25 Top Machine Learning Questions And Answers [Updated 2020]

Ever since the emergence of modern technologies such as Artificial Intelligence, Machine Learning, and Data Science, organizations are looking to adopt them to enhance their customer service. The development in these technologies has given rise to new job roles in the IT market that we haven’t heard them a few years back.

Now, most of the organizations are willing to hire professionals such as Data Scientists, Machine Learning Engineer, and Artificial Intelligence, etc. into their team. Looking at these opportunities, the number of people who want to build their career in these technologies is also increasing every year. So, to help such aspiring people, through this blog we presenting a list of few important Machine Learning questions and answers that we believe that it will be great helpful to you.

Below is the list of top questions and answers pertaining to the Machine Learning field. But before proceeding further, at this point we recommend you to first go through this free video course on Machine Learning that helps you to learn some aspect related to this technology.

1) What are the different types of Machine Learning algorithms?

Ans: Machine Learning algorithms can be divided into different categories according to their purposes. The main categories of Machine Learning algorithms are:

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning.

Supervised Learning: It is a type of ML algorithm in which you train the machine using data which is well labeled. These algorithms are the ones that involve the direct supervision of the operation. Here, the developer labels some data and sets strict boundaries upon which the algorithm operates.

In this type, human experts act as a teacher where they feed the training data to the computer containing the input/predictors and we show it the right answer and from that data, the computer should be able to learn the patterns.    

The most commonly supervised algorithms are:

Unsupervised Learning: Here, the computer is trained with unlabeled data. Here you need do not supervise the model. Instead, in this type of ML algorithm, you need to allow the model to work on its own to discover information.

The most commonly used unsupervised algorithms are:

  • K-means clustering
  • t-SNE
  • PCA.

Semi-supervised Learning: Semi-supervised algorithms represent a middle ground between supervised learning and unsupervised algorithms. These algorithms use a limited set of labeled sample data to shape the requirements of the operation.

Reinforcement Learning: It is one of the types of ML algorithms. Using this type of algorithm, the machine is trained to make specific decisions. In this type of learning, the machine is exposed to an environment in which it trains itself continuously using trial and error method. This machine learns from past experiences and tries to capture the best possible knowledge to make accurate business decisions.

The most commonly used reinforcement learning are:

  • Q-Learning
  • Temporal Difference
  • Monte-Carlo Tree Search.

2) What are different models in Machine Learning?

Ans: Different models in Machine Learning are:

  • Decision Tree based methods
  • Linear regression based methods
  • Neural Network
  • Bayesian Network
  • Support Vector Machine
  • Nearest Neighbor.

3) Which are the best Machine Learning tools?

Ans: Machine Learning tools are Artificial Intelligence-algorithmic applications that provide systems with the ability to understand and improve without considerable human input.

Some of the popular Machine Learning tools are as follows:

  • TensorFlow
  • Keras
  • Scikit-learn
  • Caffe2
  • Apache Spark MLib.

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TensorFlow: This is a Machine Learning tool developed by Google. This tool allows you to create your own libraries. This tool is used by some noted companies such as eBay, Twitter, Dropbox and many more.

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Keras: Keras is one of the high level Deep Learning frameworks. It is popular because of the user interface, ease of extensibility and modularity.

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Scikit-learn: This is an open source Machine Learning tool that was released in 2007. Python is a scripting language of this framework. It includes several models of Machine Learning such as classification, regression, clustering etc.

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Caffe2: This is an open source tool developed by Facebook. This tool provide several options for users to organize computation with the library that can be installed and run on a desktop, in the cloud, or at a data center.

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Apache Spark MLib: This is a scalable Machine Learning library. It is easy to use. This tool includes algorithms for regression, collaborative filters, clustering, decision trees and many more.

4) What are best Machine Learning libraries?

Ans: Some of the best Machine Learning libraries are:

  • Numpy
  • Pandas
  • Scikit Learn
  • StatsModels
  • NLTK
  • TensorFlow
  • PyTorch.

5) What is Machine Learning in layman’s terms?

Ans: Machine Learning is a subset of Artificial Intelligence. It is a technology that enables the systems to automatically learn and improve from past experience without being explicitly programmed. It involves a process wherein instead of writing the program or code to provide instructions to the computer to what to do, you need to provide a set of data, based upon which machines build their own logic and provide a better solution.

Machine Learning is the technology behind most of the modern applications. For example, this technology is responsible for recommending products to people while they are shopping online or recommending you the movies that you can watch on platforms like Netflix.

