Top 10 Machine Learning Algorithms for Beginners: Supervised, Unsupervised and More

Machine learning is the kingpin that towered above the deck of data science. Being the subset of Artificial Intelligence, it’s the big cheese today. Its zoned-out scope is magnetizing the present generation. As Beginners, you may stumble. No hanging back! Machine Learning Algorithms are your knights in shining armor to rescue you.

 

ABCs of Machine Learning

Be it Google’s ‘Google Assistant, Apple’s Siri, Amazon’s Alexa, or Microsoft’s Cortana, friends, today we speak with machines, and surprisingly, machine answers us. Cars are self-driving; weather forecasts, risk predictors of health ailments, robotic surgeries, and several automated household machines that perform tasks smartly, for instance, are the magic of Artificial intelligence, Superintelligence, and Machine learning.

Isn’t that Amazing!! It is!

Straightaway, how is this possible? Indeed, what is Machine Learning? Can machines learn?

If the answer is yes, how?

These questions will run in the minds of curious aspirers of machine learning and artificial intelligence.

Thankfully, this blog answers all these quires and will also introduce you to your knights in shining armor – the Machine Learning Algorithms.

Stay tucked in.                  

Firstly, What Is Machine Learning?

Be it Google’s ‘Google Assistant, Apple’s Siri, Amazon’s Alexa, or Microsoft’s Cortana, friends, today we speak with machines, amazingly and surprisingly, machine answers us.

The most compelling evidence is that machines a decade ago were meant to aid man and lessen his efforts in particular jobs or professions. To our surprise, today machines have taken a leap. Speaking frankly, they are ahead of the man performing unusual things even greater than what a man can perform. They turned out to be smarter than their creators. Accordingly, machines can also handle high-level computational decisions in almost all sectors.

According to Arthur Lee Samuel (1959), machine learning, a sub-domain of Artificial Intelligence, is the discipline that offers computers the competence to learn without being categorically programmed and make prudent decisions. Thereby, this competence makes the computer perform all the activities similar to man. 

Why do we need Machine Learning?

• Data is the rallying point that decides an organizational success and failure. Hence, Machine Learning or ML derives insights from the data and makes decisions that nail a company’s ballgame. 
• On account of COVID-19, every aspect of life has become online. From academics to the stock market, daily grinds to the healthcare sector, ML has become a necessity.
• Correspondingly, the whole caboodle requires ML in areas like face recognition, pattern recognition, speech recognition, etc.

Scope of Machine Learning

Machine Learning –

• Uniquely, offers the century’s lucrative career choice.
• Explicitly, yields plenty of job opportunities with high-paid salaries. 
• Likewise, makes drastic changes in the world of automation and as the digital world is growing in leaps and bounds where ML has a prominent role. 

How do Machines Learn?

Of course, Machine learning is immensely complex. Moreover, its work differs depending on the mission and algorithm used to chalk it. 

The machine learning working model has three parts:

• Making determinations with computational algorithms.
• Elements and Variables that derive decisions and insights.
• The result of a program is already known, it trains the system to learn.

To sum up, the ML model is programmed in a way where the answer is known. For this, the algorithm is run with some modifications until the output matches the known answers. Subsequently, the system is fed with additory data to make it learn and process it and finally arise at high insights.

Yes, friends, this is all about the quires! I believe that I gave you the right stuff.

Machine Learning Algorithms

Now let us check your knights, the Machine Learning Algorithms. These are the tools that help the newbies to jump-start their careers as they are presented with examples for easy understanding and practicing.

Firstly, in this blog, let us learn the types of machine learning. Check them out.

Types of Machine Learning Algorithms

Notably, in a given situation, the machine learning algorithms work on par with human intervention/reinforcement. The four major machine learning types are 

• supervised learning, 
• unsupervised learning, 
• semi-supervised learning and 
• reinforcement learning.

Supervised Learning

Supervised learning gives the computer a labeled set of data to learn and perform a human task. Here it replicates human expertise. It is the less complex model of all.

Correspondingly, Supervised Learning has two sets of variables. Firstly, the target variable, or labels which are the ones we want to predict, and features that help us to predict the target variables. Given the features and labels associated with these features, the program chiefly finds the underlying pattern in the data.

