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A data science course is a structured educational program designed to teach individuals the foundational concepts, tools, and techniques of data science. These data science courses typically cover a wide range of topics, including statistics, programming, machine learning, data visualization, and data analysis. They are suitable for beginners with little to no prior experience in data science, as well as professionals looking to expand their skills or transition into a data-related role.
Key components of a data science course may include:
- Foundational Concepts
- Programming Languages
- Statistical Methods
- Machine Learning
- Data Visualization
- Practical Projects
- Capstone Project
*Foundational Concepts: Introduction to basic concepts in data science, including data types, data manipulation, data cleaning, and exploratory data analysis.
* Programming Languages:Instruction in programming languages commonly used in data science, such as Python or R. Students learn how to write code to analyze and manipulate data, create visualizations, and build machine learning models.
*Statistical Methods:Coverage of statistical techniques and methods used in data analysis, hypothesis testing, regression analysis, and probability theory.
*Machine Learning: Introduction to machine learning algorithms, including supervised learning, unsupervised learning, and deep learning. Students learn how to apply machine learning techniques to solve real-world problems and make predictions from data.
*Data Visualization: Instruction in data visualization techniques and tools for effectively communicating insights from data. Students learn how to create plots, charts, and interactive visualizations to explore and present data.
*Practical Projects: Hands-on experience working on data science projects and case studies, where students apply their knowledge and skills to solve real-world problems and analyze real datasets.
*Capstone Project: A culminating project where students demonstrate their mastery of data science concepts and techniques by working on a comprehensive project from start to finish.
What is Data Science Job?
A data science job involves using various techniques, algorithms, and tools to extract insights and knowledge from structured and unstructured data. Here are some of the key data science job roles:
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Data Engineer
- Business Intelligence (BI) Analyst
- Data Architect
*Data Scientist
- Responsibilities: Analyzing large datasets, developing machine learning models, interpreting results, and providing insights to inform business decisions.
- Skills: Proficiency in programming languages like Python or R, expertise in statistics and machine learning algorithms, data visualization skills, and domain knowledge in the relevant industry.
*Data Analyst
- Responsibilities : Collecting, cleaning, and analyzing data to identify trends, patterns, and insights. Often involves creating reports and dashboards to communicate findings to stakeholders.
- Skills : Strong proficiency in SQL for data querying, experience with data visualization tools like Tableau or Power BI, basic statistical knowledge, and familiarity with Excel or Google Sheets.
*Machine Learning Engineer
- Responsibilities: Building and deploying machine learning models at scale, optimizing model performance, and integrating them into production systems.
- Skills : Proficiency in programming languages like Python or Java, experience with machine learning frameworks like TensorFlow or PyTorch, knowledge of cloud platforms like AWS or Azure, and software engineering skills for developing scalable solutions.
*Data Engineer
- Responsibilities : Designing and building data pipelines to collect, transform, and store large volumes of data. Ensuring data quality, reliability, and scalability.
- Skills : Expertise in database systems like SQL and NoSQL, proficiency in programming languages like Python or Java, experience with big data technologies like Hadoop or Spark, and knowledge of data warehousing concepts.
*Business Intelligence (BI) Analyst
- Responsibilities : Gathering requirements from business stakeholders, designing and developing BI reports and dashboards, and providing data-driven insights to support strategic decision-making.
- Skills : Proficiency in BI tools like Tableau, Power BI, or Looker, strong SQL skills for data querying, understanding of data visualization principles, and ability to translate business needs into technical solutions.
*Data Architect
- Responsibilities: Designing the overall structure of data systems, including databases, data lakes, and data warehouses. Defining data models, schema, and data governance policies.
- Skills : Deep understanding of database technologies and architectures, experience with data modeling tools like Erwin or Vision, knowledge of data integration techniques, and familiarity with data security and compliance regulations.
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MODULE 1: Introduction to Data Science
- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & it’s types
MODULE 2: Introduction to Python
- What is Python?
- Why Python?
- Installing Python
- Python IDEs
- Jupyter Notebook Overview
MODULE 3: Python Basics
- Python Basic Data types
- Lists
- Slicing
- IF statements
- Loops
- Dictionaries
- Tuples
- Functions
- Array
- Selection by position & Labels
MODULE 4: Python Packages
- Pandas
- Numpy
- Sci-kit Learn
- Mat-plot library
Data Science Course Content
Introduction to Data Science with R
- What is Data Science?
