Why do we want machines to learn? This is Billy. Billy wants to buy a car. He tries to calculate how much he needs to save monthly for that. He went over dozens of ads on the internet and learned that new cars are around $20,000, used year-old ones are $19,000, 2-year old are $18,000 and so on. Billy, our brilliant analytic, starts seeing a pattern: so, the car price depends on its age and drops $1,000 every year, but won't get lower than $10,000. In machine learning terms, Billy invented regression – he predicted a value (price) based on known historical data. People do it all the time, when trying to estimate a reasonable cost for a used iPhone on eBay or figure out how many ribs to buy for a BBQ party. 200 grams per person? 500? Yeah, it would be nice to have a simple formula for every problem in the world. Especially, for a BBQ party. Unfortunately, it's impossible. Let's get back to cars. The problem is, they have different manufacturing dates, dozens of options, technical condition, seasonal demand spikes, and god only knows how many more hidden factors. An average Billy can't keep all that data in his head while calculating the price. Me too. People are dumb and lazy – we need robots to do the maths for them. So, let's go the computational way here. Let's provide the machine some data and ask it to find all hidden patterns related to price. Aaaand it works. The most exciting thing is that the machine copes with this task much better than a real person does when carefully analyzing all the dependencies in their mind. That was the birth of machine learning.

Three components of machine learning Without all the AI-bullshit, the only goal of machine learning is to predict results based on incoming data. That's it. All ML tasks can be represented this way, or it's not an ML problem from the beginning. The greater variety in the samples you have, the easier it is to find relevant patterns and predict the result. Therefore, we need three components to teach the machine: Data Want to detect spam? Get samples of spam messages. Want to forecast stocks? Find the price history. Want to find out user preferences? Parse their activities on Facebook (no, Mark, stop collecting it, enough!). The more diverse the data, the better the result. Tens of thousands of rows is the bare minimum for the desperate ones. There are two main ways to get the data — manual and automatic. Manually collected data contains far fewer errors but takes more time to collect — that makes it more expensive in general. Automatic approach is cheaper — you're gathering everything you can find and hope for the best. Some smart asses like Google use their own customers to label data for them for free. Remember ReCaptcha which forces you to "Select all street signs"? That's exactly what they're doing. Free labour! Nice. In their place, I'd start to show captcha more and more. Oh, wait... It's extremely tough to collect a good collection of data (usually called a dataset). They are so important that companies may even reveal their algorithms, but rarely datasets. Features Also known as parameters or variables. Those could be car mileage, user's gender, stock price, word frequency in the text. In other words, these are the factors for a machine to look at. When data stored in tables it's simple — features are column names. But what are they if you have 100 Gb of cat pics? We cannot consider each pixel as a feature. That's why selecting the right features usually takes way longer than all the other ML parts. That's also the main source of errors. Meatbags are always subjective. They choose only features they like or find "more important". Please, avoid being human. Algorithms Most obvious part. Any problem can be solved differently. The method you choose affects the precision, performance, and size of the final model. There is one important nuance though: if the data is crappy, even the best algorithm won't help. Sometimes it's referred as "garbage in – garbage out". So don't pay too much attention to the percentage of accuracy, try to acquire more data first.

Learning vs Intelligence Once I saw an article titled "Will neural networks replace machine learning?" on some hipster media website. These media guys always call any shitty linear regression at least artificial intelligence, almost SkyNet. Here is a simple picture to show who is who. Artificial intelligence is the name of a whole knowledge field, similar to biology or chemistry. Machine Learning is a part of artificial intelligence. An important part, but not the only one. Neural Networks are one of machine learning types. A popular one, but there are other good guys in the class. Deep Learning is a modern method of building, training, and using neural networks. Basically, it's a new architecture. Nowadays in practice, no one separates deep learning from the "ordinary networks". We even use the same libraries for them. To not look like a dumbass, it's better just name the type of network and avoid buzzwords. The general rule is to compare things on the same level. That's why the phrase "will neural nets replace machine learning" sounds like "will the wheels replace cars". Dear media, it's compromising your reputation a lot. Machine can Machine cannot Forecast Create something new Memorize Get smart really fast Reproduce Go beyond their task Choose best item Kill all humans

If you are too lazy for long reads, take a look at the picture below to get some understanding. Always important to remember — there is never a sole way to solve a problem in the machine learning world. There are always several algorithms that fit, and you have to choose which one fits better. Everything can be solved with a neural network, of course, but who will pay for all these GeForces? Let's start with a basic overview. Nowadays there are four main directions in machine learning.

