The growth of Machine Leaning technology and its impact on various sectors of this world have changed the way the business functions today. Many organizations have started adopting this technology in order to serve their customers better and that has given rise to an increase in the number of job opportunities for professionals working in this arena.
Looking at the vast number of available job opportunities, many people are considering building their career in this field. So, to help all such people, in this blog we have discussed some top Machine Learning questions and answers that we believe can be of great help to them.
Below is the list of top questions and answers related to the Machine Learning field. But before proceeding further at this point we would like to introduce you to video courses on Machine Learning that helps you to understand this technology in a better way. In addition to this, we also suggest that our readers read this blog to find one more list of some valuable questions and answers on Machine Learning.
1) How to learn Deep Learning quickly?
Ans: Deep Learning is a subset of Machine Learning. It is an Artificial Intelligence function that imitates the working of the human brain in processing data and creating patterns for use in decision making.
There are various ways to learn Deep Learning. These are some of them:
- You can learn Deep Learning courses through the online courses provided by Simpliv
- Learn Deep Learning With Transformers
- Learn Deep Learning & Neural Networks Python – Keras : For Dummies
- With the help of free YouTube videos
- Reading blogs published by industry experts. You can find one such blog here
- Reading books on Deep Learning.
Among these, select a method that you find suitable. Then, simply start working on a project that gives you a grasp of the concepts, so that you can easily become a Deep Learning professional.
2) What are some of the applications of Deep Learning?
Ans: As we have seen, Deep Learning is a subset of Machine Learning. With its unique features this technology is showing its impact on various technologies of this world.
Following are some of the applications of Deep Learning technology.
- Speech recognition: Speech recognition is a process of recognizing speech within audio and converting it into text. Deep Learning technology enables the device to perform this task.
- Image recognition: This is one of the important features of Deep Learning. Image retrieval and classification are very important if the app uses images. This technology is used to sort images into different categories or using auto-encoders to retrieve images based on visual similarity.
- Recommendation engines: Some of theapplications such as Netflix, Amazon are using recommendation engines to enhance user experience and provide good service to their customers. These recommendation engines rely on Deep Learning technology to work efficiently.
- Chatbots: The primary aim of chatbots is to provide the best response of any query that it receives. These chatbots use Deep Learning technology to work efficiently.
- Marketing research: Deep Learning technology can also be used by organizations in their marketing department. Market segmentation, marketing campaign analysis can be improved using this technology.
- Translation: Google Translate app has the ability to transfer images with text in real-time to a language of your choice. They use Deep Learning technology to perform this task.
3) What are the best books for Machine Learning algorithms?
Ans: There are many books available to learn Machine Learning algorithms. Below is the list of some of the best well-known books:
2. Introduction to Machine Learning
3. Pattern Recognition and Machine Learning
4. Introduction to Machine Learning with Python: A Guide for Data Scientists
5. Machine Learning: A Technical Approach To Machine Learning for Beginners.
4) What’s the future in learning Big Data?
Ans: Big Data market is growingly continuously and already many companies consider using Big Data technology as their top priority. With the huge availability of data through various ways, it has become a challenging factor for most organizations to analyze it properly to extract some meaningful information from it.
Professionals who are learning Big Data get bigger benefits in the coming days. Let us see some of them.
1. Increase in the demand for Big Data professionals:
(Image source: aditiconsulting)
Professionals working in the Big Data field are in high demand as many organizations are looking to properly analyze the huge amount of data available to them to derive meaningful information. Job portal websites such as indeed.com show that there is a huge number of job opportunities available for such professionals.
2. Higher salary: As the gap between demand and availability of Big Data professionals is increasing, there has been a resulting increase in the salary of such professionals. In the present IT market, these professionals are among the highest paid employees.
3. Big Data Analytics: A highest priority for most of the companies: Today, for most companies, Big Data Analytics has become the highest priority. Many companies accept that Big Data is the single major way to get a competitive advantage.
4. A range of choices: Professionals working in the field of Big Data have a variety of jobs to choose from. Some of the available job titles for Big Data professionals are:
1) Big Data Analytics Business Consultant.
2) Big Data Analytics Architect.
3) Big Data Engineer.
4) Big Data Solution Architect.
5) Big Data Analyst.
5) What are prerequisites for learning Hadoop and Big Data?
Ans: The prerequisite knowledge required for learning Hadoop and Big Data includes:
- Linux Operating system: Knowledge of Linux Operating System is important for anyone willing to learn Hadoop and Big Data. You need to have basic knowledge of working Linux desktop, Linux commands, etc.
