# Understanding Some Commonly Used Machine Learning Algorithms

In the next ten years, it is believed that machine learning algorithms will replace many jobs worldwide. The growth of data is upgrading rapidly. Self-modifying and automated information is evolving and progressing, which results in extensive automation of data.
The simplest example of the machine learning algorithm is the way computers play chess. The algorithm is designed to understand the player’s moves and play according to them. Before we get to the conclusion, let us know the basic idea of the machine learning algorithm.

What is a machine learning algorithm?

A machine learning algorithm, in simple terms, is defined as an approach that works and runs on data. A user can build a production-ready model with the help of machine learning.

In simple terms, if an airplane is using machine learning to accomplish a task, then the machine learning algorithm will be the engines inside of the plane which completes the job. Of course the use of machine learning has become vast, from our cars and favorite search engines to dating apps to meet milfs in your area.

There are three types of machine learning algorithms:

• Supervised: In this learning, the algorithms are given a set of samples for prediction. The algorithms use patterns within the label to predict the outputs. Examples of supervised learning are Linear Regression, Decision tree, KNN.
• Unsupervised: The data in this algorithm organize into a group of clusters. It then describes its structure. The learning algorithm then converts complex data into a simple and organized way for analysis. An example of unsupervised learning algorithms is K-means and Apriori algorithms.
• Reinforcement: with this algorithm, the machine is designed to make specific decisions. The device uses the trial and error met hid to train itself continuously. It learns from its past mistakes and captures knowledge to make correct decisions. An example of a reinforcement learning algorithm is the Markov decision process.

Some of the commonly used machine learning algorithms is as follows:

• Naïve Bayes Algorithm

The algorithm is based on the Bayes theorem. It assumes that in a class, the presence of a specific feature is not related to any of the other components. The theorem means that the elements are all independent of each other.

The theorem is based on probabilities. It is one of the most popular algorithms as it is straightforward and easy to understand. The algorithm is efficient and valuable for large database sets.

• Linear Regression

The Linear regression algorithms show the relationship between two variables. It also shows that when one variable has an impact, the other variable is also affected. The algorithm helps to estimate the real continuous value of the products.
Some of the common examples are sales predictions, employee salary estimation, and predicting housing prices.

When the independent and dependent variables fit into a line, a relationship is established. The line is known as the regression line. It is presented by the equation Y= a*X+b. Y is the dependent variable, x is the independent variable, a is the slope, and b is the intercept.

Linear regression is based on predictions and is made of two types; simple and multiple linear regressions. Single linear regression is made by one independent variable, and multiple is characterized by more than one variable.

• Decision tree

The Machine learning algorithm; decision tree is one of the most used and popular algorithms. It helps in classifying problems. It is one of the most reliable and popular learning algorithms for the classification of issues.

Categorical and continuous dependent variables categorically work very well with the decision tree algorithm. The algorithm works by splitting homogeneous groups by taking remarkable attribute variables.

The learning algorithm makes a graphical representation of a tree and its branches.
A decision tree helps in capturing ideas if there is confusion on how to operate a situation.

• Logistic regression

The algorithm performs classification of tasks, unlike linear regression that performs regression problems. It works by applying a logistic function to a variety of features. The algorithm then predicts the outcome of an attribute variable.

It is also called logit regression as it adds data to the logit function and predicts probability. The output value comes between 0 and 1.

• SVM(support vector machine) algorithm

The learning algorithm works in a classification method. Each data is the plot in an n-dimensional space, where n is the number of features. The value of each feature variable is attached to a particular coordinate. It helps to make it easy to analyze the data. The line splits the data, which are called classifiers, and it then plots the features into the graph for analysis.

• KNN algorithm

The KNN (k-nearest neighbor) algorithms work well for regression and classification problems. It is one of the most simple machine learning algorithms.

The algorithm stores all the available cases. It then classifies new topics by voting, the majority vote, of its K- neighbors. The most common class to the instances is then assigned, and a distance function performs the measurements.

A simple example to explain the algorithm is by comparing it with real life. If you want to buy vegetables, then you will have to look around in markets and shops. The shop with a variety of fresh vegetables and is nearest to your location is the one you will choose to purchase.

