Silver BlogTen Machine Learning Algorithms You Should Know to Become a Data Scientist

It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.

6. Feedforward Neural Networks

These are basically multilayered Logistic Regression classifiers. Many layers of weights separated by non-linearities (sigmoid, tanh, relu + softmax and the cool new selu). Another popular name for them is Multi-Layered Perceptrons. FFNNs can be used for classification and unsupervised feature learning as autoencoders.

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Multi-Layered perceptron
machine learning algorithms
FFNN as an autoencoder

FFNNs can be used to train a classifier or extract features as autoencoders

Introductory Tutorial(s):

7. Convolutional Neural Networks (Convnets)

Almost any state of the art Vision based Machine Learning result in the world today has been achieved using Convolutional Neural Networks. They can be used for Image classification, Object Detection or even segmentation of images. Invented by Yann Lecun in late 80s-early 90s, Convnets feature convolutional layers which act as hierarchical feature extractors. You can use them in text too (and even graphs).

Use convnets for state of the art image and text classification, object detection, image segmentation.
Introductory Tutorial(s):

8. Recurrent Neural Networks (RNNs):

RNNs model sequences by applying the same set of weights recursively on the aggregator state at a time t and input at a time t (Given a sequence has inputs at times 0..t..T, and have a hidden state at each time t which is output from t-1 step of RNN). Pure RNNs are rarely used now but its counterparts like LSTMs and GRUs are state of the art in most sequence modeling tasks.

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RNN (If here is a densely connected unit and a nonlinearity, nowadays f is generally LSTMs or GRUs ). LSTM unit which is used instead of a plain dense layer in a pure RNN.

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Use RNNs for any sequence modelling task specially text classification, machine translation, language modelling
Library: (Many cool NLP research papers from Google are here)
Introductory Tutorial(s):

9. Conditional Random Fields (CRFs)

CRFs are probably the most frequently used models from the family of Probabilitic Graphical Models (PGMs). They are used for sequence modeling like RNNs and can be used in combination with RNNs too. Before Neural Machine Translation systems came in CRFs were the state of the art and in many sequence tagging tasks with small datasets, they will still learn better than RNNs which require a larger amount of data to generalize. They can also be used in other structured prediction tasks like Image Segmentation etc. CRF models each element of the sequence (say a sentence) such that neighbors affect a label of a component in a sequence instead of all labels being independent of each other.

Use CRFs to tag sequences (in Text, Image, Time Series, DNA etc.)
Introductory Tutorial(s):

7 part lecture series by Hugo Larochelle on Youtube:

10. Decision Trees

Let’s say I am given an Excel sheet with data about various fruits and I have to tell which look like Apples. What I will do is ask a question “Which fruits are red and round ?” and divide all fruits which answer yes and no to the question. Now, All Red and Round fruits might not be apples and all apples won’t be red and round. So I will ask a question “Which fruits have red or yellow color hints on them? ” on red and round fruits and will ask “Which fruits are green and round ?” on not red and round fruits. Based on these questions I can tell with considerable accuracy which are apples. This cascade of questions is what a decision tree is. However, this is a decision tree based on my intuition. Intuition cannot work on high dimensional and complex data. We have to come up with the cascade of questions automatically by looking at tagged data. That is what Machine Learning based decision trees do. Earlier versions like CART trees were once used for simple data, but with bigger and larger dataset, the bias-variance tradeoff needs to solved with better algorithms. The two common decision trees algorithms used nowadays are Random Forests (which build different classifiers on a random subset of attributes and combine them for output) and Boosting Trees (which train a cascade of trees one on top of others, correcting the mistakes of ones below them).

Decision Trees can be used to classify datapoints (and even regression)

Introductory Tutorial:
TD Algorithms (Good To Have)
If you are still wondering how can any of the above methods solve tasks like defeating Go world champion like DeepMind did, they cannot. All the 10 type of algorithms we talked about before this was Pattern Recognition, not strategy learners. To learn strategy to solve a multi-step problem like winning a game of chess or playing Atari console, we need to let an agent-free in the world and learn from the rewards/penalties it faces. This type of Machine Learning is called Reinforcement Learning. A lot (not all) of recent successes in the field is a result of combining perception abilities of a Convnet or LSTM to a set of algorithms called Temporal Difference Learning. These include Q-Learning, SARSA and some other variants. These algorithms are a smart play on Bellman’s equations to get a loss function that can be trained with rewards an agent gets from the environment.

These algorithms are used to automatically play games mostly :D, also other applications in language generation and object detection.
Introductory Tutorial(s):
Grab the free Sutton and Barto book:

Watch David Silver course:

These are the 10 machine learning algorithms which you can learn to become a data scientist.

You can also read about machine learning libraries here.

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Original. Reposted with permission.