The first methods came from pure statistics in the '50s. They solved formal math tasks — searching for patterns in numbers, evaluating the proximity of data points, and calculating vectors' directions. Nowadays, half of the Internet is working on these algorithms. When you see a list of articles to "read next" or your bank blocks your card at random gas station in the middle of nowhere, most likely it's the work of one of those little guys. Big tech companies are huge fans of neural networks. Obviously. For them, 2% accuracy is an additional 2 billion in revenue. But when you are small, it doesn't make sense. I heard stories of the teams spending a year on a new recommendation algorithm for their e-commerce website, before discovering that 99% of traffic came from search engines. Their algorithms were useless. Most users didn't even open the main page. Despite the popularity, classical approaches are so natural that you could easily explain them to a toddler. They are like basic arithmetic — we use it every day, without even thinking.

1.1 Supervised Learning Classical machine learning is often divided into two categories – Supervised and Unsupervised Learning. In the first case, the machine has a "supervisor" or a "teacher" who gives the machine all the answers, like whether it's a cat in the picture or a dog. The teacher has already divided (labeled) the data into cats and dogs, and the machine is using these examples to learn. One by one. Dog by cat. Unsupervised learning means the machine is left on its own with a pile of animal photos and a task to find out who's who. Data is not labeled, there's no teacher, the machine is trying to find any patterns on its own. We'll talk about these methods below. Clearly, the machine will learn faster with a teacher, so it's more commonly used in real-life tasks. There are two types of such tasks: classification – an object's category prediction, and regression – prediction of a specific point on a numeric axis.

Classification "Splits objects based at one of the attributes known beforehand. Separate socks by based on color, documents based on language, music by genre" Today used for:

– Spam filtering

– Language detection

– A search of similar documents

– Sentiment analysis

– Recognition of handwritten characters and numbers

– Fraud detection Popular algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbours, Support Vector Machine From here onward you can comment with additional information for these sections. Feel free to write your examples of tasks. Everything is written here based on my own subjective experience.

Machine learning is about classifying things, mostly. The machine here is like a baby learning to sort toys: here's a robot, here's a car, here's a robo-car... Oh, wait. Error! Error! In classification, you always need a teacher. The data should be labeled with features so the machine could assign the classes based on them. Everything could be classified — users based on interests (as algorithmic feeds do), articles based on language and topic (that's important for search engines), music based on genre (Spotify playlists), and even your emails. In spam filtering the Naive Bayes algorithm was widely used. The machine counts the number of "viagra" mentions in spam and normal mail, then it multiplies both probabilities using the Bayes equation, sums the results and yay, we have Machine Learning. Later, spammers learned how to deal with Bayesian filters by adding lots of "good" words at the end of the email. Ironically, the method was called Bayesian poisoning. Naive Bayes went down in history as the most elegant and first practically useful one, but now other algorithms are used for spam filtering. Here's another practical example of classification. Let's say you need some money on credit. How will the bank know if you'll pay it back or not? There's no way to know for sure. But the bank has lots of profiles of people who took money before. They have data about age, education, occupation and salary and – most importantly – the fact of paying the money back. Or not. Using this data, we can teach the machine to find the patterns and get the answer. There's no issue with getting an answer. The issue is that the bank can't blindly trust the machine answer. What if there's a system failure, hacker attack or a quick fix from a drunk senior. To deal with it, we have Decision Trees. All the data automatically divided to yes/no questions. They could sound a bit weird from a human perspective, e.g., whether the creditor earns more than $128.12? Though, the machine comes up with such questions to split the data best at each step. That's how a tree is made. The higher the branch — the broader the question. Any analyst can take it and explain afterward. He may not understand it, but explain easily! (typical analyst) Decision trees are widely used in high responsibility spheres: diagnostics, medicine, and finances. The two most popular algorithms for forming the trees are CART and C4.5. Pure decision trees are rarely used today. However, they often set the basis for large systems, and their ensembles even work better than neural networks. We'll talk about that later. When you google something, that's precisely the bunch of dumb trees which are looking for a range of answers for you. Search engines love them because they're fast. Support Vector Machines (SVM) is rightfully the most popular method of classical classification. It was used to classify everything in existence: plants by appearance in photos, documents by categories, etc. The idea behind SVM is simple – it's trying to draw two lines between your data points with the largest margin between them. Look at the picture: There's one very useful side of the classification — anomaly detection. When a feature does not fit any of the classes, we highlight it. Now that's used in medicine — on MRIs, computers highlight all the suspicious areas or deviations of the test. Stock markets use it to detect abnormal behaviour of traders to find the insiders. When teaching the computer the right things, we automatically teach it what things are wrong. Today, neural networks are more frequently used for classification. Well, that's what they were created for. The rule of thumb is the more complex the data, the more complex the algorithm. For text, numbers, and tables, I'd choose the classical approach. The models are smaller there, they learn faster and work more clearly. For pictures, video and all other complicated big data things, I'd definitely look at neural networks. Just five years ago you could find a face classifier built on SVM. Today it's easier to choose from hundreds of pre-trained networks. Nothing has changed for spam filters, though. They are still written with SVM. And there's no good reason to switch from it anywhere. Even my website has SVM-based spam detection in comments ¯_(ツ)_/¯