# What Are the Top Data Science Programming Languages?

A formal language which consists of instructions set that produce different types of output is called a programming language. These kinds of languages are used in the programs of the computer for implementing the algorithms and comprises of several apps. You can find different programming languages for data science. Many of the data scientist need to master and learn at least a single programming language. Because it is crucial device to realize the functions of data science. There are two kinds of programming languages one is high level and low level. The language of low level is less developed and computers most understandable language for performing various function. It involves machine and assembly language. While the language of assembly deals with manipulation of direct hardware and issues of performance, a machine language is binaries read and computer executed. The software of assembler will convert this assembly language into machine code.

The programming languages of low level are faster and high memory efficient when compared with the counter parts of high level. Another programming language offers powerful details and programming concepts abstraction. The high-level languages can produce code which is independent of the type of computer. They are as well closer to human language, and utilized for instructions of problem solving, and are portable. Such that most of the data scientists utilize languages of high-level programming. The one who want to enter into the data science specialization need to learn some programming languages to become a data scientist.

Different types of data science programming languages

Let’s discuss about various kinds of data science programming languages.

JavaScript

Data scientists claim JavaScript as the prominent object-oriented programming language. Nowadays, tons of java libraries are accessible for covering any type of issue which a programmer can come across to solve. You can find languages that are exception for producing dashboards and visualizing the information. This language which is versatile is able to handle multiple tasks at a time. It is also beneficial for embedding all the things from electronics to web applications and desktop. The frameworks of popular processing such as Hadoop keep running on Java language. It is the best languages used by data scientist. It can easily and quickly scale up applications which are large.

R

It is the programming language of high level constructed by statisticians. The language of open source and software are utilized for graphics and statistical computing. However, it consists of various applications in data science and also it consists of different data science libraries. The language R can be used for data sets exploring and performing analysis of hoc. But the loops consist of more than thousand iterations and it is complicated to learn that python programming language.

Julia

It is a programming language of data science which is a developed one whose purpose is for performing a high-performance computational science and speedy numerical analysis. It also can implement the concepts of mathematics such as linear algebra. Julia is an amazing language which deal with the matrices and other kind of algorithms of mathematics. It can be utilized for two programming one is back end and front end. It consists of API that can be incorporated in the programming language.

SQL

SQL in other words a structured query language has turned into a prominent programming language for maintaining the information over the years. Even though it is not utilized for performing the operations of data science, SQL tables and queries can really help data scientists in dealing with the systems of data management. This is one of the domain specific language which is extremely comfortable for manipulating, retrieving, and storing the information in databases that are relational.

Scala

Scale is the elegant and modern programming language produced in the year of 2003. It was actually designed to identify the problems associated with Java language. Its range of application from the programming of web to machine learning. It is also one of the effective and scalable languages for big data handling. In todays companies, this programming language is supporting functional and object oriented and also synchronized and concurrent processing.

Python

It is the most popular data science programming language in the recent days. Python is easy to utilize and an open source programming language which is in the field of programming languages from the year 1991. Python is a dynamic as well as object-oriented programming which supports various paradigms from functional to structured and programming of procedure. Such that it is considered as the prominent programming language for data scientists also. It consists of less than 1000 iteration so it is a best and fast choice for the manipulations of data. The processing of natural data and learning of data is easier with the python having packages in it. Also, it is easier for programmers for reading the information in a spreadsheet for making an output of CSV.

Thus, these are some of the best data science programming languages.

# Understanding Everything About Big Data

The term which actually describes large amount of data both structured and unstructured that overwhelm the business on the basis of every day is big data. Yet it is not the information which is crucial. It is what the companies do with the information which matters to them. It can be analyzed for the insights for leading a business to take best decisions as well as business moves that are strategic.

The significance of big data

The big data significance does not revolve around the amount of the data you have yet it is about what you are going to do with the data. You can pick up the data from any kind of source and then analyze it for finding answers that allow time reductions smart decision making, cost reductions, optimized offerings and new item development. When you mix the big data with the analytics of high power then you can achieve some business regarding tasks like:

• Generation of the coupons at the sale point depending on the buying habits of customer.
• Identifying the root which is causing issues, failures, and defects in real time that are nearer.
• It recalculates the total risk of the portfolios in fraction of seconds.
• The big data detects any kind of fraud type behavior even before it impacts your company.

Understanding the workings of big data

Many of the businesses are adopting big data, but before they make it to work for them, it is crucial to understand it’s working. It is like multitude of sources, locations, owners, and systems etc. There are about five important steps to take the charge of the fabric of big data which involves structured and traditional information, also with semi-structured as well as unstructured data.

