What Is Data Splitting in Learn and Test Data?

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Data is the fuel of every machine learning algorithmon which statistical inferences are made and predictions are done. Consequently, it is important to collect the data, clean it and use it with maximum efficacy. A decent data sampling can guarantee accurate predictions and drive the whole ML project forward whereas a bad data sampling can lead to incorrect predictions. Before diving into the sampling techniques, let us understand what the population is and how does it differ from a sample?

The population is the assortment or the collection of the components which shares a few of the other characteristics for all intents and purposes. The total number of observations is said to be the size of the population

The sample is a subset of the population. The process of  choosing a sample from a given set of the population is known as sampling. The number of components in the example is the sample size.

Data sampling refers to statistical approaches for picking observations from the domain to estimate a population parameter. Whereas data resampling refers to the drawing of repeated samples from the main or original source of data. It is the non-parametric procedure of statistical extrapolation. It produces unique sample distributions based on the original data and is used to improve the accuracy and intuitively measure the uncertainty of the population parameter.

Sampling methods can be divided into two parts:

1. Probability sampling procedure
2. Non-probability sampling procedure

The distinction between the two is that the example of determination depends on randomization. With randomization, each component persuades equivalent opportunity and is important for test for study.

Probability Sampling  It is a method in which each element of a given population has an equivalent chance of being selected.

• Simple random sampling –For instance, a classroom has 100 students and each student has an equal chance of getting selected as the class representative
• Systematic sampling- It is a sampling technique in which the first element is selected at random and others get selected based on a fixed sampling interval.

For instance, consider a population size of 20 (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19.20)

Suppose we want the element with the number 3 and a sample size of 5. The next selection will be made at an interval of 20/5 i.e. 4 so 3 + 4 = 7 so 3,7,11 and so on.

•  Stratified sampling – In this sampling process, the total group is subdivided into smaller groups, known as the stratato obtain a sampling process.

Assume that we need to identify the average number of votes in three different cities to elect a representative. City x has 1 million citizens, city y has 2 million citizens, city z has 3 million citizens. We can randomly choose a sample size of 60 for the entire population. But if you notice, the random samples are not balanced with respect to the different cities. Hence there could be an estimation error. To overcome this, we may choose a random sample of 10,20,30 from city x, y, z respectively. We can therefore minimize the total estimated error.

• Reservoir sampling is a randomized algorithm. It is used to select k out of n samples. The n is generally very large or unknown. For instance, reservoir sampling can be used to obtain k out of the number of fish in a lake.
• Cluster sampling - samples are taken as subgroup /clusters of the population. These subgroups are selected at random.

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Non-probability sampling  In a non-probability sampling method, each instance of a population does not have an equivalent chance of being selected. There is an element of risk of ending up with a non-representative sample which might not bring out a comprehensive outcome.

• Convenience sampling - This sampling technique includes people or samples that are easy to reach. Though it is the easiest methodology to collect a sample it runs a high risk of not being representative of a population.

For instance, consider a population size of 20 (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19.20)

The surveyor wants the person 4,7,11,18 to participate, hence it can create selection bias.

• Quota sampling – In Quota sampling methods the sample or the instances are chosen based on their traits or characteristics which matches with the population

For instanceconsider a population size of 20 (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19.20)

Consider a quota in multiple of 4 - (4,8,12,16,20)

• Judgement sampling - Also known as selective sampling. Here individuals are asked to participate.
• Snowball sampling - In this sampling technique, an individual element/person can nominate further elements/people known to them. It is only applicable when the sampling frame is difficult to identify.

A nominates P, P nominates G, G nominates M

A > P > G > M

The non-probability sampling technique may lead to selection bias and population misrepresentation.

• We often come across the case of an imbalanced dataset.
• Resampling is a technique used to overcome or to deal with  imbalanced datasets
• It includes removing samples/elements from the majority class i.e. undersampling
• Adding more instances from the minority class i.e. Oversampling

There is a dedicated library to tackle imbalanced datasets in Python - known as imblearnImblearn has multiple methods to handle undersampling and oversampling

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• Tomek Links for under-sampling - pairs of examples from opposite classes in close instances
• Majority elements are eliminated from the Tomek Links which intuitively provides a better understanding and decision boundary for ML classifier
• SMOTE for oversampling - Synthetic Minority Oversampling Technique - works by increasing new examples from the minority cases. It is a statistical technique of increasing or generating the number of instances in the dataset in a more balanced manner.

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• Pick a minority class as the input vector
• Discover its k closest neighbors (k_neighbors is indicated as a contention in the SMOTE())
• Pick one of these neighbors and spot a synthetic point anyplace on the line joining the point viable and its picked neighbor
• Rehash the above steps until it is adjusted or balanced
• Other must-read sampling methods - Near miss, cluster centroids for under sampling, ADASYN and bSMOTE for oversampling

Train-Test split

Python is bundled with overpowered ML library. The train_test_Split() module from Scikit-Learn library is one of the major python modules that provides a function to split the datasets into multiple subsets in different ways or let us say randomly into training and validation datasets. The parameter train_size takes a fraction between zero and one for specifying the training size. The remaining samples in the original data set are for testing purposes. The record which is selected for training and test sets are randomly sampled. The simplest method train_test_split() or the split_train_test() are more or less the same.