Regression "Draw a line through these dots. Yep, that's the machine learning" Today this is used for: Stock price forecasts

Demand and sales volume analysis

Medical diagnosis

Any number-time correlations Popular algorithms are Linear and Polynomial regressions.

Regression is basically classification where we forecast a number instead of category. Examples are car price by its mileage, traffic by time of the day, demand volume by growth of the company etc. Regression is perfect when something depends on time. Everyone who works with finance and analysis loves regression. It's even built-in to Excel. And it's super smooth inside — the machine simply tries to draw a line that indicates average correlation. Though, unlike a person with a pen and a whiteboard, machine does so with mathematical accuracy, calculating the average interval to every dot. When the line is straight — it's a linear regression, when it's curved – polynomial. These are two major types of regression. The other ones are more exotic. Logistic regression is a black sheep in the flock. Don't let it trick you, as it's a classification method, not regression. It's okay to mess with regression and classification, though. Many classifiers turn into regression after some tuning. We can not only define the class of the object but memorize how close it is. Here comes a regression. If you want to get deeper into this, check these series: Machine Learning for Humans. I really love and recommend it!

1.2 Unsupervised learning Unsupervised was invented a bit later, in the '90s. It is used less often, but sometimes we simply have no choice. Labeled data is luxury. But what if I want to create, let's say, a bus classifier? Should I manually take photos of million fucking buses on the streets and label each of them? No way, that will take a lifetime, and I still have so many games not played on my Steam account. There's a little hope for capitalism in this case. Thanks to social stratification, we have millions of cheap workers and services like Mechanical Turk who are ready to complete your task for $0.05. And that's how things usually get done here. Or you can try to use unsupervised learning. But I can't remember any good practical application for it, though. It's usually useful for exploratory data analysis but not as the main algorithm. Specially trained meatbag with Oxford degree feeds the machine with a ton of garbage and watches it. Are there any clusters? No. Any visible relations? No. Well, continue then. You wanted to work in data science, right?

Clustering "Divides objects based on unknown features. Machine chooses the best way" Nowadays used: For market segmentation (types of customers, loyalty)

To merge close points on a map

For image compression

To analyze and label new data

To detect abnormal behavior Popular algorithms: K-means_clustering, Mean-Shift, DBSCAN