Put a strategy of big data:

Strategy of big data is a scheme which is created at a high level to help you in improving and oversee the manner you store, manage, share, and acquire the useful data outside as well within of your company. It sets the place for the success of business between the data abundance. When you implement the technique, it is crucial to consider existing and future technology of business and its goals along with initiatives. This will let you treat big data like other business assets that are valuable instead of applications by-product.

Understand the big data sources:

• Data streaming comes from the Internet of Things and other devices that are connected will flow into systems of IT from smart cars, wearables, industrial equipment, and medical devices etc. You can analyse this big data as it approaches, as you decide which information to keep or not keep and requires further analysation.
• The data of social media stems from Instagram, Facebook, and YouTube etc. This involves usage of big data in the images, video, text, sound forms which is helpful for functions of sales, support, and marketing. This information is often semi structured or unstructured so it displays a unique challenge for analysis and consumption.
• The data which is publicly accessible comes from open data sources from massive amount such as CIA world Factbook, European union open data portal, and US government data.
• Other kinds of big data might come from cloud data sources, data lakes, customers, and suppliers.

Manage, access and store big data:

Nowadays, most of the modern systems of computing offer power, flexibility, and speed required for accessing large amounts of big data types. Companies also require techniques for ensuring quality of data, offering data governance, integrating the data, preparing data for analytics, and storage. Few information might be stored in premises in a traditional warehouse of data. However, there are also low-cost choices and flexible for handling and storing big data through solutions of cloud, Hadoop, and data lakes.

Analyze the big data:

The innovations of high performance such as in memory analytics or grid computing, the companies can be able to pick for using all their big data for analyzing. The other way to determine this upfront is based on the relevant information even before it is analyzed. On the other side, analytics of big data is how the organizations acquire insights and value from the information.

Making decisions that are data driven:

The trusted and well managed data leads to the decisions and analytics that are trusted. For staying competitive, business require to seize the entire data value and function it in a data driven way. It lets you to make decisions depending on the evidence offered by big data rather than instinct of gut. The advantages of data driven solutions are with clarity. The organizations which are data driven are more profitable and predictable.

# How Do Companies Use Data Mining?

WHAT IS DATA MINING?

Data mining is a process of data science technology. The data science technology works completely on the data so do the data mining. In this technology, many processes are involved, but data mining is said to be the most important one. In the data mining process, the collected data is analyzed for discovering the hidden patterns in it. The data is collected before analysis. The data which is collected is in impure form. This means that all the data which is accumulated is not of use. We have to use only useful information from it. That’s why data mining is done.

The data mining process discovers the hidden patterns from the data which is further used for solving the problems. The data is divided into sub-parts and then data mining is performed on them. After the process, the data is integrated again. All these tasks are executed and accomplished at the place called data warehouses. The data mining process is majorly used by companies. This is so because this process helps in reducing the overall cost with higher revenue.

STEPS FOLLOWED IN THE DATA MINING

Here the steps followed in the data mining process are described precisely. They are listed below:

• The data is first collected from the people with the help of different methods.
• After the collection of the data, it is transformed.
• After the transformation, the data is loaded into the data warehouses.
• When the data reach data warehouses, then it is managed and stored in the multidimensional databases.
• When the data is stored in the databases, the authorization is provided to the data scientists or data analysts for accessing the data. This is done with the help of application software.
• The data is then finally represented into easier and readable forms. For example, graphs, charts, etc.

TYPES OF DATA IN COMPANIES

At present, the companies are implementing the data mining process for making their business more efficient. Of course, the data mining process also provides high revenues at a low cost. This is another reason for using data mining in companies.

The first step of the companies is to gather the required data. Generally, the data of the company is classified into three types which are:

• Transactional
• Non-operational

The transactional data covers that data on which operations are performed on each day. For example, costs, inventory, sales, etc. Non-operational data includes the data of forecasting etc. While the metadata consists of the logical designs of the databases.

DATA MINING TO KNOW THE CUSTOMERS

The most important thing for a company is the customers. For the success of a company, the company must have a strong base of customers. It is very important to keep your customers attracted to your company. Many companies fail to do so because they don’t know about what products their customers are looking for? Many companies are very slow. They are slow in understanding the needs of their customers. When a company slowly understands the needs of the customers, then they start to deploy the products of poor quality which are not appreciated and liked by the customers.