To summarize, the model has input variables (x) and output variables (y), and the algorithm identifies the mapping function between them and represents the relationship as y = f(x).

Examples of the supervised learning problems are:

• Regression problems – trains the model with historical data to predict future values. 
• Classification problems – This teaches the algorithm with various labels to identify items within a specific data set.

Unsupervised Learning

Unsupervised learning provides unlabeled data to the computer and extracts the impressive unknown patterns/insights from it that were formerly unknown. 

Ultimately, the goal is to decode the insights underpinned in the data to gain more knowledge about it.

Though a complex model, there are various ways that machine learning algorithms do this, such as:

• Clustering – the computer creates clusters or groups of data from uniform data points within a data set. For example, clustering customers based on their search history.
• Density estimation – the computer draws insights from the data depending on the dataset distribution.
• Anomaly detection – here, the computer identifies the significantly different form of the data within the given data points. 
• Principal component analysis (PCA) – the computer does the fishing expedition of a data set and compiles it to make accurate predictions.

Semi-supervised learning

Semi-supervised learning provides the computer with a set of partially labeled data, and by using this labeled data, it tries to understand the parameters that interpret the unlabeled data.

Reinforcement learning

Reinforcement learning is an iterative approach where the computer requires some reinforcement signals. The computer observes its environment, and using that data, it identifies the ideal behavior and thus minimizes the risks and or maximizes the reward. 

The machine learns to achieve a goal in a complex or uncertain situation. Every bucket list bagged receives a reward during the learning period. In other words, when the training data set is absent, the machine learns per se.

Types of Machine Learning Algorithms

Here arrive the most long-winded and challenging methodologies of your machine learning quest – learning algorithms. In a simple statement, Algorithms are automated instructions. There are plenteous algorithms in Machine Learning.

Let me give you a common principle for your easy understanding before we dive deeper into the subject.

A common principle that underlies all supervised machine learning algorithms is:

Machine learning algorithms are learning a target function (f) that best maps input variables (X) to an output variable (Y): Y = f(X)

A common principle that underlies all supervised machine learning algorithms is:         Y = f(X)
where (f) is a target function (X) is the input variable and (Y) is the output variable

The Top 10 Machine Learning Algorithms

A. SUPERVISED LEARNING

1. Linear Regression

It is the most simple Machine learning algorithm falling under the Supervised Learning technique and solves regression problems.

• With the help of independent variables, it predicts the continuous dependent variable.
• Its goal is to find the best fit line that accurately predicts the output for the continuous dependent variable.
• Prediction using a single independent variable is Simple Linear Regression, while in the case of more than two independent variables, it is called Multiple Linear Regression.
• After finding the best fit line, the algorithm establishes the relationship between the dependent variable and independent variables that should be linear.
• In Linear Regression, the equation to express the relationship is y = a + bx. Hence, linear regressions’ goal is to find the values of coefficients a and b, where a is the intercept and b is the slope of the line.
• The linear regression output should be the continuous values only, such as rainfall (in cms), temperature, sales, product price, salary, age, etc. the below image depicts the relationship between the dependent variable and independent variable:

2. Logistic Regression

It is one of the most popular Machine learning algorithms borrowed from statistics and falls under Supervised Learning techniques.

It is handy for classification and regression problems but mainly used for Classification problems.

Logistic regression predicts the categorical dependent variable with the help of independent variables. It envisages discrete values by applying a nonlinear function called the logistic function to transform the data.

• The Logistic Regression problem output can be only between 0 and 1.
• Logistic regression implies where the probabilities between two classes are necessary. For example, whether a tumor is malignant or not, either 0 or 1, true or false, etc.
• The concept of Logistic regression is Maximum Likelihood estimation, where the observed data should be most probable, i.e., it trains the data to find the values of coefficients b0 and b1 – so that it minimizes the error between the predicted and the actual outcome.
• Notably, in logistic regression, the weighted sum of inputs passes through an activation function that maps values between 0 and 1. The assumed activation function is known as the sigmoid function h(x)= 1/ (1 + e^x), and the curve obtained is known as the sigmoid curve or S-curve. Consider the below image:

• The equation for logistic regression is:

