# Types of Classification in Machine Learning

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• by Amit Diwan
• 05th Sep, 2020
• Last updated on 15th Mar, 2021

• In this post, we understand the concept of classification, regression, classification predictive modelling, and the different types of classification and regression
• We understand why and how classification is important.
• We also see a few classification algorithms and their implementations in Python.
• We understand logistic regression, decision trees, random forests, support vector machines, k nearest neighbour and neural networks.
• We understand their inner workings and their prominence.

Classification refers to the process of classifying the given data set into different classes or groups. The classification algorithm is placed under predictive modelling problem, wherein every class of the dataset is given a label, to indicate that it is different from other classes. Some examples include email classification as spam or not, recognition of a handwritten character as a specific character only, and not another character and so on.

Classification algorithms need data to be trained with many inputs and their respective output, with the help of which the model learns. It is important to understand that the training data must encompass all kinds of data (options) which could be encountered in the test data set or real world.

## Classification

The 4 different prominent types of classification include the following:

• Binary classification
• Multi-class classification
• Multi-label classification
• Imbalanced classification

### Binary classification

As the name suggests, it deals with the tasks in classification that only have two class labels. Some examples include: email classification as spam or not, whether the price of a stock will go up or go down (ignoring the fact that it could also remain as is), and so on. The value obtained after classifying the data would be either 0 or 1, yes or no, normal or abnormal.

The Bernoulli probability distribution is used as prediction to classify the data as 0 or 1. Bernoulli distribution is a discrete (discontinuous) distribution that gives a binary outcome -- a 0 or a 1.

Algorithms that are used to perform binary classification include the following:

• Logistic regression
• Decision trees
• Support vector machine
• Naïve Bayes
• k’nn (k nearest neighbors)

Code to demonstrate a binary classification task:

from numpy import where
from collections import Counter
from sklearn.datasets import make_blobs
from matplotlib import pyplot
X, y = make_blobs(n_samples=560, centers=2, random_state=1)
print("Data has been generated ")
print("The number of rows and columns are ")
print(X.shape, y.shape)
my_counter = Counter(y)
print(my_counter)
for i in range(10):
print(X[i], y[i])
for my_label, _ in my_counter.items():
row_ix = where(y == my_label)[0]
pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(my_label))
pyplot.legend()
pyplot.show()

Output:

Data has been generated
The number of rows and columns are
(560, 2) (560,)
Counter({1: 280, 0: 280})
[-9.64384208 -4.14030356] 1
[-0.8821407  4.2877187] 0
… 

Code explanation

• The required packages are imported using the ‘import’ function.
• The dataset is generated using the ‘make_blobs’ function and by specifying the number of rows and columns that need to be generated.
• In addition, the number of classes into which the data points need to be labelled into is also defined. Here, it is 2.
• The number of rows and columns are displayed along with the summarization of class labelling.
• A ‘for’ loop is used to print the first few classified values.
• The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen.

### Multi-class classification

It is a type of classification wherein the input data set is classified/labelled into more than 2 classes. Some examples of multi-class classification include:

• Animal species classification
• Facial recognition/classification
• Text translation (special type of multi-class classification task)

This is different from binary classification in that it doesn’t have just two classes like 0 or 1, but more, and they need not be 0 or 1. They could be names or other continuous or discontinuous numbers. The data points are classified into one among many different classes given.

The number of class labels may be too high, when trying to classify a given photo into that of a specific person. Text translation also deals with a similar issue, wherein the word placement may vary widely and there maybe thousands of combinations of the same number of words. Multinoulli probability distribution is a discrete/discontinuous probability distribution, where the output could be any value within a given range. Algorithms that are used for binary classification can also be used for multi-class classification.