6) Which are the best online courses for Machine Learning?

Ans: As Machine Learning continues to show its impact on various sector of the world, it has also resulted in growth in number of job openings for professionals working in this field. Today, Machine Learning experts are in huge demand. Many professionals working in other sectors are now looking towards this technology to build their career in it. Even many fresh graduates are also willing to learn and master this technology to be called as a Machine Learning professional.

So, to help all such aspiring people, today several online learning platforms are available in market offering Machine Learning courses. One such wonderful platform to learn Machine Learning is Simpliv. This online learning platform offers hands-on training courses, covering a large variety of topics on Machine Learning. These courses are taught by well-known industry experts.

Each of the courses provided in this platform has been designed to help the students to understand the concept easily. All the courses are self-paced, online and have access on Android and iOS mobile. You can read this blog to check the entire list of Machine Learning courses offered by Simpliv.

7) What are the prerequisites to start Machine Learning?

Ans: Whether you are a beginner or you are an experienced professional, you should know some prerequisites for Machine Learning. Having knowledge of following concepts will help you to understand Machine Learning easily.

Following are the prerequisites you should know:

  • Statistics
  • Linear Algebra
  • Calculus
  • Probability
  • Knowledge of any of the programming languages such as Python and R.

8) How does Google use Machine Learning?

Ans: “Machine Learning is the core transformative way by which we are rethinking how we are doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we are in the early days, but you will see us – in a systematic way -apply Machine Learning in all these areas” – Sunder Pichai, Google CEO.

Google uses Machine Learning algorithms to provides its customers with a valuable and personalized experience. We can see Machine Learning technology has been embedded in many of its services like Gmail, Search, Maps etc.

  • 1) Gmail: Gmail uses Machine Learning technology to separate social, promotional and primary mails and labels them separately.
  • 2) Google search: Machine Learning plays a very important in suggesting Google users the related search terms for the keyword they type in. Google knows everything and when you start typing in the search box it automatically anticipates what its users might be looking for and provides suggested search terms. These suggestions are showcased based upon of past searches, trends, or from your present location.
  • 3) Google Assistant: Google Assistant helps its users easily search for almost everything they want. For example, it will help to search nearest restaurant, and provide you the information about the nearest theatre etc. It uses Machine Learning algorithms and provide these benefits.

9) Where I can find research papers on Machine Learning?

Ans: Over the last few years, Machine Learning technology is emerging as one of the powerful technologies of this modern world. This technology has witnessed many amazing advancement and the research papers provides us all the information about the recent happenings in the field. You can find some of the research papers published on this technology here in the below URL’s.

1) Top 20 Recent Research Papers on Machine Learning and Deep Learning

2) 5 LATEST RESEARCH PAPERS ON ML YOU MUST READ IN 2019

3) MACHINE LEARNING IEEE PAPER 2018.

10) What skills are needed for Machine Learning jobs?

Ans: Organizations across the world are relying on Machine Learning technology to increase their work efficiency and thus provide a better service to their customers. Looking at the growth of this technology, many people are willing are build their career as  Machine Learning professionals.

Some of the skills required to get Machine Learning jobs are as follows:

1) Basic skills:

2) Machine Learning Languages:

3) Machine Learning Algorithms

3) Soft skills:

 Learn Soft Skills such as:

  • Good communication skills
  • To be a good team member
  • Domain knowledge
  • Problem solving skills.

11) How should you start a career in Machine Learning?

Ans: Machine Learning is the technology that teaches the computers how to learn from data to make decisions or predictions. In order to start your career in Machine Learning and become a Machine Learning professionals, you need to follow a systematic approach that helps to achieve your goal.

Some of the steps that help the students to start their career in Machine Learning are:

1) Understand the basic concepts: Good understanding of fundamental concepts is very important in order to become successful Machine Learning expert. You understand the concepts such as Machine Learning, Artificial Intelligence, Big Data, Data Science etc.

2) Learn Mathematics: Having the knowledge of Mathematics is very important. You should know some concepts such as Linear Algebra, Probability theory and statistics, Multivariable calculus, Algorithm and optimization etc.

3) Learn programming skills:

Programming languages such as Java, Python, R are play a very important role while building Machine Learning projects. Machine Learning professionals must know any one of the programming languages.

4) Complete an Exploratory Data Analysis Project:

It is very important to make a systematic study of data and understand the hidden information from it and prepare an exploratory Data Analysis project.