Clustering is a classification with no predefined classes. It’s like dividing socks by color when you don't remember all the colors you have. Clustering algorithm trying to find similar (by some features) objects and merge them in a cluster. Those who have lots of similar features are joined in one class. With some algorithms, you even can specify the exact number of clusters you want. An excellent example of clustering — markers on web maps. When you're looking for all vegan restaurants around, the clustering engine groups them to blobs with a number. Otherwise, your browser would freeze, trying to draw all three million vegan restaurants in that hipster downtown. Apple Photos and Google Photos use more complex clustering. They're looking for faces in photos to create albums of your friends. The app doesn't know how many friends you have and how they look, but it's trying to find the common facial features. Typical clustering. Another popular issue is image compression. When saving the image to PNG you can set the palette, let's say, to 32 colors. It means clustering will find all the "reddish" pixels, calculate the "average red" and set it for all the red pixels. Fewer colors — lower file size — profit! However, you may have problems with colors like Cyan◼︎-like colors. Is it green or blue? Here comes the K-Means algorithm. It randomly sets 32 color dots in the palette. Now, those are centroids. The remaining points are marked as assigned to the nearest centroid. Thus, we get kind of galaxies around these 32 colors. Then we're moving the centroid to the center of its galaxy and repeat that until centroids stop moving. All done. Clusters defined, stable, and there are exactly 32 of them. Here is a more real-world explanation: Searching for the centroids is convenient. Though, in real life clusters not always circles. Let's imagine you're a geologist. And you need to find some similar minerals on the map. In that case, the clusters can be weirdly shaped and even nested. Also, you don't even know how many of them to expect. 10? 100? K-means does not fit here, but DBSCAN can be helpful. Let's say, our dots are people at the town square. Find any three people standing close to each other and ask them to hold hands. Then, tell them to start grabbing hands of those neighbors they can reach. And so on, and so on until no one else can take anyone's hand. That's our first cluster. Repeat the process until everyone is clustered. Done. A nice bonus: a person who has no one to hold hands with — is an anomaly. It all looks cool in motion: Interested in clustering? Check out this piece The 5 Clustering Algorithms Data Scientists Need to Know Just like classification, clustering could be used to detect anomalies. User behaves abnormally after signing up? Let the machine ban him temporarily and create a ticket for the support to check it. Maybe it's a bot. We don't even need to know what "normal behavior" is, we just upload all user actions to our model and let the machine decide if it's a "typical" user or not. This approach doesn't work that well compared to the classification one, but it never hurts to try.

Previously these methods were used by hardcore data scientists, who had to find "something interesting" in huge piles of numbers. When Excel charts didn't help, they forced machines to do the pattern-finding. That's how they got Dimension Reduction or Feature Learning methods. Projecting 2D-data to a line (PCA) It is always more convenient for people to use abstractions, not a bunch of fragmented features. For example, we can merge all dogs with triangle ears, long noses, and big tails to a nice abstraction — "shepherd". Yes, we're losing some information about the specific shepherds, but the new abstraction is much more useful for naming and explaining purposes. As a bonus, such "abstracted" models learn faster, overfit less and use a lower number of features. These algorithms became an amazing tool for Topic Modeling. We can abstract from specific words to their meanings. This is what Latent semantic analysis (LSA) does. It is based on how frequently you see the word on the exact topic. Like, there are more tech terms in tech articles, for sure. The names of politicians are mostly found in political news, etc. Yes, we can just make clusters from all the words at the articles, but we will lose all the important connections (for example the same meaning of battery and accumulator in different documents). LSA will handle it properly, that's why its called "latent semantic". So we need to connect the words and documents into one feature to keep these latent connections — it turns out that Singular decomposition (SVD) nails this task, revealing useful topic clusters from seen-together words. Recommender Systems and Collaborative Filtering is another super-popular use of the dimensionality reduction method. Seems like if you use it to abstract user ratings, you get a great system to recommend movies, music, games and whatever you want. Here I can recommend my favorite book "Programming Collective Intelligence". It was my bedside book while studying at university! It's barely possible to fully understand this machine abstraction, but it's possible to see some correlations on a closer look. Some of them correlate with user's age — kids play Minecraft and watch cartoons more; others correlate with movie genre or user hobbies. Machines get these high-level concepts even without understanding them, based only on knowledge of user ratings. Nicely done, Mr.Computer. Now we can write a thesis on why bearded lumberjacks love My Little Pony.