Code to demonstrate the multi-class classification:

from numpy import where
from collections import Counter
from sklearn.datasets import make_blobs
from matplotlib import pyplot

X, y = make_blobs(n_samples=670, centers=5, random_state=1)
print("The dataset has been generated")
print("The rows and columns are ")
print(X.shape, y.shape)
my_counter = Counter(y)
print(my_counter)
for i in range(10):
print(X[i], y[i])
for my_label, _ in my_counter.items():
row_ix = where(y == my_label)[0]
pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(my_label))
pyplot.legend()
pyplot.show() 

Output:

The dataset has been generated
The rows and columns are
(670, 2) (670,)
Counter({3: 134, 0: 134, 2: 134, 4: 134, 1: 134})
[-6.45785776 -3.30981436] 3
[-6.44623696 -2.90184841] 3
[-5.60217602 -0.65990849] 3 

Code explanation:

• The required packages are imported using the ‘import’ function.
• The dataset is generated using the ‘make_blobs’ function and by specifying the number of rows and columns that need to be generated.
• In addition, the number of classes into which the data points need to be labelled into is also defined. Here, it is 5.
• The number of rows and columns are displayed along with the summarization of class labelling.
• A ‘for’ loop is used to print the first few classified values.
• The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen.

### Multi-label classification

Multi-label classification refers to those classification problems that deal with more than one class being assigned to a single data point, i.e. every data point would belong or be labelled into more than one class/label. A simple example would be a photo that contains multiple people, not just one. This means one photo might be classified or labelled as more than one (in fact thousands) of persons. This is different from binary and multi-class classification, since the number of labels into which one data point is classified remains same, i.e one.

Some multi-label classification algorithms include:

• Multi-label random forests

Code to demonstrate multi-label classification:

from sklearn.datasets import make_multilabel_classification
X, y = make_multilabel_classification(n_samples=800, n_features=2, n_classes=5, n_labels=3, random_state=1)
print("The number of rows and columns are ")
print(X.shape, y.shape)
for i in range(8):
print(X[i], y[i]) 

Output:

The number of rows and columns are
(800, 2) (800, 5)
[22. 24.] [1 0 0 1 1]
[12. 35.] [0 1 0 1 0]
[27. 30.] [1 1 0 0 1]
..  

Code explanation

• The required packages are imported using the ‘import’ function.
• The dataset is generated using the ‘make_multilabel_classification’ function present in the scikit-learn package is used.
• It is done by specifying the number of rows and columns that need to be generated.
• The number of rows and columns are displayed along with the summarization of class labelling.
• A ‘for’ loop is used to print the first few classified values.
• The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen.

### Imbalanced classification

This is a type of classification wherein the number of data points of the dataset in every class is not distributed equally. This means imbalanced classification is basically a binary classification problem, which doesn’t have a uniform distribution of points, one class could contains an extremely large amount of data points, and the other class might contains a very small number of data points.

Examples of imbalanced classification problem include:

• Fraud detection in credit cards
• Anomaly detection in the given dataset

There are specialized algorithms that are used to classify this data into the large data point group or small data point group. Some algorithms have been listed below:

• Cost sensitive decision trees
• Cost sensitive logistic regression
• Cost sensitive support vector machines

Code to demonstrate imbalanced binary classification

#An example of imbalanced binary classification task
from numpy import where
from collections import Counter
from sklearn.datasets import make_classification
from matplotlib import pyplot
#The dataset is defined
X, y = make_classification(n_samples=800, n_features=2, n_informative=2, n_redundant=0, n_classes=2, n_clusters_per_class=1, weights=[0.99,0.01], random_state=1)
#The shape of the dataset is summarized
print("The number of rows and columns ")
print(X.shape, y.shape)
#The labelled data is summarized
my_counter = Counter(y)
print(my_counter)
#A few data points are summarized
for i in range(10):
print(X[i], y[i])
#The dataset is plotted on a graph and displayed
for my_label, _ in my_counter.items():
row_ix = where(y == my_label)[0]
pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(my_label))
pyplot.legend()
pyplot.show() 

Output:

The number of rows and columns
(800, 2) (800,)
Counter({0: 785, 1: 15})
[0.28622882 0.38305399] 0
[1.17971415 0.48003249] 0
[1.32658794 0.71712275] 0 

Code explanation

• The required packages are imported using the ‘import’ function.
• The dataset is generated using the ‘make_classification’ function present in the scikit-learn package is used.
• It is done by specifying the number of rows and columns that need to be generated.
• The number of rows and columns are displayed along with the summarization of class labelling.
• A ‘for’ loop is used to print the first few classified values.
• The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen.