5) Understand Big Data technologies:

Understanding Big Data technologies is very important as you need to analyze huge amount of data available from different sources. You need to have knowledge of how large amount of data can be stored, accessed and processed efficiently to derive meaningful information from it.  

6) Learn Deep Learning models:

Having the knowledge of some of the concepts of Deep Learning models such as Artificial Intelligence and Natural Language Processing is very important to efficiently work on Machine Learning projects. So, it is recommended for professionals to have a good understanding of Deep Learning technology.

7) Build your own Machine Learning project:

When you learn all the necessary skills,  you should start working on a Machine Learning project that helps to showcase your skills to others. Apply all the knowledge you have acquired on Machine Learning and the start building your project that further raises your knowledge to  new heights.

8) Learn Soft skills:

You need to learn some soft skills to be able to successfully present your skills set in front of the interviewer and also to work efficiently in the organization. Machine Learning professionals need to have good communication skills, must develop problem solving ability and should be able to work efficiently as  team members to achieve success in their career.

You can learn the various Machine Learning concepts through various ways. One such way is to subscribe to this online learning platform such as Simpliv

12) Where can I find jobs in Machine Learning field?

Ans: Once you have decided to make a career in Machine Learning, then it is very important to search jobs in this field in the right direction. There are certain job portal websites that show you the relevant job based upon your interest and experience.

Some of the noted job portal websites that help to search jobs in Machine Learning field are:

Apart from the above mentioned websites, you can also find Machine Learning jobs through various other platforms mentioned below:

  • Company websites.
  • Professional network
  • Social Media
  • Conferences, webinars, seminars
  • Email.

13) What is Data Science, Big Data and Machine Learning?

Ans: Data Science, Big Data and Machine Learning are the three important technologies are emerging in the present IT industry. All these technologies, in combination with each other, are making a huge impact on various sectors of the world and are revolutionizing them.

  • Data Science: Data Science is a broad field. It lies at the intersection of Math, Statistics, Artificial Intelligence, Software Engineering, etc. This field deals with data collection, cleaning, analysis, visualization, designing experimentation and many more. The aim of Data Science is to help derive insights from huge amount of data available from various sources.
  • Big Data: The amount of Data collected by companies can sometimes be so large that it creates a large set of challenges regarding data acquisition, storage, analysis, and visualization. To address these challenges, Big Data is used.

Big Data takes care of these large sets of data, different types of data types and the velocity at which the data must be processed. It is the process in which we collect and analyze the large volume of data sets that helps to discover useful hidden patterns and other information that benefits the organization in making their business decisions.

  • Machine Learning: Machine Learning is a subset of Artificial Intelligence. It is the process of teaching computers without the intervention of human beings. This technology helps the computers learn automatically without human intervention.
  • Data Science and Big Data: Many companies use Big Data to improve their efficiencies, understand the present market trends whereas Data Science provides the mechanism to understand and utilize the Big Data in a useful way.

Big Data Refers to technology (Hadoop, Java etc.), distributed computing, analytics tools, software, etc, whereas Data Science focuses on extracting useful information from available data, preparing strategies for business decisions, etc.

  • Big Data And Machine Learning:

Big Data basically means storage of large volume of data and finding out pattern in data. Machine Learning means to allow the computer to learn themselves automatically.

To summarize, Data Science is an interdisciplinary field which aims to derive meaningful insights from data. Machine Learning is a branch of Artificial Intelligence that teaches the machines the ability to learn, without being explicitly programmed. Big Data is basically the process of analyses of Big Data by discovering hidden patterns or extracting some information from it.

14) What is Regression in Machine Learning?

Ans: Regression is a Machine Learning algorithm. It is used to predict a continuous value. For example, Regression can be used to predict the price of house given  some of the features like size, price, etc. Regression is basically a statistical approach to find the relationship between variables.

There are various types of regression such as:

1) Simple Linear Regression

2) Polynomial Vector Regression

3) Support Vector Regression

4) Decision Tree Regression

5) Random Forest Regression.

15) What are different models in Machine Learning?

Ans: Different types of Machine Learning models are:

16) What is Natural Language Processing?

Ans: Natural Language Processing (NLP) is branch of Artificial Intelligence. It is the ability of a computer program to understand human language as it is spoken. This technology deals with the interaction between computers and humans using the natural language.

The aim of this technology is to read, understand and make sense of the human languages in a manner that is valuable.

17) What is reinforcement learning?

Reinforcement learning is a branch of Artificial Intelligence. It can be defined as the training of Machine Learning models to make a sequence of decisions. This technology allows the machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance.