Association rule learning "Look for patterns in the orders' stream" Nowadays is used: To forecast sales and discounts

To analyze goods bought together

To place the products on the shelves

To analyze web surfing patterns Popular algorithms: Apriori, Euclat, FP-growth

This includes all the methods to analyze shopping carts, automate marketing strategy, and other event-related tasks. When you have a sequence of something and want to find patterns in it — try these thingys. Say, a customer takes a six-pack of beers and goes to the checkout. Should we place peanuts on the way? How often do people buy them together? Yes, it probably works for beer and peanuts, but what other sequences can we predict? Can a small change in the arrangement of goods lead to a significant increase in profits? Same goes for e-commerce. The task is even more interesting there — what is the customer going to buy next time? No idea why rule-learning seems to be the least elaborated upon category of machine learning. Classical methods are based on a head-on look through all the bought goods using trees or sets. Algorithms can only search for patterns, but cannot generalize or reproduce those on new examples. In the real world, every big retailer builds their own proprietary solution, so nooo revolutions here for you. The highest level of tech here — recommender systems. Though, I may be not aware of a breakthrough in the area. Let me know in the comments if you have something to share.

"Throw a robot into a maze and let it find an exit" Nowadays used for: Self-driving cars

Robot vacuums

Games

Automating trading

Enterprise resource management Popular algorithms: Q-Learning, SARSA, DQN, A3C, Genetic algorithm

Finally, we get to something looks like real artificial intelligence. In lots of articles reinforcement learning is placed somewhere in between of supervised and unsupervised learning. They have nothing in common! Is this because of the name? Reinforcement learning is used in cases when your problem is not related to data at all, but you have an environment to live in. Like a video game world or a city for self-driving car. Neural network plays Mario Knowledge of all the road rules in the world will not teach the autopilot how to drive on the roads. Regardless of how much data we collect, we still can't foresee all the possible situations. This is why its goal is to minimize error, not to predict all the moves. Surviving in an environment is a core idea of reinforcement learning. Throw poor little robot into real life, punish it for errors and reward it for right deeds. Same way we teach our kids, right? More effective way here — to build a virtual city and let self-driving car to learn all its tricks there first. That's exactly how we train auto-pilots right now. Create a virtual city based on a real map, populate with pedestrians and let the car learn to kill as few people as possible. When the robot is reasonably confident in this artificial GTA, it's freed to test in the real streets. Fun! There may be two different approaches — Model-Based and Model-Free. Model-Based means that car needs to memorize a map or its parts. That's a pretty outdated approach since it's impossible for the poor self-driving car to memorize the whole planet. In Model-Free learning, the car doesn't memorize every movement but tries to generalize situations and act rationally while obtaining a maximum reward. Remember the news about AI beating a top player at the game of Go? Despite shortly before this it being proved that the number of combinations in this game is greater than the number of atoms in the universe. This means the machine could not remember all the combinations and thereby win Go (as it did chess). At each turn, it simply chose the best move for each situation, and it did well enough to outplay a human meatbag. This approach is a core concept behind Q-learning and its derivatives (SARSA & DQN). 'Q' in the name stands for "Quality" as a robot learns to perform the most "qualitative" action in each situation and all the situations are memorized as a simple markovian process. Such a machine can test billions of situations in a virtual environment, remembering which solutions led to greater reward. But how can it distinguish previously seen situations from a completely new one? If a self-driving car is at a road crossing and the traffic light turns green — does it mean it can go now? What if there's an ambulance rushing through a street nearby? The answer today is "no one knows". There's no easy answer. Researchers are constantly searching for it but meanwhile only finding workarounds. Some would hardcode all the situations manually that let them solve exceptional cases, like the trolley problem. Others would go deep and let neural networks do the job of figuring it out. This led us to the evolution of Q-learning called Deep Q-Network (DQN). But they are not a silver bullet either. Reinforcement Learning for an average person would look like a real artificial intelligence. Because it makes you think wow, this machine is making decisions in real life situations! This topic is hyped right now, it's advancing with incredible pace and intersecting with a neural network to clean your floor more accurately. Amazing world of technologies! Off-topic. When I was a student, genetic algorithms (link has cool visualization) were really popular. This is about throwing a bunch of robots into a single environment and making them try reaching the goal until they die. Then we pick the best ones, cross them, mutate some genes and rerun the simulation. After a few milliard years, we will get an intelligent creature. Probably. Evolution at its finest. Genetic algorithms are considered as part of reinforcement learning and they have the most important feature proved by decade-long practice: no one gives a shit about them. Humanity still couldn't come up with a task where those would be more effective than other methods. But they are great for student experiments and let people get their university supervisors excited about "artificial intelligence" without too much labour. And youtube would love it as well.