## Logistic regression

In this classification technique, instead of finding continuous values like that of linear regression, we are concerned with finding discrete values. It is simply a classification technique that classifies the given data points into one of the labelled classes. Usually, we are looking at a Boolean output, wherein the result is either 0 or 1, yes or no and so on. Some examples include:

• Classifying an email as spam or not
• Finding whether it would rain today or not

## Naïve Bayes classification

Bayes theorem is way of calculating the probability of a hypothesis (situation, which might not have occurred in reality) based on our previous experiences and the knowledge we have gained by it.

Bayes theorem is stated as follows:

P(hypo | data) = (P(data | hypo) * P(hypo)) / P(data)

In the above equation,

P(hypo | data) is the probability of a hypothesis ‘hypo’ when data ‘data’ is given, which is also known as posterior probability.

P(data | hypo) is the probability of data ‘data’ when the specific hypothesis ‘hypo’ is known to be true.

P(hypo) is the probability of a hypothesis ‘hypo’ being true (irrespective of the data in hand), which is also known as prior probability of ‘hypo’.

P(data) is the probability of the data (irrespective of the hypothesis).

The idea here is to get the value of the posterior probability, given other data. The posterior probability for a variety of different hypotheses is found out, and the probability that has the highest value is selected. This is known as the maximum probable hypothesis, and is also known as maximum a posteriori (MAP) hypothesis.

MAP(hypo) = max(P(hypo | data))

If the value of P(hypo | data) is replaced with the value we saw before, the equation would become:

MAP(hypo) = max((P(data | hypo) * P(hypo)) / P(data))

P(data) is considered as a normalizing term that helps in determining the probability. This value can be ignored when required, since it is a constant value.

Naïve Bayes classifier is an algorithm that can be used with binary or multi-class classification problems. Once a Naïve Bayes classifier has learnt from the data, it stores a list of probabilities. Probabilities such as ‘class probability’ and ‘condition probability’ is stored. Training such a model is quick since the probability of every class and its associated value needs to be determined, and this doesn’t involve any optimization processes or coefficient changing.

## K-nearest neighbour (KNN)

The simplest way to understand k-nearest neighbour, is that the training data for the algorithm is all the data in its entirety. KNN doesn’t have a different model, other than the one that stores the entire dataset, which means there is no machine learning that is actually happening. This means KNN makes predictions and extracts patterns directly from the training dataset itself.

When a new data point is encountered, the corresponding value for that can be found using KNN by navigating through the entire training dataset, by looking at the ‘k’ number of very similar neighbours. Once the ‘k’ neighbours have been identified, they are summarized and the output for every instance is found. In case of regression, the mean of this output is the result, and in case of classification, the mode of this output is the result.

### How to determine the ‘k’ neighbours?

To find ‘k’ number of instances from the training dataset that are very similar to the new data point, we use a distance factor, and the most popular metric is the Euclidean distance.

Euclidean distance can be determined by finding the square root of the sum of the square of difference between the new point and an existing point in the data set, and this sum is from values in the range (a,b).

Euclidean Distance:

(a,b) = square root( sum( a – b) ^ 2))

Other distances that can be used include:

• Hamming distance
• Manhattan distance
• Minkowski Distance

When the number of data points in the training set increases, the complexity of KNN also increases.

## Support vector machines (SVM)

The hyperplane present in linear SVM is learnt by performing simple transformations using linear algebra. The sum of the product of every pair of input data points is multiplied, and this is known as the inner product. The basic idea behind SVM is that the inner product of two vectors can be expressed as a sum of product of the first value of every vector.

To find inner product of two input vectors:

[a,b] and [c,d], we do [a*c + b*d]

In order to predict new value, the dot product can be used, and the support vector can be calculated using the below equation:

f(x) = coeff-1 + sum(coeff-2 * (a,b))

Here, ‘a’ and ‘b’ are input vectors and coeff-1 and coeff-2 are coefficients that are determined with the help of the training dataset and the learning algorithm. Stochastic gradient descent or sequential minimal optimization technique can be used. All these optimization techniques break down the main problem into sub-problems and every sub problem is solved by calculating the required value.