- Programming skills: To understand the Hadoop programming efficiently, you need to have prior knowledge of any of the programming languages such as:
- Java
- Learn Java Servlets And JSP – Build Java EE(JEE) App In 25 Steps
- Learn Spring Boot Microservices With JPA
- Python
- Scala
Hadoop Eco-system component: Understand the Hadoop Eco-system components such as HDFS, MapReduce, Hive, HBase, etc.
Structure Query Language: Understanding SQL queries is very important to learn Hadoop technologies.
6) What are some tools you can use for reinforcement learning?
Ans: Reinforcement Learning can be understood as one of the Machine Learning methods which is concerned with how software agents should take actions in an environment. It is all about algorithms tracking previous actions or behavior and providing optimized decisions using trial and error principle.
Top 5 tools that can be used for reinforcement learning are as follows:
- Open AI Gym: It is one of the most popular environments used for developing and comparing reinforcement learning models. This environment also offers APIs that facilitate feeding observations along with rewards back to agents.
- TensorFlow: TensorFlow is a well-known open-source library developed by Google. An extended version of this library, called TensorLayer, has been developed lately. This version provides popular RL modules that can be easily customized and assembled for tracking real-world Machine Learning challenges.
- Keras is one of the tool used in Reinforcement Learning that presents simplicity in implementing neural networks with just a few lines of codes with faster execution.
- DeepMind Lab: DeepMind Lab is one of the Reinforcement Learning tools which is highly customizable and extendable.
- PyTorch: PyTorch has been built to provide flexibility as a Deep Learning development library. Some of the advantages of using this tool are its multiGPU support, custom data loaders, and simplified preprocessors.
7) What is Logistic Regression?
Ans: Logistic Regression is one of the statistical methods. This method is used for analyzing a dataset in which there are one or more independent variables that determine an outcome. It is one of the popular Machine Learning algorithms.
- Logistic Regression is used in various fields such as Healthcare, Social Sciences & Machine Learning, etc.
Different types of Logistics Regression:
1. Binary Logistic Regression.
2. Multinomial Logistic Regression.
3. Ordinal Logistic Regression.
8) What is Classification and Regression in Machine Learning?
Ans: Classification and Regression are different types of Supervised Machine Learning models. Both types share the same concept of utilizing known datasets to make predictions.
The below diagram shows the different groupings of Machine Learning.
| Classification | Regression |
| 1) This type of algorithm estimates the mapping function (f) from input variables from input variables (x) to discrete or categorical output variables (y). | 1) On the other hand, Regression algorithm estimates the mapping function (f) from the input variables (x) to numerical or continuous output variables (y). In this scenario, y is a real value, which can be an integer or a floating point value. |
| 2) The task of predicting a discrete class label is known as classification. | 2) The task of predicting a continuous quantity is known as Regression |
9) Who is currently leading in the Artificial Intelligence race?
Ans: Artificial Intelligence is growing continuously and is showing its impact on various industries. Today we can see most of the countries are investing in this technology to undertake their development works.
These are some companies that are leading in the race of Artificial Intelligence.
1. China: China has recently declared to become a $150 billion AI global leader by 2030. This country has published more number of research papers than any other country.
2. USA: USA is giving tough competition to China in becoming an AI superpower country. The country has benefited with $ 10 billion venture capital channeling in direction of AI.
3. United Kingdom: In Europe,theUK is emerging as the leader of AI. The UK government has also announced huge funds to support robotics and AI research projects.
Apart from these countries, many other countries such as India, Germany, Russia, France, and Sweden are also investing in this technology as they understand that AI technology has the power to boost competitiveness, increase productivity and help them carry out their development works at a faster rate.
As many countries want to achieve the highest success in this field, but among all of them, China is moving at a faster rate to get maximum benefits from this technology.
10) What is Gradient descent algorithms?
Ans: Gradient descent is one of the most popular and widely used algorithms for training Machine Learning algorithms. It is an optimization algorithm that is used to find the values of parameters of a function (f) that minimize the cost function (cost).
Some of the variants of Gradient descent algorithms are:
- Batch Gradient descent: Batch Gradient descent calculates the error for each example within the training dataset, but only after all training examples have been evaluated does the model gets updated.
- Stochastic Gradient Descent: This algorithm, instead of going through all examples, performs the parameters update on each example.
- Mini-Batch Gradient Descent: Mini-Batch Gradient Descent is the combination of the concepts of Stochastic Gradient Method and Batch Gradient Descent. Instead of going over all examples, it sums up a lower number of examples based on the batch size.
11) Which is the most popular used Machine Learning Algorithms?
