Exploring Process Of Machine Learning Training Ppt

Slide 1

This slide lists seven key steps that make the goal of embedding computers with intelligence relatively simple starting from data collection to making predictions.

Slide 2

This slide describes the first step of Machine Learning: Data Collection. Machines begin by learning from the data that you provide them. It is critical to obtain reliable data to identify the correct patterns for your Machine Learning model. Be sure to use data from a reputable source, as this will directly impact your model's output. Good data is meaningful, has few missing or duplicate numbers, and accurately represents the subcategories/classes available.

Slide 3

This slide lists the major points involved in Data Preparation. Starting with putting all of your data together and randomizing it, ensuring that data is dispersed uniformly and that the ordering does not interfere with the learning process. Separating the cleansed data into two sets, one for training and one for testing. The training set is the set from which your model learns, and a testing set is used to assess the correctness of your model after it has been trained.

Slide 4

This slide showcases that a Machine Learning model results from executing a Machine Learning algorithm on the acquired data. It is critical to select a model that is appropriate to the work at hand. Scientists and engineers have created models that are suitable for tasks such as speech recognition, picture recognition, prediction, and so on.

Slide 5

This slide lists that the most critical phase in Machine Learning is training. During training, you feed the prepared data to your Machine Learning model, which searches for patterns and makes predictions.

Slide 6

This slide gives an overview of model evaluation. After you've trained your model, you'll want to see how it's doing. This is accomplished by assessing the model's performance with unknown data. If testing is done on the same data used for training, you will get a disproportionately high level of precision. The program, in this case, is already familiar with data and sees the same patterns as it did previously.

Slide 7

This slide lists the steps involved in parameter tuning. After you've constructed and tested your model, check if you can enhance its accuracy This is accomplished by fine-tuning the parameters in your model. Parameters are the variables in the model that are usually determined by the programmer. The accuracy will be highest at a particular parameter value, and finding these settings is referred to as parameter tweaking.

Slide 8

This slide gives an overview of prediction. Making prediction refers to the result of an algorithm after it has been trained on a previous dataset and applied to new data while anticipating the likelihood of a particular outcome, such as whether or not a customer would churn in 30 days.