## Decision trees

It is a part of predictive modelling in machine learning that is considered as one of the most powerful algorithms. It is also known as CART, i.e. classification and regression trees since this can be used in the process of classification as well as regression tasks. Decision tree can be simply visualized as a binary tree that has a root and many branches from it and leaves. It is the same as the tree data structure. The root is a single input value, and the branches that lead to leaves are used in predicting the values for the given input.

The tree structure can be stored in the form of a graph structure or a set of rules. Once the data in the form of tree is available, it is simple to make predictions on it with the help of the leaf nodes. The specific branch and its leaf node is examined to reach the node.

Data is filtered from the root of the tree and goes and sits in the branch and the leaf that is relevant to it.

No data preparation or pre-processing is required while working with CART or decision trees.

It is a method to build predictive models in machine learning. The idea behind boosting is to understand whether a weak learning algorithm can be made to learn better. This involves three attributes:

1. A weak learning algorithm that makes prediction: Decision tree is considered to be a weak learner when it comes to gradient boosting. The best splits are chosen in decision trees, thereby minimizing the loss, hence they need to be improved so that they work well even when the split is random.
2. A loss function that needs to be optimized: This value depends on the situation in hand. Many different loss functions can be used, such as squared error, measure squared error, logarithmic loss function and so on. A new boosting algorithm won’t have to be figured out for every loss function.
3. An additive model that adds weak learner to minimize the loss function: The trees to the gradient boosting technique are added one at a time, so that the existing model trees don’t have changes. This way, the loss is minimized when new trees are added. Usually, gradient descent optimization technique is used to minimize the loss.

## Random forest

Random forest is an ensemble machine leaning algorithm that uses bootstrap aggregation or bagging. It is a statistical method that helps in estimating the quantity from a given data sample. It is done to reduce the variance for those algorithms that seem to have a high variance. Examples of algorithms that have high variance include CART, and decision trees. Decision trees are extremely sensitive to the data on which they are trained. If the training data changes, the resultant tree would also be completely different. A small change in the input makes a huge difference to the overall training and output.

An ensemble method is the one that combines the predictions that have come from many different machine learning algorithms, thereby making sure that the predictions are more accurate in comparison to dealing with an algorithm that gives a single prediction. It is like combining the best algorithms to give the best of best values.

Random forest makes sure that the every sub-tree that learns and trains on the data and makes the predictions is less correlated to the other sub-trees that do the same. The learning algorithm is limited to be able to look at a random sample of the data points, so that it doesn’t have the opportunity to look through all the variables, and select an optimal point to split upon (which is actually the case with CART). It is seen that for classification trees, a good value for the number of randomly selected columns from the dataset is square root (p) where p refers to the number of input variables. On the other hand, for regression trees, a good value for the number of randomly selected columns from the dataset is p/3.

## Neural networks

It is a part of deep learning that deals with artificial neural networks. In general, the word ‘neural’ or ‘neuro’ deals with the decision making branch of the human brain. The idea behind artificial neural network, also abbreviated as ANN, is that it takes decision similar to how the neurons in the brain function while performing a function or taking a decision.

It is called deep learning since these networks have various layers, and every layer has a large number of nodes. Every layer processes some part of the data and passes on the computed data to the next layer. The input data to one layer is the output data of the previous layer. Usually, the input layer’s nodes are large in number, and the output layer has just one node indicating that the data was processed, and the output has been obtained.

Conclusion

In this post, we understood how classification works, the different types of classification and regression, their working, implementations by generating simple dataset and working through it using Python and other relevant machine learning related packages.

### Amit Diwan

Author

Amit Diwan is an E-Learning Entrepreneur, who has taught more than a million professionals with Text & Video Courses on the following technologies: Data Science, AI, ML, C#, Java, Python, Android, WordPress, Drupal, Magento, Bootstrap 4, etc.

## Types of Probability Distributions Every Data Science Expert Should know

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Types of Probability Distributions Every Data Scie...