• train set – the subset of the dataset to train a model
• test set - the subset of the dataset to test the trained model

• The train-test method is used to measure the performance of ML algorithms
• It is appropriate to use this procedure when the dataset is very large
• For any supervised Machine learning algorithms, train-test split can be implemented.
• Involves taking the data set as a whole and further subdividing it into two subsets
• The training dataset is used to fit the model
• The test dataset serves as an input to the model
• The model predictions are made on the test data
• The output (prediction) is compared to the expected values
• The ultimate objective is to evaluate the performance of the said ML model against the new or unseen data.

A visual representation of training or test data:

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It is important to note that the test data adheres to the following conditions:

1. Be large enough to fetch statistically significant results

1. Is a representation of the whole dataset. One must not pick the test set with different traits/characteristics of the training set.

1. Never train on test data - don’t get fooled by good results and high accuracy. It might be the case that one has accidentally trained the model on the test data.

The train_test_split() is coupled with additional features:

• a random seed generator as random_state parameter – this ensures which samples go to training and which go to the test set
• It takes multiple data sets with the matching number of rows and splits them on similar indices.
• The train_test_split returns four variables
• train_X  - which covers X features of the training set.
• train_y – which contains the value of a response variable from the training set
• test_X – which includes X features of the test set
• test_y – which consists of values of the response variable for the test set.

• There is no exact rule to split the data by 80:20 or 70:30; it depends on the data and the target variable. Some of the data scientists use a range of 60% to 80% for training and the rest for testing the model.
• To find the length or the number of records we use len function of python > len(X_train), len (X_test)
• The model is built by using the training set and is tested using the test set
• X_train and y_train contain the independent features or variables and response variable values for training datasets respectively.
• On the other hand, X_test and y_test include the independent features and response variables values for the test dataset respectively.

Conclusion:

Sampling is an ongoing process of accumulating the information or the observations on an estimate of the population variable. We learnabout sampling types - probability sampling procedure and non-probability sampling procedure. Resampling is a repeated process to draw samples from the main data source. And finally, we learnt about training, testing and splitting the data which are used to measure the performance of the model. The training and testing of the model are done to understand the data discrepancies and develop a better understanding of the machine learning model.

Dipayan Ghatak

Project Manager

Leading Projects across geographies in Microsoft Consultant Services.