- Significance of Data Science in today’s data-driven world, applications of Data Science, lifecycle of Data Science, and its components
- Introduction to Big Data Hadoop, Machine Learning, and Deep Learning
- Introduction to R programming and R Studio
Hands-on Exercise:
- Installation of R Studio
- Implementing simple mathematical operations and logic using R operators, loops, if statements, and switch cases
Data Exploration
- Introduction to data exploration
- Importing and exporting data to/from external sources
- What are data exploratory analysis and data importing?
- Data Frames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types
Hands-on Exercise:
- Accessing individual elements of customer churn data
- Modifying and extracting results from the dataset using user-defined functions in R
Data Manipulation
- Need for data manipulation
- Introduction to the dplyr package
- Selecting one or more columns with select(), filtering records on the basis of a condition with filter(), adding new columns with mutate(), sampling, and counting
- Combining different functions with the pipe operator and implementing SQL-like operations with SQL
Hands-on Exercise:
- Implementing dplyr
- Performing various operations for manipulating data and storing it
Data Visualization
- Introduction to visualization
- Different types of graphs, the grammar of graphics, the plotting package, categorical distribution with geom_bar(), numerical distribution with geom_hist(), building frequency polygons with geom_freq-poly(), and making a scatter-plot with geom_plot()
- Multivariate analysis with geom_box plot
- Uni variate analysis with a bar plot, a histogram and a density plot, and multivariate distribution
- Creating bar-plots for categorical variables using geom_bar(), and adding themes with the theme() layer
- Visualization with plot, frequency plots with geom_freq poly(), multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
- Working with co-ordinates and themes to make graphs more presentable, understanding plot and various plots, and visualization with visages
- Geographic visualization with magma() and building web applications with shiny R
Hands-on Exercise:
- Creating data visualization to understand the customer churn ratio using plotting charts
- Using plot for importing and analyzing data
- Visualizing tenure, monthly charges, total charges, and other individual columns using a scatter plot
Introduction to Statistics
- Why do we need statistics?
- Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures of spread
- Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, A NOVA, normal distribution, and binary distribution
Hands-on Exercise:
- Building a statistical analysis model that uses quantification, representations, and experimental data
- Reviewing, analyzing, and drawing conclusions from the data
Machine Learning
- Introduction to Machine Learning
- Introduction to linear regression, predictive modeling, simple linear regression vs multiple linear regression, concepts, formulas, assumptions, and residuals in Linear Regression, and building a simple linear model
- Predicting results and finding the p-value and an introduction to logistic regression
- Comparing linear regression with logistics regression and bi variate logistic regression with multivariate logistic regression
- Confusion matrix the accuracy of a model, understanding the fit of the model, threshold evaluation with ROAR, and using norm() and inline()
- Understanding the summary results with null hypothesis, F-statistic, and building linear models with multiple independent variables
Hands-on Exercise:
- Modeling the relationship within data using linear predictor functions
- Implementing linear and logistics regression in R by building a model with ‘tenure’ as the dependent variable
Logistic Regression
- Introduction to logistic regression
- Logistic regression concepts, linear vs logistic regression, and math behind logistic regression
- Detailed formulas, logit function and odds, bi-variate logistic regression, and Poisson regression
- Building a simple binomial model and predicting the result, making a confusion matrix for evaluating the accuracy, true positive rate, false positive rate, and threshold evaluation with ROAR
- Finding out the right threshold by building the ROC plot, cross validation, multivariate logistic regression, and building logistic models with multiple independent variables
- Real-life applications of logistic regression
Hands-on Exercise:
- Implementing predictive analytics by describing data
- Explaining the relationship between one dependent binary variable and one or more binary variables
- Using glam() to build a model, with ‘Churn’ as the dependent variable
Decision Trees and Random Forest
- What is classification? Different classification techniques
- Introduction to decision trees
- Algorithm for decision tree induction and building a decision tree in R
- Confusion matrix and regression trees vs classification trees
- Introduction to bagging
- Random forest and implementing it in R
- What is Naive Bayes? Computing probabilities
- Understanding the concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node
- Over fitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning, pruning a decision tree and predicting values, finding out the right number of trees, and evaluating performance metrics
Hands-on Exercise:
- Implementing random forest for both regression and classification problems
- Building a tree, pruning it using ‘churn’ as the dependent variable, and building a random forest with the right number of trees
- Using ROCR for performance metrics
Unsupervised Learning
- What is Clustering? Its use cases
- what is k-means clustering? What is canopy clustering?