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It is also behind the SAS Institute for Data Science. Hence, SAS is the organisation you would want to go to if you're aiming for a long-term career in data science. Conclusion To conclude, big data and data Analytics are a field of endless opportunities. By investing in the right credential, one can pave the way to a viable and lucrative career path. Beware though, there are lots of companies that provide certifications, but only recognised and reputed credentials will give you the opportunities you are seeking. Hiring companies look for these certifications as a mark of authenticity of your hands-on experience and the amount of work you can handle effectively. Therefore, the credential you choose for yourself plays a vital role in the career you can have in the field of Data analytics. Happy learning! 5651 Top Data Analytics Certifications What is data analytics?In the world of IT, every s... Read More ## Why Should You Start a Career in Machine Learning? If you are even remotely interested in technology you would have heard of machine learning. In fact machine learning is now a buzzword and there are dozens of articles and research papers dedicated to it. Machine learning is a technique which makes the machine learn from past experiences. Complex domain problems can be resolved quickly and efficiently using Machine Learning techniques. We are living in an age where huge amounts of data are produced every second. This explosion of data has led to creation of machine learning models which can be used to analyse data and to benefit businesses. This article tries to answer a few important concepts related to Machine Learning and informs you about the career path in this prestigious and important domain.What is Machine Learning?So, here’s your introduction to Machine Learning. This term was coined in the year 1997. “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at the tasks improves with the experiences.”, as defined in the book on ML written by Mitchell in 1997. The difference between a traditional programming and programming using Machine Learning is depicted here, the first Approach (a) is a traditional approach, and second approach (b) is a Machine Learning based approach.Machine Learning encompasses the techniques in AI which allow the system to learn automatically looking at the data available. While learning, the system tries to improve the experience without making any explicit efforts in programming. Any machine learning application follows the following steps broadlySelecting the training datasetAs the definition indicates, machine learning algorithms require past experience, that is data, for learning. So, selection of appropriate data is the key for any machine learning application.Preparing the dataset by preprocessing the dataOnce the decision about the data is made, it needs to be prepared for use. Machine learning algorithms are very susceptible to the small changes in data. To get the right insights, data must be preprocessed which includes data cleaning and data transformation. Exploring the basic statistics and properties of dataTo understand what the data wishes to convey, the data engineer or Machine Learning engineer needs to understand the properties of data in detail. These details are understood by studying the statistical properties of data. Visualization is an important process to understand the data in detail.Selecting the appropriate algorithm to apply on the datasetOnce the data is ready and understood in detail, then appropriate Machine Learning algorithms or models are selected. The choice of algorithm depends on characteristics of data as well as type of task to be performed on the data. The choice also depends on what kind of output is required from the data.Checking the performance and fine-tuning the parameters of the algorithmThe model or algorithm chosen is fine-tuned to get improved performance. If multiple models are applied, then they are weighed against the performance. The final algorithm is again fine-tuned to get appropriate output and performance.Why Pursue a Career in Machine Learning in 2021?A recent survey has estimated that the jobs in AI and ML have grown by more than 300%. Even before the pandemic struck, Machine Learning skills were in high demand and the demand is expected to increase two-fold in the near future.A career in machine learning gives you the opportunity to make significant contributions in AI, the future of technology. All the big and small businesses are adopting Machine Learning models to improve their bottom-line margins and return on investment. The use of Machine Learning has gone beyond just technology and it is now used in diverse industries including healthcare, automobile, manufacturing, government and more. This has greatly enhanced the value of Machine Learning experts who can earn an average salary of$112,000.  Huge numbers of jobs are expected to be created in the coming years.  Here are a few reasons why one should pursue a career in Machine Learning:The global machine learning market is expected to touch \$20.83B in 2024, according to Forbes.  