• The output (y-value) is generated by log transforming the x-value, using the logistic function h(x)= 1/ (1 + e^ -x) . Then apply a threshold to convert this probability into a binary classification.
• Try the below-listed ones to improve the logistic regression model
  1. interaction terms
  2. using a non-linear model
  3. removing features
  4. regularize techniques

Decision Tree Classification Algorithm

• One of the supervised learning techniques is the Decision Tree technique useful for both classification and regression problems but is chiefly preferred to solve Classification problems.
• We build a Decision Tree using the Classification and Regression Tree algorithm (CART algorithm).
• It is a tree-structured classifier with a root node and expanding branches. The internal nodes represent the features, the branches represent the decision rules, and the leaf nodes represent the outcomes of a dataset.
• The Decision tree has two nodes: Decision Node and Leaf Node. A decision node makes any decision and has multiple branches, while Leaf nodes represent the output of those decisions. Leaf nodes do not contain any further branching.
• Features of a dataset form the basis for the decisions or the tests to be performed.
• In a decision tree, you ask a question, the answer for which is a simple (Yes/No), based on which the tree further splits into subtrees or branches.
• It is a graphical representation of all the possible solutions. Data can be categorical or numerical.
• The below diagram explains the general structure of a decision tree:

Let us understand it with an example, say determining the fitness of a person.

4. K-Nearest Neighbor(KNN) Algorithm for Machine Learning

• The fourth one among Supervised Learning techniques is K-Nearest Neighbour (K-NN) with a simple Machine Learning algorithm.
• K-NN algorithm gathers or finds the similarity between the new data and available data and puts the new data into the most matching category present.
• It does not get trained but eventually stores a large amount of data and categorizes new data according to the available data. Hence, it is also known as the lazy learner algorithm.
• K-NN algorithm stores all the available data and so needs a lot of memory or space to store it and then retrieve it just on time. Simply this implies that when new data appears, it classifies the data into a well-suited category. 
• K-NN algorithm can solve both Regression and Classification problems. It might be a mean output variable for regression problems and a mode output variable for classification problems. 
• K-NN is a non-parametric algorithm for it does not make any assumptions about the underlying data.
• An example of applying K-NN

4. Naïve Bayes Classifier Algorithm

• This algorithm uses Bayes’s Theorem to calculate the probability of chances for an event to occur while another event has already unfolded.
• In order, to calculate the probability of a hypothesis(h) being true, we use Bayes’s Theorem, where our prior knowledge(d) is as follows:

P(h|d)= (P(d|h) P(h)) / P(d)

where:

• P(h|d) is Posterior probability.
• P(d|h) is Likelihood.
• P(h) is Class prior probability.
• P(d) = Predictor prior probability.

So, it gets its name ‘naive’ as it assumes all the variables to be independent of each other, which is a naive assumption to make in real-world examples.

B. UNSUPERVISED LEARNING

5. K-means Clustering Algorithm

• K-means clustering, a non-hierarchical approach applied widely for large dataset applications to form good clusters. 
• Generally, it determines the number of clusters before preparing the model itself.
• Once the model is ready, it measures the K values by some evaluation techniques. 
• K-means is an iterative process for it calculates the distance from each observation to the centroids present. Further, the algorithm process requires the data to be in the proper format. If the data is with different units of measure, bring all the variables into one unit/ measure and use the process of Scaling for further algorithm processing. 
• Process of Scaling is by either Z Scaling or Min-Max Scaling. 
• Example:

5. Principal component analysis

• Principal component analysis (PCA) is a type of dimensionality reduction algorithm, in which feature extraction helps to reduce redundancies and compress datasets.
• It uses linear transformation and creates new data representation, thus fabricating a set of “principal components.”
• Direction is the first principal component. The variance of the dataset will get maximized.
• While the second principal component is completely uncorrelated to the first one. Further, it also finds the maximum variance in the data, but yields in a direction that is perpendicular, or orthogonal, to the first component.
• This process keeps repeating based on the number of dimensions. So, the next principal component occurs in most variance in the direction orthogonal to the prior components.
• It carries and maintains the complexity of data as much as possible.