- What is hierarchical clustering?
- Introduction to unsupervised learning
- Feature extraction, clustering algorithms, and the k-means clustering algorithm
- Theoretical aspects of k-means, k-means process flow, k-means in R, implementing k-means, and finding out the right number of clusters using a scree plot
- Dendograms, understanding hierarchical clustering, and implementing it in R
- Explanation of Principal Component Analysis (PCA) in detail and implementing PCA in R
Hands-on Exercise:
- Deploying unsupervised learning with R to achieve clustering and dimensional reduction
- K-means clustering for visualizing and interpreting results for the customer churn data
Association Rule Mining and Recommendation Engines
- Introduction to association rule mining and MBA
- Measures of association rule mining: Support, confidence, lift, and prior algorithm, and implementing them in R
- Introduction to recommendation engines
- User-based collaborative filtering and item-based collaborative filtering, and implementing a recommendation engine in R
- Recommendation engine use cases
Hands-on Exercise:
- Deploying association analysis as a rule-based Machine Learning method
- Identifying strong rules discovered in databases with measures based on interesting discoveries
Self-paced Course Content
Introduction to Artificial Intelligence
- Introducing Artificial Intelligence and Deep Learning
- What is an artificial neural network? TensorFlow: The computational framework for building AI models
- Fundamentals of building ANN using TensorFlow and working with TensorFlow in R
Time Series Analysis
- What is a time series? The techniques, applications, and components of time series
- Moving average, smoothing techniques, and exponential smoothing
- Uni variate time series models and multivariate time series analysis
- AROMA model
- Time series in R, sentiment analysis in R (Twitter sentiment analysis), and text analysis
Hands-on Exercise:
- Analyzing time series data
- Analyzing the sequence of measurements that follow a non-random order to identify the nature of phenomenon and forecast the future values in the series
Support Vector Machine (SVM)
- Introduction to Support Vector Machine (SVM)
- Data classification using SVM
- SVM algorithms using separable and inseparable cases
- Linear SVM for identifying margin hyperplane
Naive Bayes
- What is the Bayes theorem?
- What is Naive Bayes Classifier?
- Classification Workflow
- How Naive Bayes classifier works and classifier building in Scikit-Learn
- Building a probabilistic classification model using Naïve Bayes and the zero probability problem
Text Mining
- Introduction to the concepts of text mining
- Text mining use cases and understanding and manipulating the text with ‘tm’ and ‘stringR’
- Text mining algorithms and the quantification of the text
- TF-IDF and after TF-IDF
R Programming Language – Introduction
R is an open-source programming language that is widely used as a statistical software and data analysis tool. R generally comes with the Command-line interface. R is available across widely used platforms like Windows, Linux, and macOS. Also, the R programming language is the latest cutting-edge tool.
It was designed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently being developed by the R Development Core Team.
R programming language is an implementation of the S programming language. It also combines with lexical scoping semantics inspired by Scheme. Moreover, the project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000.
What is R Programming Language?
- R programming is used as a leading tool for machine learning, statistics, and data analysis. Objects, functions, and packages can easily be created by R.
- It’s a platform-independent language. This means it can be applied to all operating systems.
- It’s an open-source free language. That means anyone can install it in any organization without purchasing a license.
- R programming language is not only a statistic package but also allows us to integrate with other languages (C, C++). Thus, you can easily interact with many data sources and statistical packages.
- The R programming language has a vast community of users and it’s growing day by day.
- R is currently one of the most requested programming languages in the Data Science job market which makes it the hottest trend nowadays
- It was designed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently being developed by the R Development Core Team.
- R programming language is an implementation of the S programming language. It also combines with lexical scoping semantics inspired by Scheme. Moreover, the project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000.
