# Top 10 Machine Learning Algorithms for Beginners

All areas of Machine Learning – Supervised, Unsupervised, Semi-supervised, and Reinforcement Learning use multiple algorithms for different types of tasks such as prediction, classification, regression, etc. Each machine learning algorithm handles a specific problem, and so beginners can dive into one to find solutions, one at a time.

Here is a compilation of the best machine learning algorithms frequently used in all areas of machine learning.

Now you can practice ML algorithms here.

Forming relationships between two variables is almost the starting point of a model, and linear regression in machine learning accomplishes this. The relationship between the dependent and independent variables is established by aligning them on a regression line. Then the goal is to find the line of best fit that explains the relationship between the two variables.

The linear regression line is represented by a mathematical equation by,

y = mx + c

Where y is the dependent variable, x is the independent variable, m is the slope, and c is the y-intercept.

Now, when the dependent variable is dichotomous (binary), logistic regression is used to estimate discrete values ​​(unlike linear regression which handles continuous values) in a set of independent variables.

This algorithm is used in predictive analytics where the probability of occurrence of an event is predicted based on the logit function, which is why it is also called “logit regression”.

Mathematically, it is represented by,

y = e^(b0 + b1*x) / (1 + e^(b0 + b1*x))

Where x is the input value, y is the predicted output, b0 is the bias, and b1 is the coefficient for x.

ANNs are used in most recent AGI-related models that use self-supervised learning. This algorithm attempts to mimic the human brain by copying the behavior and connections of neurons. The structure of ANNs has three or more layers that are interconnected for processing input data.

These are used in various smart devices as well as automation devices such as automatic cars, smart speakers and lights, and much more.

Convolutional Neural Networks (CNN), one of the most used neural networks in recent developments, are a type of ANN. These are mostly computer vision based networks where the first layer is the input layer, the middle layers are the hidden layers that do this computation and the third layer is the output layer.

An optimization algorithm to minimize the cost function by updating machine learning model parameters. It is used in various machine learning algorithms and is most commonly used in deep learning. It is used in various fields such as robotics, computer games, mechanical engineering, etc.

There are three types of gradient descent algorithms:

1. Batch Gradient Descent: Processes all training data for each gradient descent iteration. If the dataset is large, this method is too expensive.
2. Stochastic Gradient Descent: This treats an example of iterative training, which results in the parameters updating each time.
3. Mini-batch gradient descent: The fastest gradient descent that processes large amounts of iterations in small batches, corresponding to similar iterations.

A supervised learning algorithm used for visualizing a map of possible outcomes for a series of decisions. Basically, it divides the data set into two or more homogeneous ones to compare the possible outcomes and then makes decisions based on advantages and probabilities.

It’s like making a list of pros and cons, and making decisions based on expectations and the potentiality of different options, but in machine learning it’s based on a mathematical construct.

Bayesian probability is a type of probability concept where, instead of the frequency of a phenomenon, the probability is interpreted by the quantification of a personal belief or knowledge representing a reasonable expectation. Naive Bayes is used for classification problems and assumes that the features of the algorithm are independent of each other and unaffected by changes to each other.

For example, the weight and size of a table may change and may be related, but do not change the fundamental fact that it is a table. This simplistic algorithm is able to handle large data sets and make real-time predictions.

Bayes’ theorem is given by,

P(X|Y) = (P(Y|X) x P(X))/P(Y)

Where P(X) is the probability that X is true, P(X/Y) is the conditional probability where X is true when Y is also true.

This supervised machine learning algorithm classifies all new cases based on stored old cases which are separated into different classes based on their similarity scores. K Nearest Neighbors (KNN) is used for both regression and classification problems.

K refers to the number of nearby points considered when segregating and classifying a set of known groups. The algorithm makes the classification by a majority vote of the K neighboring points.

The main use cases and actual applications of the algorithm can be found in recommendation systems of OTT platforms like Amazon and Netflix, as well as facial recognition systems.

For clustering tasks, K-means is an unsupervised distance-based machine learning algorithm. The algorithm classifies data sets into K clusters where in one set the data points remain homogeneous, but not in different clusters.

This algorithm is used to group Facebook users who have common interests based on their likes and dislikes, as well as to segment similar e-commerce products.

Another supervised learning algorithm, random trees is a collection of multiple decision trees that are built on different samples during training. It builds on the accuracy of decision trees by mapping decisions from different trees onto a single tree called the CART (Classification and Regression Trees) model.

This helps increase accuracy when a large portion of the data in a dataset is missing. The final prediction is based on the prediction result that is voted the highest. This algorithm is mainly used in e-commerce recommendation engines and financial models.

SVMs are supervised machine learning algorithms that plot individual data in a number of dimensional spaces, based on the number of features. The classification is performed by determining the hyper-plane that distinguishes two sets of support vectors.

Simply, SVMs are used to represent the coordinates of individual observations. These are commonly used in machine learning applications such as facial expression classification, speech recognition, and image detection.