On the other hand, online learning is a combination of different techniques of ML where data arrives in sequential order and the learner (algorithm/model) aims to learn and update the best predictor for future data at every step.
In our daily routine, we unknowingly perfectly transfer the knowledge of some activity or task to the related one. Whenever we come across a new problem statement or task, first we recognize it and try to apply the relevant experience which results in hassle-free completion of the task.
In machine learning, feature selection is the procedure of selecting important features from the data…
In Federated Learning, a model is trained from user interaction with mobile devices. Federated Learning enables mobile phones to collaboratively learn over a shared prediction model while keeping all the training data on the device, changing the ability to perform machine learning techniques by the need to store the data on the cloud.
The T-test is a hypothesis testing method that helps in testing the significance of two or more groups and determining the important differences between the groups being compared. It is a variation of inferential statistics and is mostly used with datasets that possess a normal distribution but with unidentified variances.
In the era of Data Science where knowledge of programming languages like Python and R is essential to implement the fundamental algorithms and techniques related to Machine learning and Data analytics.
Scaling the target value is a good idea in regression modelling; scaling of the data makes it easy for a model to learn and understand the problem.
Long Short Term Memory in short LSTM is a special kind of RNN capable of learning long term sequences. They were introduced by Schmidhuber and Hochreiter in 1997. It is explicitly designed to avoid long term dependency problems. Remembering the long sequences for a long period of time is its way of working.
Transfer learning is a technique for predictive modelling on a different yet similar problem that can then be reused partly or wholly to accelerate its training and eventually improve the performance of the model for the problem. It is the reuse of a pre-trained model on a new problem. This technique is currently becoming very popular in deep learning because it can train deep neural networks with comparatively little data and in less time. Finding its use in the data science field as most real-world problems typically do not have millions of labelled data points to train such complex models. Features from a model that has learned to identify something can become useful to kick-start a model meant to identify another thing.
To fill the gap between Source data (train data) and Target data (Test data) a concept called domain adaptation is used. It is the ability to apply an algorithm that is trained on one or more source domains to a different target domain.
the decision tree was an example of a black-box algorithm in any random forest, that we can access to know how it works.by visualizing trees in random forest we can know about the model
It combines Freeze Algorithm, AutoPipe, AutoDP, and AutoCache modules to increase the training speed significantly.