Why Use R?
- Statistical Analysis
- Open Source
- Data Visualization
- Data Manipulation
- Integration
- Community and Packages
*Statistical Analysis : R is designed for analysis and It provides an extensive collection of graphical and statistical techniques, By making a preferred choice for statisticians and data analysts.
*Open Source : R is an open – source software, which means it is freely available to anyone. It can be accessble by a vibrant community of users and developers.
*Data Visualization : R boasts an array of libraries like ggplot2 that enable the creation of high-quality, customizable data visualizations.
*Data Manipulation : R offers tools that are for data manipulation and transformation. For example: IT simplifies the process of filtering , summarizing and transforming data.
*Integration : R can be easily integrate with other programming languages and data sources. IT has connectors to various databases and can be used in conjunction with python, SQL and other tools
*Community and Packages : R has vast ecosystem of packages that extend its functionality. There are packages that can help you accomplish needs of analytics.
Features of R Programming Language
- R Packages: One of the major features of R is it has a wide availability of libraries. R has CRAN(Comprehensive R Archive Network), which is a repository holding more than 10, 0000 packages.
- Distributed Computing: Distributed computing is a model in which components of a software system are shared among multiple computers to improve efficiency and performance. Two new packages ddR and multiplyb used for distributed programming in R were released in November 2015.
Statistical Features of R
- Basic Statistics
- Static graphics
- Probability distributions
- Data analysis
* Basic Statistics : The most common basic statistics terms are the mean, mode, and median. These are all known as “Measures of Central Tendency.” So using the R language we can measure central tendency very easily.
*Static graphics : R is rich with facilities for creating and developing interesting static graphics. R contains functionality for many plot types including graphic maps, mosaic plots, biplots, and the list goes on.
*Probability distributions : Probability distributions play a vital role in statistics and by using R we can easily handle various types of probability distributions such as Binomial Distribution, Normal Distribution, Chi-squared Distribution, and many more.
*Data analysis: It provides a large, coherent, and integrated collection of tools for data analysis.
Basic R program
Since R is much similar to other widely used languages syntactically, it is easier to code and learn in R. Programs can be written in R in any of the widely used IDE like R Studio, Rattle, Tinn-R, etc. After writing the program save the file with the extension .r. To run the program use the following command on the command line:
Advantages of R
- R is the most comprehensive statistical analysis package. As new technology and concepts often appear first in R.
- As R programming language is an open source. Thus, you can run R anywhere and at any time.
- R programming language is suitable for GNU/Linux and Windows operating systems.
- R programming is cross-platform and runs on any operating system.
- In R, everyone is welcome to provide new packages, bug fixes, and code enhancements.
Disadvantages of R
- In the R programming language, the standard of some packages is less than perfect.
- Although, R commands give little pressure on memory management. So R programming language may consume all available memory.
- In R basically, nobody to complain if something doesn’t work.
- R programming language is much slower than other programming languages such as Python and MATLAB.
Applications of R
- We use R for Data Science. It gives us a broad variety of libraries related to statistics. It also provides the environment for statistical computing and design.
- R is used by many quantitative analysts as its programming tool. Thus, it helps in data importing and cleaning.
- R is the most prevalent language. So many data analysts and research programmers use it. Hence, it is used as a fundamental tool for finance.
- Tech giants like Google, Facebook, Bing, Twitter, Accenture, Wipro, and many more using R nowadays.
Conclusion
In conclusion , R programming language has emerged as powerful and versatile too for data analysis, statistical modelling and machine learning. R remains the top choice for data scientists, statisticians and researchers across various domain.
Introduction to Machine Learning in R
The word Machine Learning was first coined by Arthur Samuel in 1959. The definition of machine learning can be defined as that machine learning gives computers the ability to learn without being explicitly programmed. Also in 1997, Tom Mitchell defined machine learning that “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”. Machine learning is considered to be the most interesting field of computer science.
What is Machine Learning?
Machine Learning is a branch of artificial intelligence that develops algorithms by learning the hidden patterns of the datasets used it to make predictions on new similar type data, without being explicitly programmed for each task.
Traditional Machine Learning combines data with statistical tools to predict an output that can be used to make actionable insights.