Reinforcement is widely used in building Artificial Intelligence for playing computer games. This technology is also used for text summarization engines.

18) What is Batch in Machine Learning?

Ans: Batch is a term used in Machine Learning. Batch refers to the number of samples to work through before updating the internal model parameters. It is the number of training examples utilized in one iteration.

19) Where do I start learning Machine Learning?

Ans: Looking at the growth of Machine Learning technology, many professionals are willing to learn Machine Learning technology. Following are some guidelines that help to learn Machine Learning easily and become a Machine Learning expert.

1. Understand what Machine Learning is.

2. Develop interest in this field.

3. Analyze your current skills set and know what else you need to learn

4. Learn any of the one of the programming language such as Python, Java, R etc.

5. Learn Machine Learning libraries such as Numpy, Pandas etc.

6. Take help of online education platforms such as Simpliv.

7. Look into YouTube tutorials such as Artificial Intelligence and Data Visualization With Tableau

8. Read books Machine Learning technology written by well-known authors such as Machine Learning

9. Participate in Machine Learning forums like Kaggle, reddit.com.

10. Learn soft skills such as good communication, team work, etc.

Once you gain sufficient knowledge about Machine Learning,  start building your own project and then next start applying for jobs in various job portal websites.

20) How to choose a Machine Learning algorithm?

Ans: Choosing the right algorithm for your project is very important. To  do this, you need to consider several criteria mentioned below:

1) Categorize the problem.

2) Understand the data properly.

3) List all the available algorithms.

4) Now implement Machine Learning algorithms.

21) What Python libraries do you use in Machine Learning?

Ans: The following are some of the Python libraries used in Machine Learning.

  • NumPy
  • SciPy
  • Pandas
  • Matplotlib
  • Plotly
  • Scikit-Learn
  • Theano
  • TensorFlow
  • Keras

22. What are the top Machine Learning frameworks?

Ans: There are plenty of frameworks available that helps to build Machine Learning projects. Some of the top Machine Learning frameworks are:

  • Scikit-Learn: It is a free Machine Learning framework. It includes many Machine Learning algorithms like Linear Regression, Logistic regression, K-mean algorithm, Support vector machine. 
  • TensorFlow: TensorFLow is created by Google. It is also an open-source library which is general used for Deep Learning or Machine Learning algorithms using neural networks.
  • Amazon Machine Learning: This framework provides visualization tools and wizards that guide you through the process of creating Machine Learning (ML) models without having to learn complex ML algorithms and technology.
  • Azure ML studio: This framework is developed by Microsoft. It allows the users to create and train models, and then turn them into API’s that can be consumed by other services.
  • Torch: This framework supports various Machine Learning utilities and algorithms. It has community driven packages in Machine Learning, computer vision, image processing, Deep Learning etc.
  • Theano: This framework is built in Python. It allows to define, create and optimize mathematical calculations.

23. Which is the best software to create AI in 2019.

Ans: The following are the best software to create AI in 2019.

  • Keras
  • Microsoft CNTK
  • Caffe
  • Torch
  • MLPack.

24. Is Machine Learning is a subset of Data Science?

Ans: Data Science is a broader filed comprising of statistics, programming, data visualization, Big Data, Machine Learning etc. It refers to analysis of data to get  meaningful information.

Machine Learning is a subset of Data Science. It is a process where Machines learn to perform tasks that they aren’t specifically programmed to do. Machine Learning helps Data Science by making provision for Data Analysis, Data Preparation and even Decision Making like real time testing, online learning, etc.

To be precise, Data Science is a broad concept covering every aspect of Data processing and Machine Learning is a part of Data Science.

25. How much maths do you use in Machine Learning work?

Ans: Knowledge of mathematics is very important in order to build Machine Learning projects. While implementing such projects, it becomes necessary for Machine Learning developers to proceed with some research in mathematical and theoretical advancement.

The following mathematics concepts are used in various Machine Leaning projects based upon requirements:

  • Linear algebra
  • Probability Theory and Statistics
  • Calculus
  • Algorithms and Complex Optimizations
  • Discrete mathematics.

Conclusion:

With the growth of this technology, the demand for professionals working in this field is increasing continuously in the IT market. It is very important for professionals to continuously keep upgrading their skills set with all the new happening in this technology. We hope this blog serves its purpose of providing you a good list of questions and answers related to the Machine Learning field.

Now we request you to kindly share this blog into your social network so that someone looking for a similar kind of information gets benefitted. If you feel something more needs to be added into the above list then please send your thoughts in the comment section that will help us a lot.

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