Machine Learning is a subdivision of AI (artificial Intelligence) that is mainly focused on creating such systems that can automatically learn and improve from experience or data, rather than relying on explicit instructions.
The term "machine learning workflow" refers to the succession of phases or procedures involved in the process of developing a successful machine learning system. The basic machine learning flow is:
Data —> Model —> Prediction
Various Stages in Machine Learning Flow:
The steps of the machine learning process are as follows:
- Collection Of Data: In this step, we collect data from various sources which include databases, sensors, APIs, and other means.
- Preprocessing of Data: Cleaning the data and preparing it for ML tasks. This includes removing missing or duplicate data, normalization, scaling, and feature selection.
- Splitting of Data: Splitting the data into different sets such as training, testing, etc. It is done to train the model on the training data and evaluate the performance of the model on the testing data.
- Model Selection: It is the process of selecting a suitable ML algorithm for the task at the problem. This is determined by the type of data, the nature of the problem, and the availability of resources for computation.
- Training Model: It is an iterative method here the training data is adjusted in a way that minimizes the errors or loss functions. It will iteratively repeat the process until the model finds a satisfactory solution.
- Choosing an Algorithm to Learn: It is important to know the types of Machine Learning because we need to know that which algorithm and where to apply to solve business problems.
Generally, there are three types of ML which include
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Let us learn in detail about these types.
1. Supervised Learning:
Supervised learning is a form of machine learning in which a model is trained using labeled data. It is generally applicable, and since you already have training data and know the answer, the algorithm produces a set of input data and their matching output. Supervised Learning can be further classified into two types:
- Fraud Detection
- Image recognition
- Medical Diagnostics
- Product Sales Prediction
- Weather Forecasting
- Market Forecasting
- Population Growth Prediction
2. Unsupervised Learning:
In unsupervised learning, the machine must uncover and create the labels itself where you have data but look for insights within the data. It is further divided into below types of algorithms:
- Clustering: The task of clustering is to identify different patterns within the clusters of the data without any previous knowledge about labels.
- Dimensionality Reduction: It is a technique used in ML to reduce the number of attributes or variables in a set of data while keeping as much information as possible.
There are two main approaches to dimensionality reduction:
- Feature Selection: This includes selecting a subset of features that are more relevant to the main problem given. It is done by analyzing the relation between features and target variables by using machine learning algorithms.
- Feature Extraction: This involves creating new features that collect the most essential data from the original features.
- Product recommendations
- Medical diagnostics
- Gene sequencing
- Data Structure discovery
The advantage of unsupervised learning is that since we discover the patterns, they enable you to see patterns in data that you are not aware of before.
3. Reinforcement Learning:
It is the process of learning through trial and error. Here the model learns based on experience and feedback. Unlike other ML types, reinforcement learning continuously improves its model by mining feedback from previous iterations. Most business problems are supervised learning problems.
It is used when the goal is already known but the path to achieving is not known and that path requires lots of trial and error to discover.
- AWS Deep Racer.
7. Predictions: At this point, Finally, the created system is used in the real world. The fundamental value of machine learning is realized here. In this step, machine learning model is fed with the real-world data, and model makes predictions on that data. As per the prediction, model accuracy and efficiency is calculated.