Machine learning is used in many different applications, from image and speech recognition to natural language processing, recommendation systems, fraud detection, portfolio optimization, automated task, and so on. Machine learning models are also used to power autonomous vehicles, drones, and robots, making them more intelligent and adaptable to changing environments.
A typical machine learning tasks are to provide a recommendation. Recommended systems are a common application of machine learning, and they use historical data to provide personalized recommendations to users. In the case of Netflix, the system uses a combination of collaborative filtering and content-based filtering to recommend movies and TV shows to users based on their viewing history, ratings, and other factors such as genre preferences.
Reinforcement learning is another type of machine learning that can be used to improve recommendation-based systems. In reinforcement learning, an agent learns to make decisions based on feedback from its environment, and this feedback can be used to improve the recommendations provided to users. For example, the system could track how often a user watches a recommended movie and use this feedback to adjust the recommendations in the future.
Personalized recommendations based on machine learning have become increasingly popular in many industries, including e-commerce, social idea, and online advertising, as they can provide a better user experience and increase engagement with the platform or service.
The breakthrough comes with the idea that a machine can singularly learn from the data (i.e., an example) to produce accurate results. Machine learning is closely related to data mining and Data Science. The machine receives data as input and uses an algorithm to formulate answers.
Difference between Machine Learning and Traditional Programming
The Difference between Machine Learning and Traditional Programming is as follows:
|
Machine Learning |
Traditional Programming |
Artificial Intelligence |
|---|---|---|
| Machine Learning is a subset of artificial intelligence(AI) that focus on learning from data to develop an algorithm that can be used to make a prediction. | In traditional programming, rule-based code is written by the developers depending on the problem statements. | Artificial Intelligence involves making the machine as much capable, So that it can perform the tasks that typically require human intelligence. |
| Machine Learning uses a data-driven approach, It is typically trained on historical data and then used to make predictions on new data. | Traditional programming is typically rule-based and deterministic. It hasn’t self-learning features like Machine Learning and AI. | AI can involve many different techniques, including Machine Learning and Deep Learning, as well as traditional rule-based programming. |
| ML can find patterns and insights in large datasets that might be difficult for humans to discover. | Traditional programming is totally dependent on the intelligence of developers. So, it has very limited capability. | Sometimes AI uses a combination of both Data and Pre-defined rules, which gives it a great edge in solving complex tasks with good accuracy which seem impossible to humans. |
| Machine Learning is the subset of AI. And Now it is used in various AI-based tasks like Chat bot Question answering, self-driven car., etc. | Traditional programming is often used to build applications and software systems that have specific functionality. | AI is a broad field that includes many different applications, including natural language processing, computer vision, and robotics. |
How machine learning algorithms work
Machine Learning works in the following manner.
- Forward Pass: In the Forward Pass, the machine learning algorithm takes in input data and produces an output. Depending on the model algorithm it computes the predictions.
- Loss Function: The loss function, also known as the error or cost function, is used to evaluate the accuracy of the predictions made by the model. The function compares the predicted output of the model to the actual output and calculates the difference between them. This difference is known as error or loss. The goal of the model is to minimize the error or loss function by adjusting its internal parameters.
- Model Optimization Process: The model optimization process is the iterative process of adjusting the internal parameters of the model to minimize the error or loss function. This is done using an optimization algorithm, such as gradient descent. The optimization algorithm calculates the gradient of the error function with respect to the model’s parameters and uses this information to adjust the parameters to reduce the error. The algorithm repeats this process until the error is minimized to a satisfactory level.
Once the model has been trained and optimized on the training data, it can be used to make predictions on new, unseen data. The accuracy of the model’s predictions can be evaluated using various performance metrics, such as accuracy, precision, recall, and F1-score.
Machine Learning lifecycle:
The lifecycle of a machine learning project involves a series of steps that include
- Study the Problems
- Data Collection
- Data Preparation
- Model Selection
- Model building and Training
- Model Evaluation
- Model Tuning
- Deployment
- Monitoring and Maintenance
*Study the Problems: The first step is to study the problem. This step involves understanding the business problem and defining the objectives of the model.
