Well Posed Learning Problem:
The formal definition of Well posed learning problem is, “A computer program is said to learn from Experience E when given a task T, and some performance measure P. If it performs on T with a performance measure P, then it upgrades with experience E.
To break it down, the three important components of a well-posed learning problem are,
- Task
- Performance Measure
- Experience
To understand the topic better let’s have a look at a few classical examples,
- Learning to play Checkers:
A computer might improve its performance as an ability to win at the class of tasks that are about playing checkers. The performance keeps improving through experience by playing against itself.
To simplify,
T -> Play the checkers game.
P -> Percentage of games won against the opponent.
E -> Playing practice games against itself.
- Handwriting Recognition:
Handwriting recognition (HWR) is a technology that converts a user’s handwritten letters or words into a computer-readable format (e.g., Unicode text).
Its applications are numerous, it is used in reading postal addresses, bank forms, etc.
T -> recognizing and classifying handwritten words from images.
P -> Percentage of correctly identified words.
E -> set of handwritten words with their classifications in a database.
- A Robot Driving Learning Problem:
For a robot to drive on a four-lane highway it needs a human-like understanding of all the possibilities it might encounter.
With the use of sight scanners and advanced machine learning algorithms, it can be made possible.
T –> To drive on public four-lane highways using sight scanners.
P -> the average distance progressed before an error.
E -> the order of images and steering instructions noted down while observing a human driver.
- A spam filtering for emails learning problem:
A spam filter is software that detects unsolicited and undesired email and prevents it from reaching the inbox of a user.
T -> Identifying whether or not an email is spam.
P -> The percentage of emails correctly categorized as spam or nonspam.
E -> Observing how you categorize emails as spam or nonspam.
- Face Recognition Problem:
A facial recognition system device is capable of matching a human face from a digital image or a video frame against a database of faces.
It works by locating and measuring facial characteristics from a given image and is often used to verify users through ID verification services.
T -> Predicting distinct sorts of faces.
P -> Ability to anticipate the largest number of different sorts of faces.
E -> train the system with as many datasets of varied facial photos as possible.
Life Cycle of Machine Learning:
Machine learning has given computers the ability to learn on their own without having to be explicitly programmed.
However, how does a machine learning system function? As a result, the machine learning life cycle may be used to explain it.
A machine learning project’s life cycle is a cyclic method for developing an effective machine learning project. The life cycle’s primary goal is to find a solution to the problem or project.
1. Data Gathering: As the name suggests in this step we gather all the data-related problems.
The steps involve Identifying various data sources, collecting data, and integrating the data obtained from different sources.
2. Preparation of Data: We must prepare the data for further processing after it has been collected. Data preparation entails putting our data in an appropriate location and preparing it for use in machine learning training.
In this stage, we combine all of the data and then randomize the order of the data.
This stage may be separated into two parts: data exploration, in which we learn about the data, and data pre-processing, in which the data is prepared for analysis.
3. Data Wrangling: It is the process of cleaning the data, selecting the variable to utilize, and changing the data into a suitable format for analysis in the following phase.
4. Data Analysis: The data has now been cleaned and prepped and is ready to be analyzed. This stage entails Analytical methods selection, creating models, examine the outcome.
5. Train Model: Datasets are used to train the model, which is then used to train the model using various machine learning techniques.
A model must be trained in order for it to comprehend the numerous patterns, rules, and characteristics.
6. Test Model: The % correctness of the model is determined by testing it against the project or problem’s requirements.
7. Deploy model: We deploy the model in the real system if the above-prepared model produces an accurate output that meets our requirements at a reasonable pace.
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