The basic machine learning process can be divided into three parts.
1. Data input: Previous data or information is used as a foundation for decision-making in the future.
2. Abstraction: The underlying method broadens the representation o0f the incoming data.
3. Generalization: To create a framework for making judgements, the abstracted representation is generalised.
Following figure is a schematic representation of the machine learning process.
Let's take a look at the scenario of the standard learning process, which involves studying from textbooks and in-person instruction. Many students have a tendency to strive to memorise (or "learn by memory") as many things as they can. When the range of learning is not as broad, ms might be effective. Additionally, the exam's question types tend to be straightforward and easy to understand. Answering questions requires only writing down the information that has been memorised.
The approach of memorization, however, doesn't function well when the scope widens and the complexity of the exam's questions increases. A student could find it difficult to remember a large enough number of topics. The ability to memorise things also differs from student to student. Additionally, when the questions become more difficult, it may be ineffective to just repeat what was memorised. As the student advances to higher classes, the issue just gets worse.
Therefore, what we observe in the case of human learning is that students can perform well in exams up until a certain point only by excellent memorization and faultless recall, i.e. just based on knowledge input. Beyond that, it is necessary to adopt a superior learning strategy:
Must be able to deal with the complexity of the material and the difficulties associated with memorization
To be able to respond to inquiries for which a direct response has not yet been mastered.
Finding the main concepts or points in a large body of information is a wise course of action. MS aids in the development of topic outlines and a conceptual mapping of those outlines with the full body of knowledge. A large body of information, for instance, might cover all living things and their traits, such as whether they are land or water dwellers, if they lay eggs, whether they have scales or fur or neither, etc. No matter how good a photographic memory a pupil may have, memorising the traits of every live animal is a challenging undertaking. It is preferable to sketch a general idea of the main groupings to which all live animals belong and the traits that distinguish each of the basic groups. Invertebrates and vertebrates are the two primary animal classifications. Animals classified as vertebrates include fish, birds, reptiles, amphibians, and mammals. Here, we've mapped the major animal groups and their distinguishing traits.
1. Invertebrate: Do not have skeletons or backbones
2. Vertebrate
Fishes: Always live in water and lay eggs
Amphibians: Semi-aquatic i.e. may live in water or land; smooth skin; lay eggs
Reptiles: Semi-aquatic like amphibians; scaly skin; lay eggs; cold-blooded
Birds: Can fly; lay eggs; warm-blooded
Mammals: Have hair or fur; have milk to feed their young; warm-blooded
This makes it easier to remember because the scope of knowing the animal groupings to which the animals belong has been reduced. The concept of mapping animal groupings and their traits can be used to generate the rest of the responses concerning animal characteristics.