*Data Collection: When the problem is well-defined, we can collect the relevant data required for the model. The data could come from various sources such as databases, APIs, or web scraping.
*Data Preparation: When our problem-related data is collected. then it is a good idea to check the data properly and make it in the desired format so that it can be used by the model to find the hidden patterns. This can be done in the following steps:
-
- Data cleaning
- Data Transformation
- Explanatory Data Analysis and Feature Engineering
- Split the dataset for training and testing.
*Model Selection: The next step is to select the appropriate machine learning algorithm that is suitable for our problem. This step requires knowledge of the strengths and weaknesses of different algorithms. Sometimes we use multiple models and compare their results and select the best model as per our requirements.
*Model building and Training: After selecting the algorithm, we have to build the model.
-
- In the case of traditional machine learning building mode is easy it is just a few hyperparameter tunings.
- In the case of deep learning, we have to define layer-wise architecture along with input and output size, number of nodes in each layer, loss function, gradient descent optimizer, etc.
- After that model is trained using the pre processed dataset.
*Model Evaluation: Once the model is trained, it can be evaluated on the test dataset to determine its accuracy and performance using different techniques like classification report, F1 score, precision, recall, ROC Curve, Mean Square error, absolute error, etc.
*Model Tuning : Based on the evaluation results, the model may need to be tuned or optimized to improve its performance. This involves tweaking the hyper parameters of the model.
*Deployment : Once the model is trained and tuned, it can be deployed in a production environment to make predictions on new data. This step requires integrating the model into an existing software system or creating a new system for the model.
*Monitoring and Maintenance : Finally, it is essential to monitor the model’s performance in the production environment and perform maintenance tasks as required. This involves monitoring for data drift, retraining the model as needed, and updating the model as new data becomes available.
How Machine Learning Works?
- Clean the data obtained from the dataset
- Select a proper algorithm for building a prediction model
- Train your model to understand the pattern of project
- Predict your results with higher accuracy
Classification Of Machine Learning
Machine learning implementations are classified into 3 major categories, depending on the nature of learning.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
R language
*Supervised Learning : Supervised learning as the name itself suggests that under the presence of supervision. In short in supervised learning we try to teach the machine with the data using labels and which already have the correct answer in it. After this, the machine will create an example set of data so that the supervised algorithm analyses the training data and produce the correct output of the labeled data. For example, if we create a set of data of fruits then we will be labeling as the fruit having a round shape with a dip upside and red in color then it is termed as an apple. Now when we ask the machine to identify the apple from the basket of fruits then it will use the previous labeling and identify an apple. Supervised Learning is classified into two categories as below:
-
- Classification: A classification problem is when the output variable is a category, such as “Red” or “Orange” or “countable” or “not countable”.
- Regression: A regression is used when the output variable is real value, such as “rupees” or “height”.
*Unsupervised Learning : Unsupervised learning is the training of machines using information that is not labeled and it works without any guidance. Here the main task of the machine is to separate the data using the similarities, differences, and patterns without any prior supervision. Hence, the machine is restricted to find the hidden structure in unlabeled data by own-self. For example, if we provide a group of cats and dogs which are never seen before. Then the machine will differentiate the group of cats and dogs according to their behavior and nature. Now when we provide the pictures of dogs and cats according to the classification made by the machine it will provide the result. Unsupervised Learning is classified into two categories as below:
-
- Clustering : A clustering problem is where the machine identify the inherent groupings in the data, such as grouping customers according to visits in the shop.
- Association : An association problem is where we can find the relation between two events or items, such as people buying item A also tends to buy B.
*Reinforcement Learning The reinforcement learning method is all about taking suitable action to maximize reward in a particular situation. It is supervised by various machines to take the best possible path to solve the problem in a specific situation. The difference between reinforcement learning and supervised learning is that in supervised learning the data has a key of the correct answer which it uses to find the answer but in reinforcement, the agent decides what to do perform the given task. For example, while traveling from one place to another we always consider the shortest and best part possible to reach the destination. Some main points in reinforcement learning:
-
- Input: The input should be from the initial stage where the model actually starts.
- Output: There are multiple outputs to any problem.
- Training: As the training is dependent on input, the model will return the state and the user will decide to reward or discard the model based on its output.
