Home C Machine Learning Techniques Every Aspiring Data Scientist Should Know

Machine Learning Techniques Every Aspiring Data Scientist Should Know

Machine Learning Models

In the IT industry, tools, techniques, frameworks, and technology are disruptive, as new methodologies, techniques are in constant iteration with time. Radical change and complexities of the field make following up with new methods difficult even for field expertise and overwhelming for novice learners.

To keep stuff simple and make machine learning fun-oriented for those who are beginners to learn techniques, let’s look at six different methods with a short description, sample example, and visualization.

Machine learning is all about models that can process data and provide significant outcomes. One can think about models as algorithms or calculations on input data to provide values that have a business solution. Consider the following example that gives a gist of what machine learning algorithms have to offer. 

A realtor in the Real estate business can evaluate a property by speculation and intuition. However, realtor speculations are very close to market values. A Realtor uses his experience gained in identifying price variations from houses evaluated in the past and applies those patterns to make predictions for new property into consideration. Intuitions are models with algorithms that process data. Consider this flow chart for evaluating the property.

Fig: A decision tree that also considers the total size of each house. Source: Kaggle

Machine learning models work in a very similar way. There are simple models like decision making algorithms to fancier or complex models. The techniques identified offer an overview and help you build machine learning skills.

  1. Regression
  2. Classification
  3. Clustering
  4. Dimensionality Reduction
  5. Ensemble Methods
  6. Neural Nets and Deep Learning

There are two general categories of classification of these techniques. One is Supervised Learning models and Unsupervised Learning Models. Let’s understand these two and proceed with knowing machine learning techniques.

Supervised Learning Techniques: 

Supervised Learning Process

As we now understand machine learning, now we shall think in terms of the input data feed to the models. In Supervised learning, input data is known and can be labeled. The machine learns from this labeled input data and resulting output data to predict future outcomes or events.

Supervised Learning example

Here in the example, the machine has learned things from labeled data, it processes the input data to classify dogs and cats. Thus the machine learns the input from training data and then applies the knowledge to test data. 

Unsupervised Learning Techniques: 

In this technique, the system learns the input data with no labels or classification and processes the data without any parameters. Machine learning is improvised every iteration by algorithms classifying unsorted information according to attributes, patterns, and differences without any labeling of data.

Unsupervised learning model

Here in the example, the machine has to process information with no labels. It processes the input data to classify as groups. Thus the machine learns the input from untrained data and then applies the knowledge to test data. 

Keynote: Supervised learning and unsupervised learning methods are vital concepts in the field of modeling data in machine learning. A proper understanding of the fundamentals is a must before you proceed into the pool of various machine learning algorithms.

Here you will find a detailed overview of Supervised, Unsupervised, and Reinforcement Techniques


The regression method falls in the category of Supervised Learning technique. Regression algorithms follow the principles of physics to process the data to get future predictions. For example, the value of property assets in real estate may depreciate or increase with variables like no of rooms, locality etc.

linear regression model

In regression models, multiple variables are input for modeling a data set. Consider the simple linear equation to model a dataset:

y = m*x + b

Here data pairs x and y are used to train the machine to determine the position and slope of a line that best approximates the checks in the data. You can sketch a graph based on these variables and plot prediction continuous results, based on the predictor variable.

There are several forms of regression techniques. The simplest one being linear regression to a more complex regression model for polynomial data calculation and representations. 

Several regression models include linear regression, polynomial regression, decision trees, and random forest regressions, neural nets, among others. You can start your basics with a linear regression model.


Classification models as the terminology refer to modeling data based on multiple variables. Classification is the method of predicting the state of given data points and belongs to the category of supervised learning where the system runs with the input data. 

Source BiSmart

The classification model is not based on any two values or variables only but can be multiple due to probability calculation. For example, a model may help you discover if a customer in a market may visit a shop and purchase or may not purchase, or may even not visit in the first place. The classification should not be confused with clustering. Clustering models data to clusters. 

Another example of image classification of traffic roads, an image may consist of 1) Car 2) Bike 3) Truck 4) Lane 5) None of these 6) some of these, etc. 

The above pattern considered is based on logistic classification analysis, which is the simplest of all. As you further progress, you can sharpen your skills in non-linear classifications.

Classification techniques in machine learning

You can start with the following top 5 Classification Algorithms in Machine Learning

  1. Logistic Regression.
  2. Naive Bayes Classifier.
  3. K-Nearest Neighbors.
  4. Decision Tree. Random Forest.
  5. Support Vector Machines.


Clustering methods fall into unsupervised learning models. Clustering methods predict input data and provide outcomes into data values as clusters or groups, and are used for visualization to inspect the quality of the solution. Here the output data is not used for training the system, but it defines the output. The algorithm uses visualization to design a solution.

Clustering Model

For example: Consider the scenario we used in understanding unsupervised learning methods. There exist two groups 1) Group 1: Cats 2) Group 2: Dogs. If there is another animal, a third one, say a horse in the input group, then there would possibly be another group defined in the output.

The most common clustering technique is K-Means, where K denotes the number of clusters created. (Note that there are many ways for determining the value of K, such as the elbow method.)

Generally, what K-Means does with the data points:

  1. Randomly takes K centers within the data.
  2. Assigns each data point to the most familiar of the randomly created centers.
  3. Re-evaluate the center of each cluster.

If centers do not update (or change very little), the process is stopped. Otherwise, we turn to step 2. (This process will lead to an infinite loop if the centers continue to change, a breakpoint applied by setting the maximum number of iterations.) 

As you learn to cluster, you find algorithms such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Mean Shift Clustering, Agglomerative Hierarchical Clustering, Expectation-Maximization Clustering using Gaussian Mixture Models, among others.

Dimensionality Reduction

As we study input data, we find datasets with hundreds and thousands of columns of feature data. Sometimes or most of the time, it is required to reduce this dataset with only the required features. Dimensionality Reduction algorithms clean invaluable, or redundant, or missing data.

Dimensionality Reduction

Consider use cases i) removing unwanted pixels from an image, ii) reducing unwanted noise from audio, iii) sort out spam emails from the inbox.

The algorithm helps to sort irrelevant variables and help for feature selection and extraction. Reduces the repetition of data and provides accurate results.

Ensemble Methods

It is a technique to pile data by applying the prediction feature variable from different ML models. Consequently, it consolidates several predictive models to form a highly detailed and optimized predictive output. The method helps to make decisions while considering various factors.

Ensemble Methods

Ensemble Model

As you learn, you will find Random Forest algorithms use the Ensemble technique to combine multiple decision trees trained with different data sets to provide an accurate result when compared to the output of any single decision tree. 

Consider an example to assess cricketers and team performance. Metric to measure the performance of batsmen, seamer, and all-rounder player changes. The team performance measurement values change in consideration of a team as a whole.

Neural Networks

Neural Network algorithms process complex and divisional patterns of datasets. It captures non-linear patterns in data and processes in multiple layers to provide single and precise output. A neural network consists of multiple hidden layers that consume the input dataset to predict the outcome. High graphical processing units are required to exercise non-linear data evolving intelligent systems for accurate results. 

Neural Network in Machine Learning

Neural Network, Source:analyticsindiamag

Deep learning is an extended model with many layers added into hidden layers that perform multiple parameters that require complex knowledge for accurate predictions.

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