeepMindAI. Previously @Upstart, @Yale, @TrueVenturesTEC.Demystifying artificial intelligence & machine learning. Discussions on safe and intentional application of AI for positive social impact.