We are living in a digital age and this explosion of data has made the use of machine learning models a necessity. Machine Learning is the only way to extract meaning out of data and businesses need Machine Learning engineers to analyze huge data and gain insights from them to improve their businesses.If you like numbers, if you like research, if you like to read and test and if you have a passion to analyse, then machine learning is the career for you. Learning the right tools and programming languages will help you use machine learning to provide appropriate solutions to complex problems, overcome challenges and grow the business.Machine Learning is a great career option for those interested in computer science and mathematics. They can come up with new Machine Learning algorithms and techniques to cater to the needs of various business domains.As explained above, a career in machine learning is both rewarding and lucrative. There are huge number of opportunities available if you have the right expertise and knowledge. On an average, Machine Learning engineers get higher salaries, than other software developers.Years of experience in the Machine Learning domain, helps you break into data scientist roles, which is not just among the hottest careers of our generation but also a highly respected and lucrative career. Right skills in the right business domain helps you progress and make a mark for yourself in your organization. For example, if you have expertise in pharmaceutical industries and experience working in Machine learning, then you may land job roles as a data scientist consultant in big pharmaceutical companies.Statistics on Machine learning growth and the industries that use MLAccording to a research paper in AI Multiple (https://research.aimultiple.com/ml-stats/), the Machine Learning market will grow to 9 Billion USD by the end of 2022. There are various areas where Machine Learning models and solutions are getting deployed, and businesses see an overall increase of 44% investments in this area. North America is one of the leading regions in the adoption of Machine Learning followed by Asia.The Global Machine Learning market will grow by 42% which is evident from the following graph. Image sourceThere is a huge demand for Machine Learning modelling because of the large use of Cloud Based Applications and Services. The pandemic has changed the face of businesses, making them heavily dependent on Cloud and AI based services. Google, IBM, and Amazon are just some of the companies that have invested heavily in AI and Machine Learning based application development, to provide robust solutions for problems faced by small to large scale businesses. Machine Learning and Cloud based solutions are scalable and secure for all types of business.ML analyses and interprets data patterns, computing and developing algorithms for various business purposes.Advantages of Machine Learning courseNow that we have established the advantages of perusing a career in Machine Learning, let’s understand from where to start our machine learning journey. The best option would be to start with a Machine Learning course. There are various platforms which offer popular Machine Learning courses. One can always start with an online course which is both effective and safe in these COVID times.These courses start with an introduction to Machine Learning and then slowly help you to build your skills in the domain. Many courses even start with the basics of programming languages such as Python, which are important for building Machine Learning models. Courses from reputed institutions will hand hold you through the basics. Once the basics are clear, you may switch to an offline course and get the required certification.Online certifications have the same value as offline classes. They are a great way to clear your doubts and get personalized help to grow your knowledge. These courses can be completed along with your normal job or education, as most are self-paced and can be taken at a time of your convenience. There are plenty of online blogs and articles to aid you in completion of your certification.Machine Learning courses include many real time case studies which help you in understanding the basics and application aspects. Learning and applying are both important and are covered in good Machine Learning Courses. So, do your research and pick an online tutorial that is from a reputable institute.What Does the Career Path in Machine Learning Look Like?One can start their career in Machine Learning domain as a developer or application programmer. But the acquisition of the right skills and experience can lead you to various career paths. Following are some of the career options in Machine Learning (not an exhaustive list):Data ScientistA data scientist is a person with rich experience in a particular business field. A person who has a knowledge of domain, as well as machine learning modelling, is a data scientist. Data Scientists’ job is to study the data carefully and suggest accurate models to improve the business.AI and Machine Learning EngineerAn AI engineer is responsible for choosing the proper Machine Learning Algorithm based on natural language processing and neural network. They are responsible for applying it in AI applications like personalized advertising.  A Machine Learning Engineer is responsible for creating the appropriate models for improvement of the businessData EngineerA Data Engineer, as the name suggests, is responsible to collect data and make it ready for the application of Machine Learning models. Identification of the right data and making it ready for extraction of further insights is the main work of a data engineer.Business AnalystA person who studies the business and analyzes the data to get insights from it is a Business Analyst. He or she is responsible for extracting the insights from the data at hand.Business Intelligence (BI) DeveloperA BI developer uses Machine Learning and Data Analytics techniques to work on a large amount of data. Proper representation of data to suit business decisions, using the latest tools for creation of intuitive dashboards is the role of a BI developer.  