6. Apriori Algorithms

• Also known as the Frequent itemset mining algorithm or it is an algorithm behind “You may also like” seen in the recommendations scoop.
• One among the unsupervised learning is an Apriori algorithm used for Association Rule Mining. 
• Apriori searches the datasets for a series of frequent sets of items. Its working principle basis is the association and correlation between the itemsets. 
• Generally, the apriori algorithm operates on a database containing huge transactions. For example, the items customers buy at Reliance stores or City Central.
• It attracts the customers to buy their products effortlessly and thus, increases the sales performance of the particular store by employing recommendation engines with tags such as You may also like, Also bought together, etc.
• Moreover, Apriori uses ARM (Associate Rule Mining), a crucial technique in data science. ARM identifies the frequency of itemsets, patterns, and associations in a given dataset and predicts the most relevant subsequent item in the set. This ARM technique performs based on customers’ previous purchases which help in business decision-making.
• Example: In a grocery store, if a person purchases items A, B, and C, the chances of him buying D & E are calculated and predicted by the Associate Rule Mining technique.

C. ENSEMBLE LEARNING

• Whenever we need to make an important decision, we will gather as much information as possible to know the information we browse and ask friends, colleagues, or even strangers. In this process of gathering more information, we, along with others, conglomerate the decision-making process.
• Similarly, Machine learning predictions also follow the above principle. Many cases require more than one model. We may need multiple models and train those models to make predictions.
• Finally, depending on the given inputs, the model processes them and produces an outcome. The prediction of the outcome depends on the type of pattern the model got trained.

Ensemble Learning Techniques

7. Random Forest Algorithm

• Random Forest algorithm is a concept of ensembling learning, where several classifiers are stacked together to improve the performance.
• It is a plethora of decision trees.
• This technique involves classifying a new item based on its attributes where each tree is classified, and that class receives a “vote” from that tree. The forest picks the class that receives the most caste votes cast by overall trees in the forest.
• The random forest combines decision trees, where some possibly predict the correct output as they work independently to form their output and thus give their predictions.

8. Gradient Boosting Algorithm and AdaBoosting Algorithm

• Boosting is an Ensembling Learning Technique used to handle enormous data.
• It attempts to create a strong classifier out of several weak classifiers and thus make predictions that are highly accurate and robust.
• Boosting, initially, builds the first model from the training data, then creates a second model after correcting the errors in the first model. The process continues till the perfect prediction arrives.
• Adaboost was the successful algorithm designed for binary classification of data.
• Markedly, the theoretical gradient boosting machines are built on Adaboost and it is the best kickoff to learn and understand boosting.
• In deed, this ML method uses short decision trees, where after creating the first tree, in each training session, its performance is constantly weighed to assess the amount of attention needed for creating the next tree.
• In case, the training data is tough to predict, it is given more weight and vice versa.

Other Aspects

• As models are created sequentially, the weights of each are updated constantly as they affect the learning performance of the next tree in the sequence.
• Predictions for new data are made after all the trees are built, and the performance of each tree is weighted for the accuracy of the type of training data the model received.
• Removing all the oddities and abnormal formations of data really matters a lot as the algorithm pays much attention to correcting the mistakes.
• Lastly, these boosting algorithms customarily work well in data science competitions like Crowd Analytix, Kaggle, AV Hackathon, etc.
• Example:

In conclusion, these are the hand-picked machine learning algorithms for the beginners. So, newbies use them, in unison with others such as Python and R Codes, to achieve accurate outcomes and cherish your goals.

I hope this article is surely a great help to you. In case, if you feel you need to master it then you can join any reputed and certified institutes to hone your skills. Luckily, one among the renowned institutes is Henry Harvin.

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Last Takeaway

Chiefly, Machine learning is a subset of AI and a resounding career. A typical question asked is “which algorithm should I go with?” straightaway the answer would be, ‘it varies depending on many factors!’ Especially, the factors could be the size, quality, and nature of data; the computational time available; the urgency of the task; and what one wants to do with the data.

Therefore, without trying, even an experienced data scientist may not be able to tell which algorithm will suit better. Practically, there are numerous Machine Learning algorithms. Identically, these are the most popular ones and if you’re a noob to Machine Learning, these would be a great starting point to learn and start off successfully.

Good Luck! Go ahead! Get Glory!

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