Machine Learning Interview Questions and Answers

Frequently Asking Interview Question and Answers

Here we have provided you with Machine Learning interview questions and answers. You can recall whatever you have learned in Machine Learning through these questions. These questions will also help you to boost your confidence level.

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1.What is the difference between artificial intelligence and machine learning?

Answer : Difference between Machine learning and Artificial Intelligence. … Artificial refers to something which is made by human or non natural thing and Intelligence means ability to understand or think. There is a misconception that Artificial Intelligence is a system, but it is not a system. AI is implemented in the system.

2.Is Python necessary for machine learning?

Answer :Python does contain special libraries for machine learning namely scipy and numpy which great for linear algebra and getting to know kernel methods of machine learning. … You can build your own regressions analysis and time series simulation easily, which would create strong machine learning algorithms.

3.Is deep learning better than machine learning?

Answer : The key difference between deep learning vs machine learning stems from the way data is presented to the system. Machine learning algorithms almost always require structured data, whereas deep learning networks rely on layers of the ANN (artificial neural networks).

4.What is machine learning artificial intelligence?

Answer :While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks “smartly.” Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems.

5.Why is Python better for machine learning?

Answer :Python is widely considered as the preferred language for teaching and learning Ml (Machine Learning). … As compared to c, c++ and Java the syntax is simpler and Python also consists of a lot of code libraries for ease of use. >Though it is slower than some of the other languages, the data handling capacity is great.

6.Is Python enough for machine learning?

Answer :Machine Learning is not limited to any specific language. You will come across ML libraries in different programming languages like C,C++, R and Python. … Python ensures efficient scientific computing and data processing, thanks to its helpful libraries like SciPy, NumPy and Pandas.

7.How do I learn Python machine learning?

Answer :Download and install Python SciPy and get the most useful package for machine learning in Python.
Load a dataset and understand it’s structure using statistical summaries and data visualization.
Create 6 machine learning models, pick the best and build confidence that the accuracy is reliable.

8.What should I learn before machine learning?

Answer :Having prior knowledge of the following is necessary before learning machine learning.
Linear algebra.
Probability theory.
Optimization theory.

9.Which is best R or Python?

Answer: In a nutshell, he says, Python is better for for data manipulation and repeated tasks, while R is good for ad hoc analysis and exploring datasets. … R has a steep learning curve, and people without programming experience may find it overwhelming. Python is generally considered easier to pick up.

10.Is octave good for machine learning?

Answer :Octave is free but Matlab is better for resources and supports. Hi, Matlab is better for machine learning problems and you can also go for Python. … Python is also used as a glue between different approaches to data science.

11.Is Python necessary for machine learning?

Answer : Python does contain special libraries for machine learning namely scipy and numpy which great for linear algebra and getting to know kernel methods of machine learning. … You can build your own regressions analysis and time series simulation easily, which would create strong machine learning algorithms.

12.Why do we use Python and machine learning AI?

Answer : Python offers concise and readable code. While complex algorithms and versatile workflows stand behind machine learning and AI, Python’s simplicity allows developers to write reliable systems. Developers get to put all their effort into solving an ML problem instead of focusing on the technical nuances of the language.

13.How difficult is machine learning?

Answer : However, machine learning remains a relatively ‘hard’ problem. There is no doubt the science of advancing machine learning algorithms through research is difficult. … Machine learning remains a hard problem when implementing existing algorithms and models to work well for your new application.

14.Which Python library is used for machine learning?

Answer :It uses the advantage of numerical and scientific computing libraries of Python such as NumPy and SciPy. Tensorflow is a Python library written in both C++ and Python based on Deep Learning. Tensorflow is most stared repository in GitHub for machine learning topic. PyTorch is a deep learning library.

15.How do I become an expert in machine learning?

Answer :
Step 1: Understand the basics. …
Step 2: Learn some Statistics. …
Step 3: Learn Python or R (or both) for data analysis. …
Step 4: Complete an Exploratory Data Analysis Project. …
Step 5: Create unsupervised learning models.

16.How do I become a machine learning scientist?

Learn to code using Python or a similar language. To become a machine learning engineer, you’ll need to know how to read, create, and edit computer code. …
Work through online data exploration courses. …
Complete online courses related to machine learning. …
Earn a relevant certification or degree to help you land a job.

17.What is meant by machine learning?

The application of Artificial Intelligence (AI) is machine learning would provide an opportunity to improve the experience without being explicitly programmed to learn. Machine Learning focuses on developing software to learn on their own that can be used to access information.

18.What is machine learning and its types?

In general, supervised machine learning and unsupervised machine learning are two types of machine learning algorithms. In addition, new fields emerge with the development of this field, known as reinforcement learning.

19.What is machine learning useful for?

Machine learning is used globally in value-added industries in all industries. It helps organizations to understand, learn, discover, improve and organize their processes. So, understanding machine learning is more important and cost-effective than you can do.

20.Why do we need machine learning?

The unnecessary aspects of machine learning are important because they can be adjusted as they enter new information. They come from older systems for decision making, reliability and results.

21.What is a machine learning model?

The machine learning model represents the mathematics of the actual process. The learning algorithm has a similar structure to match the input parameters with the training data. The machine learning model is the result of the learning process, after which you can use it.

22.What is the best machine learning algorithm?

Top Machine Learning AlgorithmsNaïve Bayes Classifier Algorithm.K Means Clustering Algorithm.Support Vector Machine Algorithm.Apriori Algorithm.Linear Regression.Logistic Regression.Artificial Neural Networks.Random Forests.

23.What is machine learning target?

Rank tries to change the properties of the data set that you want to look at carefully. Now the relationship between historical data, ethics and other things is exemplified by the use of the learning machine example from a product system to the use of features in place of policy, and objectives, and databases.

24.How does a machine learning model work?

The existence of uncertainty in machine learning does not provide a blueprint for prediction based on evidence. The algorithm learns to monitor the responses to known data points and data and performs a simulation to provide useful information on the response to new statistics

Hope you find these Machine Learning interview questions useful. Go through them carefully; they not only help in revisiting what you have learned but also will help you to face the interview with confidence.