1. What is Machine learning?
Machine learning is a method of data analysis that automates analytical model building. It is a branch of AI based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
2. Mention the difference between Data Mining and Machine learning?
Data mining is designed to extract the principles from large quantities of data, while machine learning teaches a computer how to learn and comprehend the given parameters. Or to put it another way, data mining is simply a method of researching to determine a particular outcome based on the total of the gathered data.
3. What is ‘Overfitting’ in Machine learning?
Overfitting is a modeling error in statistics that happens when a function is too closely aligned to a limited set of data points.Overfitting the model generally takes the form of making an overly complex model to explain idiosyncrasies in the data under study.
4. Why overfitting happens?
Overfitting happens when a model learns the detail and noise within the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations within the training data is picked up and learned as concepts by the model.
5. How can you avoid overfitting ?
- Cross-validation. Cross-validation is a powerful preventative measure against overfitting.
- Train with more data. It won't work every time, but training with more data can help algorithms detect the signal better.
- Remove features.
- Early stopping.
- Regularization.
- Ensembling.
6. What is inductive machine learning?
The inductive machine learning involves the method of the learning by examples, where a system, from a set of observed instances tries to induce a general rule.
7. What are the five popular algorithms of Machine Learning?
The list of the five most commonly used machine learning algorithms are:- Linear Regression.
- Logistic Regression.
- Decision Tree.
- Naive Bayes.
- kNN.
8. What are the different Algorithm techniques in Machine Learning?
The different Algorithm techniques in Machine Learning are:- Linear Regression.
- Logistic Regression.
- Decision Tree.
- SVM.
- Naive Bayes.
- kNN.
- K-Means.
- Random Forest.
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9. What are the three stages to build the hypotheses or model in machine learning?
The three stages to create the hypotheses in machine learning are model building, model testing and applying model.
10. What is the standard approach to supervised learning?
Supervised learning is the machine learning task of learning a function that maps an input to an output based on the example input-output pairs. It infers a function from labeled training data consisting of a set of the training examples.
11. What is ‘Training set’ and ‘Test set’?
- Training set—a subset to train a model.
- Test set—a subset to test the trained model.
12. List down various approaches for machine learning?
The various approaches for machine learning are:- Supervised Learning;
- Unsupervised Learning;
- Semi-supervised Learning; and.
- Reinforcement Learning.
13. Explain what is the function of ‘Unsupervised Learning’?
Unsupervised learning is a type of machine learning task where you only have to insert the input data (X) and no corresponding output variables are needed. It does not have labeled data for training.
14. Explain what is the function of ‘Supervised Learning’?
Supervised learning uses a training set to teach models to yield the desired output. This training dataset includes the inputs and correct outputs, which allow the model to learn over time. The algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized.
15. What is algorithm independent machine learning?
Algorithm independent machine learning are:- Mathematical Foundations that do not depend. upon any particular classifier or learning. algorithm used.
- Techniques used in conjunction with different.
16. What is the difference between artificial learning and machine learning?
AI is a bigger concept to create intelligent machines that can simulate the human thinking capability and behavior, whereas, machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly.
