In this Blog-post we are going to discuss about the top 10 Machine Learning Algorithm Questions which are 80% asked for the post of the Data Science on any other Data Science related company , I have seen lots of new comers in the field of machine learning learnt about the technology and also deploy simple machine learning models related to the Linear Regression, Logistic Regression etc. but mostly fails on their interview. This is why I thought to add a new article which is completely based on the Machine Learning Interviews.
So Lets begin :
- What is Machine Learning?
Machine Learning is the branch of the computer science which deals with the system programming in order to automatically learn and improve with the experience. For example: Robots are programmed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data.
2. What is ‘Overfitting’ in Machine learning?
When a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs. When a model is excessively complex, overfitting is normally observed, because of having too many parameters with respect to the number of training data types, The model exhibits poor performance which has been overfit.
3. How can you avoid overfitting ?
By using a lot of data overfitting can be avoided, overfitting happens relatively as you have a small dataset, and you try to learn from it. But if you have a small database and you are forced to come with a model based on that. In such situation, you can use a technique known as cross validation. In this method the dataset splits into two section, testing and training datasets, the testing dataset will only test the model while, in training dataset, the datapoints will come up with the model. In this technique, a model is usually given a dataset of a known data on which training (training data set) is run and a dataset of unknown data against which the model is tested. The idea of cross validation is to define a dataset to “test” the model in the training phase.
4) Why overfitting happens?
The possibility of overfitting exists as the criteria used for training the model is not the same as the criteria used to judge the efficacy of a model.
5.) Mention the difference between Data Mining and Machine learning?
Machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed. While, data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns. During this process machine, learning algorithms are used.
6.) What are the five popular algorithms of Machine Learning?
a) Decision Trees
b) Neural Networks (back propagation)
c) Probabilistic networks
d) Nearest Neighbor
e) Support vector machines
7.) What are the different Algorithm techniques in Machine Learning?
The different types of techniques in Machine Learning are
a) Supervised Learning
b) Unsupervised Learning
c) Semi-supervised Learning
d) Reinforcement Learning
e) Transduction
f) Learning to Learn
8.) What are the three stages to build the hypotheses or model in machine learning?
a) Model building
b) Model testing
c) Applying the model
9.) What is the standard approach to supervised learning?
The standard approach to supervised learning is to split the set of example into the training set and the test.
10.) What is ‘Training set’ and ‘Test set’?
In various areas of information science like machine learning, a set of data is used to discover the potentially predictive relationship known as ‘Training Set’. Training set is an examples given to the learner, while Test set is used to test the accuracy of the hypotheses generated by the learner, and it is the set of example held back from the learner. Training set are distinct from Test set.
I will cover lots of topic related to the machine learning and deep learning in our upcoming posts. I tried to cover the important machine learning questions which is basically asked on a each interview for the freshers as well for the beginners.
Happy Reading and Stay Safe 🙂