Machine Learning methods
Machine Learning is mainly
divided into three core types: Supervised, Unsupervised and Reinforcement Learning.
Supervised Learning: Trains models on labeled data to
predict or classify new, unseen data.
Unsupervised Learning: Finds patterns or groups in unlabeled data, like clustering or
dimensionality reduction.
Reinforcement Learning: Learns through trial and error to maximize rewards, ideal for
decision-making tasks. Before going to the Machine Learning, in order to make predictions proper,
the certain steps are used to produce a machine learning model that can make perfect
predictions.
Machine learning lifecycle
is a process that guides development and deployment of machine learning models
in a structured way. It consists of various steps. Each step plays a crucial
role in ensuring the success and effectiveness of the machine learning model.
By following the machine learning lifecycle we can solve complex problems, can get data-driven insights and create scalable and sustainable models. The steps are:Problem Definition need to identify the business problem and frame and establishes a foundation for machine learning lifecycle. The Crucial elements are project objectives, desired outcomes and the scope of the task.
Data Collection After problem definition, machine learning lifecycle progresses to data collection. It is systematic collection of datasets as raw data to train model. The features and characteristics of Data Collection are Relevance, Quality, Quantity
·
Data Cleaning and Preprocessing With datasets in hand now we need to do data cleaning and preprocessing. Data cleaning involves addressing issues such as missing values,
outliers and inconsistencies in data that could compromise the accuracy and
reliability of the machine learning model. Preprocessing is done by
standardizing formats, scaling values and encoding categorical variables
creating a consistent and well-organized dataset.
Supervised Learning: Supervised learning algorithms are categorized into two main
types: Classification - where the goal is to predict
discrete labels or categories Regression - where the aim is to predict continuous numerical values.Supervised Learning algorithums are Linear Regression which is one of the simplest ways to predict numbers using a straight
line helping to find the relationship between input and output.Logistic
Regression, Used when the output is a "yes or no" type answer. It
helps in predicting categories like pass/fail or spam/not spam.Decision
Trees a model that makes decisions by asking a series of simple questions,
like a flowchart. Easy to understand and use.Support Vector Machines (SVM)
more advanced—it tries to draw the best line (or boundary) to separate
different categories of data.k-Nearest Neighbors (k-NN)This model looks
at the closest data points (neighbors) to make predictions. Super simple and
based on similarity.
Unsupervised learning: This type of learning is divided into three main categories Clustering , Association Rule Mining and Dimensionality Reduction. Clustering, a group of algorithms that group data points into clusters based on their similarities or differences. Types of clustering algorithms are: Centroid-based Methods, Distribution-based Methods, Connectivity based methods, Density Based methods:Dimensionality Reduction, used to simplify datasets by reducing the number of features while retaining the most important information.
Reinforcement Learning, learning interacts with environment and learn from them based on rewards. The methods are Model-Based Methods which use a model of the environment to predict outcomes and help the agent plan actions by simulating potential results.Model-Free Methods The agent learns directly from experience by interacting with the environment and adjusting its actions based on feedback.
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