Supervised learning is the types of machine learning in which machines are trained using well “labelled” training data, and on basis of that data, machines predict the output. The labelled data means some input data is already tagged with the correct output.
In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly. It applies the same concept as a student learns in the supervision of the teacher.
Supervised learning is a process of providing input data as well as correct output data to the machine learning model. The aim of a supervised learning algorithm is to find a mapping function to map the input variable(x) with the output variable(y).
In the real-world, supervised learning can be used for Risk Assessment, Image classification, Fraud Detection, spam filtering, etc.

How Supervised Learning Works?

In supervised learning, models are trained using labelled dataset, where the model learns about each type of data. Once the training process is completed, the model is tested on the basis of test data (a subset of the training set), and then it predicts the output.
The working of Supervised learning can be easily understood by the below example and diagram:

Suppose we have a dataset of different types of shapes which includes square, rectangle, triangle, and Polygon. Now the first step is that we need to train the model for each shape.
• If the given shape has four sides, and all the sides are equal, then it will be labelled as a Square.
• If the given shape has three sides, then it will be labelled as a triangle.
• If the given shape has six equal sides then it will be labelled as hexagon.
Now, after training, we test our model using the test set, and the task of the model is to identify the shape.
The machine is already trained on all types of shapes, and when it finds a new shape, it classifies the shape on the bases of a number of sides, and predicts the output.

Steps Involved in Supervised Learning:

• First Determine the type of training dataset
• Collect/Gather the labelled training data.
• Split the training dataset into training dataset, test dataset, and validation dataset.
• Determine the input features of the training dataset, which should have enough knowledge so that the model can accurately predict the output.
• Determine the suitable algorithm for the model, such as support vector machine, decision tree, etc.
• Execute the algorithm on the training dataset. Sometimes we need validation sets as the control parameters, which are the subset of training datasets.
• Evaluate the accuracy of the model by providing the test set. If the model predicts the correct output, which means our model is accurate.

Types of supervised Machine learning Algorithms:

Supervised learning can be further divided into two types of problems:

1. Regression
Regression algorithms are used if there is a relationship between the input variable and the output variable. It is used for the prediction of continuous variables, such as Weather forecasting, Market Trends, etc. Below are some popular Regression algorithms which come under supervised learning:
• Linear Regression
• Regression Trees
• Non-Linear Regression
• Bayesian Linear Regression
• Polynomial Regression
2. Classification
Classification algorithms are used when the output variable is categorical, which means there are two classes such as Yes-No, Male-Female, True-false, etc.
Spam Filtering,
• Random Forest
• Decision Trees
• Logistic Regression
• Support vector Machines
Note: We will discuss these algorithms in detail in later chapters.

Advantages of Supervised learning:

• With the help of supervised learning, the model can predict the output on the basis of prior experiences.
• In supervised learning, we can have an exact idea about the classes of objects.
• Supervised learning model helps us to solve various real-world problems such as fraud detection, spam filtering, etc.

Disadvantages of supervised learning:

• Supervised learning models are not suitable for handling the complex tasks.
• Supervised learning cannot predict the correct output if the test data is different from the training dataset.
• Training required lots of computation times.
• In supervised learning, we need enough knowledge about the classes of object.
So, this brings us to the end of blog. This Tecklearn ‘Supervised Machine Learning’ blog helps you with commonly asked questions if you are looking out for a job in Data Science. If you wish to learn Data Science and build a career in Data Science domain, then check out our interactive, Data Science using R Language Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/data-science-training-using-r-language/

Data Science using R Language Training

About the Course

Tecklearn’s Data Science using R Language Training develops knowledge and skills to visualize, transform, and model data in R language. It helps you to master the Data Science with R concepts such as data visualization, data manipulation, machine learning algorithms, charts, hypothesis testing, etc. through industry use cases, and real-time examples. Data Science course certification training lets you master data analysis, R statistical computing, connecting R with Hadoop framework, Machine Learning algorithms, time-series analysis, K-Means Clustering, Naïve Bayes, business analytics and more. This course will help you gain hands-on experience in deploying Recommender using R, Evaluation, Data Transformation etc.

Why Should you take Data Science Using R Training?

• The Average salary of a Data Scientist in R is $123k per annum – Glassdoor.com
• A recent market study shows that the Data Analytics Market is expected to grow at a CAGR of 30.08% from 2020 to 2023, which would equate to $77.6 billion.
• IBM, Amazon, Apple, Google, Facebook, Microsoft, Oracle & other MNCs worldwide are using data science for their Data analysis.

What you will Learn in this Course?

Introduction to Data Science
• Need for Data Science
• What is Data Science
• Life Cycle of Data Science
• Applications of Data Science
• Introduction to Big Data
• Introduction to Machine Learning
• Introduction to Deep Learning
• Introduction to R&R-Studio
• Project Based Data Science
Introduction to R
• Introduction to R
• Data Exploration
• Operators in R
• Inbuilt Functions in R
• Flow Control Statements & User Defined Functions
• Data Structures in R
Data Manipulation
• Need for Data Manipulation
• Introduction to dplyr package
• Select (), filter(), mutate(), sample_n(), sample_frac() & count() functions
• Getting summarized results with the summarise() function,
• Combining different functions with the pipe operator
• Implementing sql like operations with sqldf()
Visualization of Data
• Loading different types of datasets in R
• Arranging the data
• Plotting the graphs
Introduction to Statistics
• Types of Data
• Probability
• Correlation and Co-variance
• Hypothesis Testing
• Standardization and Normalization
Introduction to Machine Learning
• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Supervised Learning algorithm: Linear Regression and Logistic Regression
Logistic Regression
• Intro to Logistic Regression
• Simple Logistic Regression in R
• Multiple Logistic Regression in R
• Confusion Matrix
• ROC Curve
Classification Techniques
• What are classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?
• What is Naive Bayes?
• Support Vector Machine: Classification
Decision Tree
• Decision Tree in R
• Information Gain
• Gini Index
• Pruning
Recommender Engines
• What is Association Rules & its use cases?
• What is Recommendation Engine & it’s working?
• Types of Recommendations
• User-Based Recommendation
• Item-Based Recommendation
• Difference: User-Based and Item-Based Recommendation
• Recommendation use cases
Time Series Analysis
• What is Time Series data?
• Time Series variables
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• Implement ARIMA model for forecasting
• Exponential smoothing models
• Identifying different time series scenario based on which different Exponential Smoothing model can be applied

Got a question for us? Please mention it in the comments section and we will get back to you.

519