AI News, Top 10 Machine Learning Algorithms for Beginners

Top 10 Machine Learning Algorithms for Beginners

The study of ML algorithms has gained immense traction post the Harvard Business Review article terming a ‘Data Scientist’ as the ‘Sexiest job of the 21st century’.

So, for those starting out in the field of ML, we decided to do a reboot of our immensely popular Gold blog The 10 Algorithms Machine Learning Engineers need to know - albeit this post is targetted towards beginners.

or ‘instance-based learning’, where a class label is produced for a new instance by comparing the new instance (row) to instances from the training data, which were stored in memory.

Supervised learning: Supervised learning can be explained as follows: use labeled training data to learn the mapping function from the input variables (X) to the output variable (Y).

Reinforcement learning: Reinforcement learning is a type of machine learning algorithm that allows the agent to decide the best next action based on its current state, by learning behaviours that will maximize the reward.

They are typically used in robotics – where a robot can learn to avoid collisions by receiving negative feedback after bumping into obstacles, and in video games – where trial and error reveals specific movements that can shoot up a player’s rewards.

However, such lists are subjective and as in the case of the quoted paper, the sample size of the polled participants is very narrow and consists of advanced practitioners of data mining.

Logistic Regression Linear regression predictions are continuous values (rainfall in cm),logistic regression predictions are discrete values (whether a student passed/failed) after applying a transformation function.

Logistic regression is best suited for binary classification (datasets where y = 0 or 1, where 1 denotes the default class.

The output (y-value) is generated by log transforming the x-value, using the logistic function h(x)= 1/ (1 + e^ -x) .

The logistic regression equation P(x) = e ^ (b0 +b1*x) / (1 + e^(b0 + b1*x)) can be transformed into ln(p(x) / 1-p(x)) = b0 + b1*x.

The goal of logistic regression is to use the training data to find the values of coefficients b0 and b1 such that it will minimize the error between the predicted outcome and the actual outcome.

The model is used as follows to make predictions: walk the splits of the tree to arrive at a leaf node and output the value present at the leaf node.

To calculate the probability of an outcome given the value of some variable, that is, to calculate the probability of a hypothesis(h) being true, given our prior knowledge(d), we use Bayes’ Theorem as follows: P(h|d)= (P(d|h) * P(h)) / P(d) where This algorithm is called ‘naive’ because it assumes that all the variables are independent of each other, which is a naive assumption to make in real-world examples.

When an outcome is required for a new data instance, the KNN algorithm goes through the entire dataset to find the k-nearest instances to the new instance, or the k number of instances most similar to the new record, and then outputs the mean of the outcomes (for a regression problem) or the mode (most frequent class) for a classification problem.

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