Ans: Various Machine Learning algorithms are being used depending on the requirement. Some of the most popularly used Machine Learning algorithms are:
Type: Supervised Machine Learning
- Decision Trees
- Learn Machine Learning Advanced: Decision Trees In R
- Learn Machine Learning Advanced: Decision Trees In Python
- Random Forest
- Logistic Regression
- Linear Regression.
- Support Vector Machine
- Learn Machine Learning Adv: Support Vector Machines (SVM) In R
- Learn Machine Learning Adv: Support Vector Machines (SVM) Python
Type: Unsupervised Machine Learning
- PCA
- t-SNE
- K-Means Clustering.
Type: Reinforcement Machine Learning
- Q-Learning
- Temporal Difference
- Monte-Carlo Tree Search
Type: Semi-Supervised Machine Learning
Along with the above mentioned list, many other Machine Learning algorithms exist. No Single Machine Learning is best for Machine Learning problem. So, as per the need, these different Machine Learning algorithms are used.
12) Why are algorithms are important for Machine Learning?
Ans: Machine Learning algorithms are a collection of decisions that are taken in order to reach a particular target. Using Machine Learning technology, we make machines to perform a specific task. Here we provide set of instructions (algorithms) to the machines, using which they will be able to carry out that task. With the help of appropriate algorithms, machines will be able to learn faster and efficiently and thus be able to perform better. So, algorithms become very important for Machine Learning.
13) What is the difference between Machine Learning, AI and NLP?
Ans: Artificial Intelligence, Machine Learning and Natural Language Processing are the three technologies that are being adopted by most of the organizations in order to provide a better service to their customers. All the three technologies have a huge impact on various industries across the world. Let us understand what these technologies are and the major differences exist between them.
| Artificial Intelligence | Machine Learning | Natural Language Processing |
| 1) Artificial Intelligence is a broader concept. 2) It is defined as the branch of computer science that emphasizes the creation of intelligent machines that work, operate and react like human beings. This technology can be described as a machine’s capacity to work on tasks that would previously need the attention of human intelligence. | 1) Machine Learning is a subset of Artificial Intelligence. 2) It enables the machines to make decisions based on past data. It is a scientific approach to solve certain tasks using various algorithms and predictions. | 1) Natural Language Processing is a component of Artificial Intelligence. 2) It enables computers to understand, perform an action and interact with human using their language. It is process of explaining a structure or a command to a machine in the natural language as used by humans; translating it into a format which the machine will be able to understand and process it and generate it back to the user. |
14) Is Machine Learning used in video games?
Ans: Machine Learning is one of the powerful technologies of the present IT industry. It is way of achieving Artificial Intelligence. This technology is revolutionizing the many industries in the world and also has made an impact on the gaming industry as well, especially when it comes to the way the video games are developed.
The advantage of using Machine Learning in gaming industry is that it has the ability to respond to a player’s action dynamically. A video game developed with this technology could react and change how the world, non-player characters (NPC’s) or objects behave in real-time, based on player’s actions and decision.
15) What are best books for learning games in Artificial Intelligence?
Ans: Some of the best books for learning games in Artificial Intelligence are:
- Programming Game by Example – written by Mat Buckland
- Artificial Intelligence for Games – written by Ian Millington
- Introduction to Game AI – written by Neil Kirby
- AI Game Programming for Game AI
- Practical Game AI Programming
- Learning game AI Programming with Lua.
16) Are bots in games powered by Machine Learning?
Ans: Now bots powered by Machine Learning technology are being used to develop games. Deep Learning technology, a subset of Machine Learning, is making a strong impact with labs such as DeepMind, and its comparator OpenAI in developing video games that have the ability to beat professional players.
17) How will Machine Learning impact the gaming industry?
Ans: Today, gaming has grown into a large global industry of over a hundred billion dollars. Many companies which are into game development are relying on technologies such as Artificial Intelligence and Machine Learning to develop games. These technologies have contributed a lot in making games smarter and more interactive.
Some of the ways how Machine Learning and Artificial Intelligence are impacting the gaming industry are as follows:
- Making games more realistic: Today gamers don’t want just good graphics; they want the games to resemble reality. So, Machine Learning allows the developers to develop games that are more realistic.
- Improving the overall gaming experience: Machine Learning is playing an important role in improving the overall gaming experience. It makes the playing experience better by making it more interactive.
- The mobile game industry is growing at a continuously higher rate. Mobile game developers are using Machine Learning technique to make mobile games. Some new Machine Learning and Deep Learning frameworks & libraries are developed to cater specifically to the mobile platform.