The data mining process helps in understanding the need of every customer by discovering hidden patterns from the data and by deep analysis of the data. One strong example of this is when looking at adult dating and hookup applications. Free sex apps like Meetnfuck App utilized data from users on other top casual sex sites in order to tailor their platform to better fit the needs of their target demographic. The examples of data mining to better understand customers are endless. Another example of this would be food delivery applications. We get many offers on food items every day. What these food delivery companies do is analyze the shopping behavior of every customer. They analyze that which food item is frequently purchased by which customers. Then they make the clusters of those customers who purchase the same food items. Then they provide special offers to the customers accordingly. In this way, the data mining process help in building a strong customer base.

DATA MINING FOR THE MARKETING

The companies invest a huge amount of money in the marketing and advertisement campaigns of their products. Even after this, many of them fail in promoting their products. This is so because the marketing and advertisements are not reached to the right audience. As said above, the data mining process help in understanding the needs of the customers. After knowing the needs of the customers, companies can easily deliver the products. The data mining process helps in keeping an eye on the online activities of the customers. For example, for what products they are searching for, which products or types of products are liked by the customers, etc.

The data mining process helps in doing the advertising and marketing to the targeted audience. This activity results in a lower cost with an efficient result. The best example of this would be Netflix. If you have the subscription of Netflix then you would better know that this application suggests next movies or series watch after finishing one. Netflix does so by analyzing the past data of its customers. It analyzes the past data as well as the last movies or series you have watched. Based on that analysis, it gives new suggestions. This activity also helps in building good relations with customers. That’s why data mining is being used by many companies around the world.

DATA MINING FOR RISK MANAGEMENT

The companies have one more reason to use data mining in their business which is risk management. The data mining process prevents the company from many risks. The data mining process for risk management is more useful for financial institutions. The companies have to face many fake people who do not return the borrowed money. This activity leads the company to debt. With the help of data mining, the companies analyze the past data of their customers. The analysis of the internal data is preferred by the companies first. After the analysis, they decide whether it is safe to approve the loan to the applicant or not. Along with this, the data mining process also increases the quality of the tools which are used in risk management.

# What is Data Science?

Data science is a common term used in various fields these days because it is gaining more importance due to several reasons. It is a type of study meant for obtaining meaningful insights from data with a combination of programming skills, domain expertise, business skills, and statistics. Data scientists will use machine learning algorithms with artificial intelligence applications that can perform several tasks that need human intelligence. In addition, they generate insights that add more value to a business. The demands of data scientists are increasing day by day in the markets and they will get jobs in a company with a high salary.

What is the significance of data science?

Several companies today utilize digital spaces that deal with structured and unstructured data. As a result, they want to remain competitive in the markets for a long time to earn more revenues. With data science, it is possible to develop the big data required for development and implementation purposes. Data science is a blend of several structures including machine learning principles thereby showing ways to explore the hidden patterns from the raw data. It provides methods to determine the predictions and make decisions with deep analysis.

What are the advantages of data science?

The primary advantage of data science is that it helps to improve the products and services of a company based on customer feedback. A business cannot survive in the markets unless it has a robust customer base. Data science enables businesses to learn more about the buying trends of customers in detail thereby showing to make changes accordingly. A risk management plan is necessary irrespective of the industry and volume. Businesses can get solutions for risk management problems with big data analytics for running them without any difficulties.

Learning more about the life cycle of data science

Most companies will make mistakes in data collection and analysis without understanding the needs properly. Therefore, it is necessary to learn more about the life cycle of data science in detail for meeting essential requirements. It involves six phases enabling companies to focus more on their goals with high accuracy. They include discovery, data preparation, model planning, model building, operationalization, and communication which help to get optimal results. All phases play an important role in case studies letting companies find solutions for a problem with desired outputs. Moreover, they give ways to take a business to the next levels that can generate more revenues.

Major challenges faced by data scientists

Data science is growing in different parts of the world because it contributes more to improve decision-making skills and other things. A majority of data scientists face many challenges when they deal with data. Some of them include multiple data sources, data quality, data quantity, predictions, and not identifying the issues properly. Therefore, data scientists should know to manage them with ease for overcoming complications. They can create meta-algorithms which ultimately paves ways to generate data from others with similar results but different data sets.

How businesses can leverage data analytics?

