Q1)What is Machine learning?
Machine learning is a branch of computer science which deals with system programming in order to automatically learn and improve with experience. For example: Robots are programed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data.Q2)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.Q3)Differentiate supervised and unsupervised machine learning.
- In supervised machine learning, the machine is trained using labeled data. Then a new dataset is given into the learning model so that the algorithm provides a positive outcome by analyzing the labeled data. For example, we first require to label the data which is necessary to train the model while performing classification.
- In the unsupervised machine learning, the machine is not trained using labeled data and let the algorithms make the decisions without any corresponding output variables.
Q4)How does Machine Learning differ from Deep Learning?
- Machine learning is all about algorithms which are used to parse data, learn from that data, and then apply whatever they have learned to make informed decisions.
- Deep learning is a part of machine learning, which is inspired by the structure of the human brain and is particularly useful in feature detection.
Q5)What are the five popular algorithms of Machine Learning?
- Decision Trees
- Neural Networks (back propagation)
- Probabilistic networks
- Nearest Neighbor
- Support vector machines
Q6)What are the different Algorithm techniques in Machine Learning?
The different types of techniques in Machine Learning are- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Reinforcement Learning
- Transduction
Q7)What do you understand by Reinforcement Learning technique?
Reinforcement learning is an algorithm technique used in Machine Learning. It involves an agent that interacts with its environment by producing actions and discovering errors or rewards. Reinforcement learning is employed by different software and machines to search for the best suitable behavior or path it should follow in a specific situation. It usually learns on the basis of reward or penalty given for every action it performs.
Q8) 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.Q9)What is Genetic Programming?
Genetic programming is one of the two techniques used in machine learning. The model is based on the testing and selecting the best choice among a set of results.Q10)What is the difference between heuristic for rule learning and heuristics for decision trees?
The difference is that the heuristics for decision trees evaluate the average quality of a number of disjointed sets while rule learners only evaluate the quality of the set of instances that is covered with the candidate rule.Q11)What is classifier in machine learning?
A classifier in a Machine Learning is a system that inputs a vector of discrete or continuous feature values and outputs a single discrete value, the class.Q12)What are the advantages of Naive Bayes?
In Naïve Bayes classifier will converge quicker than discriminative models like logistic regression, so you need less training data. The main advantage is that it can’t learn interactions between features.Q13)What is a model selection in Machine Learning?
The process of choosing models among diverse mathematical models, which are used to define the same data is known as Model Selection. Model learning is applied to the fields of statistics, data mining, and machine learning.Q14)What are the three stages of building the hypotheses or model in machine learning?
- Model building : It chooses a suitable algorithm for the model and trains it according to the requirement of the problem.
- Applying the model : It is responsible for checking the accuracy of the model through the test data.
- Model testing : It performs the required changes after testing and apply the final model.
Q15)What are the common ways to handle missing data in a dataset?
Missing data is one of the standard factors while working with data and handling. It is considered as one of the greatest challenges faced by the data analysts. There are many ways one can impute the missing values. Some of the common methods to handle missing data in datasets can be defined as deleting the rows, replacing with mean/median/mode, predicting the missing values, assigning a unique category, using algorithms that support missing values, etc.Q16)What do you understand by ILP?
ILP stands for Inductive Logic Programming. It is a part of machine learning which uses logic programming. It aims at searching patterns in data which can be used to build predictive models. In this process, the logic programs are assumed as a hypothesis.Q17)What are the two methods used for the calibration in Supervised Learning?
- Platt Calibration
- Isotonic Regression
Q18)What is Perceptron in Machine Learning?
In Machine Learning, Perceptron is a supervised learning algorithm for binary classifiers where a binary classifier is a deciding function of whether an input represents a vector or a number.Q19)Explain the two components of Bayesian logic program?
Bayesian logic program consists of two components. The first component is a logical one ; it consists of a set of Bayesian Clauses, which captures the qualitative structure of the domain. The second component is a quantitative one, it encodes the quantitative information about the domain.Q20)What are Bayesian Networks (BN)?
Bayesian Network is used to represent the graphical model for probability relationship among a set of variables.More Questions Coming Soon....