As the need for realism in games is increasing day by day, Machine Learning technology is playing an important role in making games more interactive and intelligent.
18) How can a Machine Learning algorithm be developed?
Ans: Developing a Machine Learning algorithm requires you to follow a process. It is an excellent opportunity to gain a really in-depth understanding of the problems that needs to be overcome while building a Machine Learning algorithm.
The following is the process to develop a Machine Learning algorithm.
1) Understand the algorithms: In order towrite an algorithm, you need to have a clear understanding of the basics of algorithms. You should know what an algorithm is and for what reason it is used.
2) Learn through different sources: Once you understand the basics of algorithms, then you need to start understanding the concepts in depth. Start learning through different sources such as textbooks, blogs, YouTube videos, etc.
3) Implement the algorithm: Once you learn all the important concepts, make some important points and try to outline the algorithm. Now that you have an outlined algorithm, you can start implementing it.
4) Validate with trusted implementation: Validating your new code is very important in order to check that your code is working properly.
5) Document your process: This is a very important step. Once you successfully built your own algorithm, you need to document it properly. This will help you to gain an even better understanding about developing the algorithm and to also showcase your talent to potential employers.
19) How do Machine Learning algorithms learn from data?
Ans: Machine Learning is a technique that allows the machines to learn from their past experiences. Machine Learning algorithms find natural patterns in data that generate some meaningful information and help the organizations to make their business decisions. One of the Machine Learning types, namely supervised learning, trains a model on known input and output data to generate reasonable predictions for the responses to new data. Another type, known as unsupervised learning type, finds hidden patterns in data.
20) What amazing things can be done using Machine Learning?
Ans: There are many amazing things happening in the world with the help of Machine Learning technology. Today, this technology is used by several companies to enhance their customer experience. Some of the amazing things that can be done using Machine Learning are:
1) Product recommendations: Product recommendation is one of the features provided by Machine Learning. Many e-commerce business and video streaming apps utilize this feature to recommend the right products to their customer. Machine Learning technology studies the past search history of their customers and recommends them the products.
2) Social Media services: Machine Learning is having a lot of impact on the social media we use in our day-to-day life. This technology helps social media like Facebook in suggesting to its users to connect with their friends.
3) Virtual Personal Assistants: Many Virtual Personal Assistants such as Amazon’s Alexa and Google Assistant are powered by Machine Learning technology. These devices assist in finding useful information, when asked via text or voice.
4) Information retrieval: One of the major benefits of Machine Learning is that it is useful in extracting some meaningful information from a huge amount of structured and unstructured data available. Since most of the data are available from different sources such as web blogs, websites, social media etc., Machine Learning technology plays an important role in extracting valuable information from them.
5) Language identification: Itisanother feature provided by Machine Learning technology. It is a process of identifying the type of language. Some devices like Apache OpenNLP, Apache tika are the language identifying software.
21. What is learning path for Data Analytics?
Ans: Data Analytics are professionals that are in huge demand. Organization across different sectors such as banking and finance, manufacturing, etc. are looking to hire these professionals. Let us see the learning path that the professionals should follow to learn Data Analytics.
1) Learn the basic concepts: Start learning from basic concepts. Having knowledge of some concepts such as Machine Learning, Artificial Intelligence, Big Data etc. is very important and helps to understand Data Analytics easily.
2) Learn Mathematics and statistics: Data Science professionals needs to have good knowledge of math and statistics subjects. They need to learn important concepts such as linear algebra and carry an understanding of algorithms and basic concepts of statistics.
3) Learn programming languages: Candidates needs to have knowledge of programing languages. They can choose to learn SAS, R, and Python. The ideal programming language they need to learn depends on which project they are working on or the company they are working in.
4) Learn Structure Query Language: Knowledge of Structure Query Language (SQL) is necessary for data extraction, manipulation and collection. So, candidates should focus on learning SQL.
5) Learn Data Visualization: Data Visualization tools help the candidates to make their results clear to the audience. So, learn Data Visualization tools such as Tableau.
6) Learn Soft skills: Once you have a good understanding of technical skills, you need to learn some soft skills such as good communication skills, ability to solve problems, work as a good team member, etc.
7) Build your own Data Analytics project: When you gain sufficient knowledge about Data Analytics, it is ideal to start building your own project. This will help you to showcase your talent to others. Working on your own project by applying all the skill sets you have learnt will further increase your knowledge to new heights.
Conclusion:
The above discussion was aimed to provide our readers some top questions and answers related to Machine Learning technology. Please write your feedback in the comment section. We also request you to share this blog in your social network so that it can reach to maximum people who are looking for such information.