Businesses can leverage data analytics with professionals who have a wide range of skills which ultimately help to attain top positions in the markets. Data science is an ideal one for all sizes of businesses to understand the current state of business thereby helping to build a solid foundation to predict future outcomes. This, in turn, gives ways to develop a product that perfectly matches the market needs. It even enables a business to target potential customers with customized advertisements. Targeting by using data can be very precise and specific as detailed by https://LocalSexFinder.app in their blog about hookup apps and how they target users based on location. Data analytics can help companies to streamline their operations significantly which maximizes the profits.

Things to consider while hiring a data scientist

Companies that are in need of data scientists should consider certain important things before hiring them. Some of them include the purpose, profiles, qualifications, previous experience if any, building a data-driven culture, and so on. It is necessary to evaluate the skills of data scientists in detail with special attention to get more ideas. Another thing is that it makes feasible ways to find the best one among them that can help to make the projects a successful one. A company should give more importance to a data scientist who contributes more to the development and growth.

# Best Online Data Science Courses

Data Science courses are offered by many online platforms from different universities and companies. Each course has unique features that benefit people looking for a data science course online. Here are the best online Data science courses which do not need any prior programming experience. Its features are also included.

IBM Coursera Professional certificate course

Professional Data Science course from IBM is specifically designed for learners to adapt to the working environment. Real-life examples are explained and experienced by learners. Positive results are obtained from those who are opting for a career change. Learners looking for better and fun options can easily learn from the basics. It is from 3 to 7 weeks per sub course. The complete course includes 9 sub courses. Upon completion, the learner is well equipped with real-time situations to compete with the experienced data scientists. With certain consistency and motivation, this course certification can be helpful for career growth.

MIT edX certification course

Comparatively, this is a smaller course with 5 sub-courses. Data science taught here is mostly based on machine learning and statistics. Big data has been in demand in recent years and is included in this course with statistical data and probability. The basic concepts taught here are different, as it is more beneficial to large global companies where millions of data are involved. The decision-making skills are highly complex having ample data. It highly involves playing with numbers and analyzing to get conclusions. The basics become stronger after the completion of the course. MIT has the curriculum which is hypothetical yet close to solving real-life problems.

Harvard University edX certification course

The courses offered by Harvard are recognized worldwide. The quality is equal to learning from the college itself. Data science certification from edX is affordable and has additional value for career growth as the content taught is updated. Data Science taught here is through machine learning and R language. These are two main programming languages through which data science is high on demand. Knowing programming basics helps in moving forward through the course easily. People without a programming background can pick up skills with more practice and consistency. The way of learning here is with probability or imagination. For more experience, the learner might need practice from different programming skills. This has 9 sub-courses with 8 weeks each to complete. The course enhances programming skills.

UC SanDiego edX certification

UC SanDiego offers a micro Master’s program in Data Science. The package consists of all kinds of data science in different fields taught by many teachers from different areas. Python, probability, Big data, statistics, and machine learning are the things through which data science is practiced. As the certification is equal to a Master’s program, the concepts go deep into the subjects. It allows learners to explore data science. It is highly suggested for those who are into the subjects and want to dive into large data. It is a lengthy course, taking more than a year. The validity of the certification stays long giving an extra level of educational qualification.

University of Michigan Python certification

The data science course offered by the University of Michigan is solely on Python. It is the easiest way to learn data science which became a trend among engineers. Knowing the basics of Python is crucial, as the course only has problems solving with Python. It is one of the easiest programming languages where many programmers enjoy coding. It might seem extra effort, but the process is smooth and less stressful compared to other data science courses. It helps in being an expert to become a data scientist in a specific area. It takes 5 months to complete the certification course with each sub course 7 weeks each.

Stanford University Coursera certification

Machine learning certification offered by Stanford University on Coursera is a highly advanced data science course. Baidu AI group, former head of Google Brain and Andrew NG founded this course to enhance the learning experience for learners. The concepts taught are based on machine learning. The industry is expected to go big in the future. Machine learning is a wide concept and covering every detail almost impossible. This course extends to making simple robots. Passionate young learners are seeking this course to make the most of the technology by living in any part of the world.

Choosing the best course

Choosing the best suitable course is entirely dependent on the learner. It is important to make time and spend money valuably. Most of the mentioned courses can be completed for free without certification. For those who want certification for high profile jobs need to make sure they are comfortable with the course. Certification needs completion of the course within the minimum required time. Each website has its own rules and regulations for certifications. Visit the main websites to know.