Human Machine Interface learning engineerCreating tools using machine learning techniques to ease the human machine interaction or automate decisions, is the role of a Human Machine Interface learning engineer. This person helps in generating choices for users to ease their work.Natural Language Processing (NLP) engineer or developerAs the name suggests, this person develops various techniques to process Natural Language constructs. Building applications or systems using machine learning techniques to build Natural Language based applications is their main task. They create multilingual Chatbots for use in websites and other applications.Why are Machine Learning Roles so popular?As mentioned above, the market growth of AI and ML has increased tremendously over the past years. The Machine Learning Techniques are applied in every domain including marketing, sales, product recommendations, brand retention, creating advertising, understanding the sentiments of customer, security, banking and more. Machine learning algorithms are also used in emails to ease the users work. This says a lot, and proves that a career in Machine Learning is in high demand as all businesses are incorporating various machine learning techniques and are improving their business.One can harness this popularity by skilling up with Machine Learning skills. Machine Learning models are now being used by every company, irrespective of their size--small or big, to get insights on their data and use these insights to improve the business. As every company wishes to grow faster, they are deploying more machine learning engineers to get their work done on time. Also, the migration of businesses to Cloud services for better security and scalability, has increased their requirement for more Machine Learning algorithms and models to cater to their needs.Introducing the Machine learning techniques and solutions has brought huge returns for businesses.  Machine Learning solution providers like Google, IBM, Microsoft etc. are investing in human resources for development of Machine Learning models and algorithms. The tools developed by them are popularly used by businesses to get early returns. It has been observed that there is significant increase in patents in Machine Learning domains since the past few years, indicating the quantum of work happening in this domain.Machine Learning SkillsLet’s visit a few important skills one must acquire to work in the domain of Machine Learning.Programming languagesKnowledge of programming is very important for a career in Machine Learning. Languages like Python and R are popularly used to develop applications using Machine Learning models and algorithms. Python, being the simplest and most flexible language, is very popular for AI and Machine Learning applications. These languages provide rich support of libraries for implementation of Machine Learning Algorithms. A person who is good in programming can work very efficiently in this domain.Mathematics and StatisticsThe base for Machine Learning is mathematics and statistics. Statistics applied to data help in understanding it in micro detail. Many machine learning models are based on the probability theory and require knowledge of linear algebra, transformations etc. A good understanding of statistics and probability increases the early adoption to Machine Learning domain.Analytical toolsA plethora of analytical tools are available where machine learning models are already implemented and made available for use. Also, these tools are very good for visualization purposes. Tools like IBM Cognos, PowerBI, Tableue etc are important to pursue a career as a  Machine Learning engineer.Machine Learning Algorithms and librariesTo become a master in this domain, one must master the libraries which are provided with various programming languages. The basic understanding of how machine learning algorithms work and are implemented is crucial.Data Modelling for Machine Learning based systemsData lies at the core of any Machine Learning application. So, modelling the data to suit the application of Machine Learning algorithms is an important task. Data modelling experts are the heart of development teams that develop machine learning based systems. SQL based solutions like Oracle, SQL Server, and NoSQL solutions are important for modelling data required for Machine Learning applications. MongoDB, DynamoDB, Riak are some important NOSQL based solutions available to process unstructured data for Machine Learning applications.Other than these skills, there are two other skills that may prove to be beneficial for those planning on a career in the Machine Learning domain:Natural Language processing techniquesFor E-commerce sites, customer feedback is very important and crucial in determining the roadmap of future products. Many customers give reviews for the products that they have used or give suggestions for improvement. These feedbacks and opinions are analyzed to gain more insights about the customers buying habits as well as about the products. This is part of natural language processing using Machine Learning. The likes of Google, Facebook, Twitter are developing machine learning algorithms for Natural Language Processing and are constantly working on improving their solutions. Knowledge of basics of Natural Language Processing techniques and libraries is must in the domain of Machine Learning.Image ProcessingKnowledge of Image and Video processing is very crucial when a solution is required to be developed in the area of security, weather forecasting, crop prediction etc. Machine Learning based solutions are very effective in these domains. Tools like Matlab, Octave, OpenCV are some important tools available to develop Machine Learning based solutions which require image or video processing.ConclusionMachine Learning is a technique to automate the tasks based on past experiences. This is among the most lucrative career choices right now and will continue to remain so in the future. Job opportunities are increasing day by day in this domain. Acquiring the right skills by opting for a proper Machine Learning course is important to grow in this domain. You can have an impressive career trajectory as a machine learning expert, provided you have the right skills and expertise.
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Why Should You Start a Career in Machine Learning?

If you are even remotely interested in technology ... Read More