What is data analytics?In the world of IT, every small bit of data count; even information that looks like pure nonsense has its significance. So, how do we retrieve the significance from this data? This is where Data Science and analytics comes into the picture.  Data Analytics is a process where data is inspected, transformed and interpreted to discover some useful bits of information from all the noise and make decisions accordingly. It forms the entire basis of the social media industry and finds a lot of use in IT, finance, hospitality and even social sciences. The scope in data analytics is nearly endless since all facets of life deal with the storage, processing and interpretation of data.Why data analytics? Data Analytics in this Information Age has nearly endless opportunities since literally everything in this era hinges on the importance of proper processing and data analysis. The insights from any data are crucial for any business. 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It needs to be renewed after three years.The application process is in itself quite complex, and it also involves signing the CAP Code of Ethics before one is given the certification. The CAP panel reviews each application, and those who pass this review are the only ones who can give the exam.  Prerequisite: A bachelor’s degree with 5 years of professional experience or a master's degree with 3 years of professional experience.  Exam Fee & Format: The base price is $695. For individuals who are members of INFORMS the price is$495. (Source) The pass percentage is 70%. The format is a four option MCQ paper. Salary: $76808 per year (Source) 2. Cloudera Certified Associate (CCA) Data Analyst Cloudera has a well-earned reputation in the IT sector, and its Associate Data analyst certification can help bolster the resume of Business intelligence specialists, system architects, data analysts, database administrators as well as developers. It has a specific focus on SQL developers who aim to show their proficiency on the platform.This certificate validates an applicant's ability to operate in a CDH environment by Cloudera using Impala and Hive tools. One doesn't need to turn to expensive tuitions and academies as Cloudera offers an Analyst Training course with almost the same objectives as the exam, leaving one with a good grasp of the fundamentals. Prerequisites: basic knowledge of SQL and Linux Command line Exam Fee & Format: The cost of the exam is$295 (Source), The test is a performance-based test containing 8-12 questions to be completed in a proctored environment under 129 minutes.  Expected Salary: You can earn the job title of Cloudera Data Analyst that pays up to $113,286 per year. (Source)3. Associate Certified Analytics Professional (aCAP)aCAP is an entry-level certification for Analytics professionals with lesser experience but effective knowledge, which helps in real-life situations. It is for those candidates who have a master’s degree in a field related to data analytics. It is one of the few vendor-neutral certifications on the list and must be converted to CAP within 6 years, so it offers a good opportunity for those with a long term path in a Data Analytics career. It also needs to be renewed every three years, like the CAP certification. Like its professional counterpart, aCAP helps a candidate step out in a vendor-neutral manner and drastically increases their professional credibility. Prerequisite: Master’s degree in any discipline related to data Analytics. Exam Fee: The base price is$300. For individuals who are members of INFORMS the price is $200. (Source). There is an extensive syllabus which covers: i. Business Problem Framing, ii. Analytics Problem Framing, iii. Data, iv. Methodology Selection, v. Model Building, vi. Deployment, vii. Lifecycle Management of the Analytics process, problem-solving, data science and visualisation and much more.4. SAS Certified Data Analyst (Using SAS9)From one of the pioneers in IT and Statistics - the SAS Institute of Data Management - a SAS Certified Data Scientist can gain insights and analyse various aspects of data from businesses using tools like the SAS software and other open-source methodology. It also validates competency in using complex machine learning models and inferring results to interpret future business strategy and release models using the SAS environment. SAS Academy for Data Science is a viable institute for those who want to receive proper training for the exam and use this as a basis for their career. Prerequisites: To earn this credential, one needs to pass 5 exams, two from the SAS Certified Big Data Professional credential and three exams from the SAS Certified Advanced Analytics Professional Credential. Exam Fee: The cost for each exam is$180. (Source) An exception is Predictive Modelling using the SAS Enterprise Miner, costing $250, This exam can be taken in the English language. One can join the SAS Academy for Data Science and also take a practice exam beforehand. Salary: You can get a job as a SAS Data Analyst that pays up to$90,000 per year! (Source) 5. IBM Data Science Professional CertificateWhenever someone studies the history of a computer, IBM (International Business Machines) is the first brand that comes up. IBM is still alive and kicking, now having forayed into and becoming a major player in the Big Data segment. The IBM Data Science Professional certificate is one of the beginner-level certificates if you want to sink your hands into the world of data analysis. It shows a candidate's skills in various topics pertaining to data sciences, including various open-source tools, Python databases, SWL, data visualisation, and data methodologies.  One needs to complete nine courses to earn the certificate. It takes around three months if one works twelve hours per week. It also involves the completion of various hands-on assignments and building a portfolio. A candidate earns the Professional certificate from Coursera and a badge from IBM that recognises a candidate's proficiency in the area. Prerequisites: It is the optimal course for freshers since it requires no requisite programming knowledge or proficiency in Analytics. Exam Fee: It costs $39 per month (Source) to access the course materials and the certificate. The course is handled by the Coursera organisation. Expected Salary: This certification can earn you the title of IBM Data Scientist and help you earn a salary of$134,846 per annum. (Source) 6. Microsoft Certified Azure Data Scientist AssociateIt's one of the most well-known certifications for newcomers to step into the field of Big Data and Data analytics. This credential is offered by the leader in the industry, Microsoft Azure. This credential validates a candidate's ability to work with Microsoft Azure developing environment and proficiency in analysing big data, preparing data for the modelling process, and then progressing to designing models. One advantage of this credential is that it has no expiry date and does not need renewal; it also authorises the candidate’s extensive knowledge in predictive Analytics. Prerequisites: knowledge and experience in data science and using Azure Machine Learning and Azure Databricks. Exam Fee: It costs $165 to (Source) register for the exam. One advantage is that there is no need to attend proxy institutions to prepare for this exam, as Microsoft offers free training materials as well as an instructor-led course that is paid. There is a comprehensive collection of resources available to a candidate. Expected Salary: The job title typically offered is Microsoft Data Scientist and it typically fetches a yearly pay of$130,993.(Source) Why be a Data Analytics professional? For those already working in the field of data, being a Data Analyst is one of the most viable options. The salary of a data analyst ranges from $65,000 to$85,000 depending on number of years of experience. This lucrative salary makes it worth the investment to get a certification and advance your skills to the next level so that you can work for multinational companies by interpreting and organising data and using this analysis to accelerate businesses. These certificates demonstrate that you have the required knowledge needed to operate data models of the volumes needed by big organizations. 1. 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It has been consistently rated as being among the hottest careers of the decade and allows professionals to have a long and satisfying career. Companies Hiring Certified Data Analytics Professionals Oracle A California based brand, Oracle is a software company that is most famous for its data solutions. With over 130000 employees and a revenue of 39 billion, it is surely one of the bigger players in Data Analytics. MicroStrategy Unlike its name, this company is anything but micro, with more than 400 million worth of revenue. It provides a suite of analytical products along with business mobility solutions. It is a key player in the mobile space, working natively with Android and iOS. SAS One of the companies in the list which provides certifications and is also without a doubt one of the largest names in the field of Big Data, machine learning and Data Analytics, is SAS. The name SAS is derived from Statistical Analysis System. This company is trusted and has a solid reputation. 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! 5631 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?

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