Machine learning is a sub class of Artificial Intelligence (AI) that delivers systems the aptitude to automatically learn and advance from knowledge without being explicitly programmed. Machine learning spotlights on the development of computer programs that know how to access data and utilize it learn for themselves. The process of learning commences with observations or data, such as instants, direct understanding, instruction, or reading the data pattern. Machine learning algorithms are frequently classified as supervised, unsupervised and semi-supervised. Supervised machine learning algorithms can apply to new data by using labeled examples; unsupervised machine learning algorithms apply data that is neither classified nor labeled; semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised 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.
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.
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.
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.
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.
The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule.
The different types of techniques in Machine Learning are
The standard approach to supervised learning is to split the set of example into the training set and the test.
This might or might not apply to the job you’re going after, but your answer will help to show you know more than just the technical aspects of machine learning. Deep learning is a subset of machine learning. It refers to using multi-layered neural networks to process data in increasingly complex ways, enabling the software to train itself to perform tasks like speech and image recognition through exposure to these vast amounts of data. Thus the machine undergoes continual improvement in the ability to recognize and process information. Layers of neural networks stacked on top of each for use in deep learning are called deep neural networks.
Deductive machine learning starts with a conclusion, then learns by deducing what is right or wrong about that conclusion. Inductive machine learning begins with examples from which to conclude.
The answer depends on the degree of accuracy needed and the size of the training set. If you have a small training set, you can use a low variance/high bias classifier. If your training set is large, you will want to choose a high variance/low bias classifier.
Both bias and variance are errors. Bias is an error due to flawed assumptions in the learning algorithm. Variance is an error resulting from too much complexity in the learning algorithm.
You can reduce dimensionality by combining features with feature engineering, removing collinear features, or using algorithmic dimensionality reduction.
Classification predicts group or class membership. Regression involves predicting a response. Classification is a better technique when you need a more definite answer.
Anyone who has used Spotify or shopped at Amazon will recognize a recommendation system: It’s an information filtering system that predicts what a user might want to hear or see based on choice patterns provided by the user.
KNN stands for K- Nearest Neighbours, it is classified as a supervised algorithm.
K-means is an unsupervised cluster algorithm.
It is known as a true positive rate. The number of positives that your model has claimed compared to the actual defined number of positives available throughout the data.
It is also known as a positive predicted value. This is more based on the prediction. It is a measure of a number of accurate positives that the model claims when compared to the number of positives it actually claims.
Type 1 error is classified as a false positive. I.e. This error claims that something has happened but the fact is nothing has happened. It is like a false fire alarm. The alarm rings but there is no fire.
Type 2 error is classified as a false negative. I.e. This error claims that nothing has happened but the fact is that actually, something happened at the instance.
The best way to differentiate a type 1 vs type 2 error is:Calling a man to be pregnant- This is Type 1 example Calling pregnant women and telling that she isn’t carrying any baby- This is type 2 example
A process of decomposing generic functions into a superposition of symmetric functions is considered to be a Fourier Transform.
The F1 score is defined as a measure of a model’s performance.
You can quote ISLR’s authors Hastie, Tibshirani who asserted that, in presence of few variables with medium / large sized effect, use lasso regression. In presence of many variables with small / medium sized effect, use ridge regression.
Conceptually, we can say, lasso regression (L1) does both variable selection and parameter shrinkage, whereas Ridge regression only does parameter shrinkage and end up including all the coefficients in the model. In presence of correlated variables, ridge regression might be the preferred choice. Also, ridge regression works best in situations where the least square estimates have higher variance. Therefore, it depends on our model objective.
Correlation is the standardized form of covariance.
Covariances are difficult to compare. For example: if we calculate the covariances of salary ($) and age (years), we’ll get different covariances which can’t be compared because of having unequal scales. To combat such situation, we calculate correlation to get a value between -1 and 1, irrespective of their respective scale.
Yes, we can use ANCOVA (analysis of covariance) technique to capture association between continuous and categorical variables.
Regularization becomes necessary when the model begins to ovefit / underfit. This technique introduces a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and hence reduce cost term. This helps to reduce model complexity so that the model can become better at predicting (generalizing).
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.
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.
An individual can easily find missing or corrupted data in a data set either by dropping the rows or columns. On contrary, they can decide to replace the data with another value.
In Pandas they are two ways to identify the missing data, these two methods are very useful.
isnull() and dropna().
This question depicts your understanding of the algorithm. This is something that one has to be very creative and also should have in-depth knowledge about the algorithms and first and foremost the individual should have a good understanding of the algorithms. Best way to answer this question would be start off with Web Sequence Diagrams.
Every sector in the industry is watching a job crunch. However, when it is about Artificial Intelligence and Machine learning, the need for skilled professionals of Machine Learning are at higher side. Machine Learning participants having in-depth knowledge and obtained skill-based training are able to discover better career opportunities in the worldwide job marketplaces. Besides, Machine Learning has occupied the topmost place in the arena of Artificial Intelligence. A Machine Learning participants is expected a minimum salary of 48, 000 dollars per annum. However, the salary of an experienced Machine Learning expert can reach to its double. The salaries are very reliant upon the location, business, and the company’s requirements.
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