Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.
Machine learning is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.
Regression is a statistical approach to find the relationship between variables. In machine learning, this is used to predict the outcome of an event based on the relationship between variables obtained from the data-set. More formally, given a variable Y and a number of variables X1, X2, …, Xp that may be related to Y, regression analysis helps us to determine how the value of Y changes when any one of the X’s is varied, while the other X’s are held fixed.
Classification is a type of supervised learning. It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. It predicts a class for an input variable as well. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks.
Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.
Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters.
Model evaluation is an integral part of the model development process. It helps to find the best model that represents our data and how well the chosen model will work in the future. Evaluating model performance with the data used for training is not acceptable in data science because it can easily generate overoptimistic and overfitted models. The evaluation process needs to use different data to train and test the model.
Hyperparameter tuning is choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned.
Deep learning is a subset of machine learning that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation and others.
A neuron is a mathematical function that takes inputs and passes them through a series of transformations and outputs a value. A layer is a collection of neurons that take the same inputs and produce the same outputs.
An activation function is a function that is added into an artificial neural network in order to help the network learn complex patterns in the data. Activation functions are used to determine the output of neural network like yes or no. It maps the resulting values in between 0 to 1 or -1 to 1 etc. (depending upon the function).
AI ethics is a branch of ethics that is concerned with the moral issues surrounding artificial intelligence. It is also concerned with the values instilled in the design, construction, use, and regulation of artificially intelligent systems.
We have covered a lot of topics in this study session. We hope you have learned something new. If you have any questions, feel free to ask. We will try our best to answer them. Thank you for attending this workshop. We hope to see you in the next one.
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