*R language is basically developed by statisticians to help other statisticians and developers faster and efficiently with the data. As by now, we know that machine learning is basically working with a large amount of data and statistics as a part of data science the use of R language is always recommended. Therefore the R language is mostly becoming handy for those working with machine learning making tasks easier, faster, and innovative. Here are some top advantages of R language to implement a machine learning algorithm in R programming.
Advantages to Implement Machine Learning Using R Language
- It provides good explanatory code. For example, if you are at the early stage of working with a machine learning project and you need to explain the work you do, it becomes easy to work with R language comparison to python language as it provides the proper statistical method to work with data with fewer lines of code.
- R language is perfect for data visualization. R language provides the best prototype to work with machine learning models.
- R language has the best tools and library packages to work with machine learning projects. Developers can use these packages to create the best pre-model, model, and post-model of the machine learning projects. Also, the packages for R are more advanced and extensive than python language which makes it the first choice to work with machine learning projects.
Popular R Language Packages Used to Implement Machine Learning
- lattice
- Data Explorer
- Dale x(Descriptive Machine Learning Explanations)
- dplyr
- Esquire
- caret
- janitor
- r part
- lattice: The lattice package supports the creation of the graphs displaying the variable or relation between multiple variables with conditions.
- Data Explorer: This R package focus to automate the data visualization and data handling so that the user can pay attention to data insights of the project.
- Dale x(Descriptive Machine Learning Explanations): This package helps to provide various explanations for the relation between the input variable and its output. It helps to understand the complex models of machine learning
- dplyr: This R package is used to summarize the tabular data of machine learning with rows and columns. It applies the “split-apply-combine” approach.
- Esquire: This R package is used to explore the data quickly to get the information it holds. It also allows to plot bar graph, histograms, curves, and scatter plots.
- caret: This R package attempts to streamline the process for creating predictive models.
- janitor: This R package has functions for examining and cleaning dirty data. It is basically built for the purpose of user-friendliness for beginners and intermediate users.
- rpart: This R package helps to create the classification and regression models using two-stage procedures. The resulting models are represented as binary
Application Of R in Machine Learning
There are many top companies like Google, Facebook, Uber, etc using the R language for application of Machine Learning. The application are:
- Social Network Analytics
- To analyze trends and patterns
- Getting insights for behaviour of users
- To find the relationships between the users
- Developing analytical solutions
- Accessing charting components
- Embedding interactive visual graphics
Example of Machine Learning Problems
- Web search like Siri, Alexa, Google, Cortona : Recognize the user’s voice and fulfill the request made]
- Social Media Service : Help people to connect all over the world and also show the recommendations of the people we may know
- Online Customer Support : Provide high convenience of customer and efficiency of support agent
- Intelligent Gaming : Use high level responsive and adaptive non player characters similar to human like intelligence
- Product Recommendation : A software tool used to recommend the product that you might like to purchase or engage with
- Virtual Personal Assistance : It is the software which can perform the task according to the instructions provided
- Traffic Alerts : Help to switch the traffic alerts according to the situation provided
- Online Fraud Detection : Check the unusual functions performed by the user and detect the frauds
- Healthcare : Machine Learning can manage a large amount of data beyond the imagination of normal human being and help to identify the illness of the patient according to symptoms
- Real world example : When you search for some kind of cooking recipe on YouTube, you will see the recommendations below with the title “You May Also Like This”. This is a common use of Machine Learning.
Types of Machine Learning Problems
- Regression : The regression technique helps the machine learning approach to predict continuous values. For example, the price of a house.
- Classification : The input is divided into one or more classes or categories for the learner to produce a model to assign unseen modules. For example, in the case of email fraud, we can divide the emails into two classes i.e “spam” and “not spam”.
- Clustering : This technique follows the stigmatization, finding a group of similar entities. For example, we can gather and take readings of the patients in the hospital.
- Association : This technique finds co-occurring events or items. For example, market-basket.
- Anomaly Detection : This technique works by discovering abnormal cases or behavior. For example, credit card fraud detection.
- Sequence Mining : This technique predicts the next stream event. For example, click-stream event.
- Recommendation : This technique recommends the item. For example, songs or movies according to the celebrity in it.
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