"Bunch of stupid trees learning to correct errors of each other" Nowadays is used for: Everything that fits classical algorithm approaches (but works better)

Search systems (★)

Computer vision

Object detection Popular algorithms: Random Forest, Gradient Boosting

It's time for modern, grown-up methods. Ensembles and neural networks are two main fighters paving our path to a singularity. Today they are producing the most accurate results and are widely used in production. However, the neural networks got all the hype today, while the words like "boosting" or "bagging" are scarce hipsters on TechCrunch. Despite all the effectiveness the idea behind these is overly simple. If you take a bunch of inefficient algorithms and force them to correct each other's mistakes, the overall quality of a system will be higher than even the best individual algorithms. You'll get even better results if you take the most unstable algorithms that are predicting completely different results on small noise in input data. Like Regression and Decision Trees. These algorithms are so sensitive to even a single outlier in input data to have models go mad. In fact, this is what we need. We can use any algorithm we know to create an ensemble. Just throw a bunch of classifiers, spice it up with regression and don't forget to measure accuracy. From my experience: don't even try a Bayes or kNN here. Although "dumb", they are really stable. That's boring and predictable. Like your ex. Instead, there are three battle-tested methods to create ensembles. Stacking Output of several parallel models is passed as input to the last one which makes a final decision. Like that girl who asks her girlfriends whether to meet with you in order to make the final decision herself. Emphasis here on the word "different". Mixing the same algorithms on the same data would make no sense. The choice of algorithms is completely up to you. However, for final decision-making model, regression is usually a good choice. Based on my experience stacking is less popular in practice, because two other methods are giving better accuracy. Bagging aka Bootstrap AGGregatING. Use the same algorithm but train it on different subsets of original data. In the end — just average answers. Data in random subsets may repeat. For example, from a set like "1-2-3" we can get subsets like "2-2-3", "1-2-2", "3-1-2" and so on. We use these new datasets to teach the same algorithm several times and then predict the final answer via simple majority voting. The most famous example of bagging is the Random Forest algorithm, which is simply bagging on the decision trees (which were illustrated above). When you open your phone's camera app and see it drawing boxes around people's faces — it's probably the results of Random Forest work. Neural networks would be too slow to run real-time yet bagging is ideal given it can calculate trees on all the shaders of a video card or on these new fancy ML processors. In some tasks, the ability of the Random Forest to run in parallel is more important than a small loss in accuracy to the boosting, for example. Especially in real-time processing. There is always a trade-off. Boosting Algorithms are trained one by one sequentially. Each subsequent one paying most of its attention to data points that were mispredicted by the previous one. Repeat until you are happy. Same as in bagging, we use subsets of our data but this time they are not randomly generated. Now, in each subsample we take a part of the data the previous algorithm failed to process. Thus, we make a new algorithm learn to fix the errors of the previous one. The main advantage here — a very high, even illegal in some countries precision of classification that all cool kids can envy. The cons were already called out — it doesn't parallelize. But it's still faster than neural networks. It's like a race between a dump truck and a racecar. The truck can do more, but if you want to go fast — take a car. If you want a real example of boosting — open Facebook or Google and start typing in a search query. Can you hear an army of trees roaring and smashing together to sort results by relevancy? That's because they are using boosting. Nowadays there are three popular tools for boosting, you can read a comparative report in CatBoost vs. LightGBM vs. XGBoost

"We have a thousand-layer network, dozens of video cards, but still no idea where to use it. Let's generate cat pics!" Used today for: Replacement of all algorithms above

Object identification on photos and videos

Speech recognition and synthesis

Image processing, style transfer

Machine translation Popular architectures: Perceptron, Convolutional Network (CNN), Recurrent Networks (RNN), Autoencoders