Machine Learning Tutorial

especially in deep learning. Additional Types : Module 1: Machine Learning Pipeline This section covers preprocessing。

exploratory data analysis and model evaluation to prepare data， like a flowchart. Easy to understand and use. 4. Support Vector Machines (SVM) A bit more advanced—it tries to draw the best line (or boundary) to separate different categories of data. 5. k-Nearest Neighbors (k-NN) This model looks at the closest data points (neighbors) to make predictions. Super simple and based on similarity. 6. Naïve Bayes A quick and smart way to classify things based on probability. It works well for text and spam detection. 7. Random Forest (Bagging Algorithm) A powerful model that builds lots of decision trees and combines them for better accuracy and stability. 8. Introduction to Ensemble Learning Ensemble learning combines multiple simple models to create a stronger， monitored and maintained efficiently in real-world production systems. ， commonly used for time series problems like sales， ML teaches systems to think and understand like humans by learning from the data. Machine Learning is mainly divided into three core types: Note: The following are not part of the original three core types of ML， uncover insights and build reliable models. 1. Data Preprocessing 2. Exploratory Data Analysis 3. Model Evaluation Module 2: Supervised Learning Supervised learning algorithms are generally categorized into  two main types:  Supervised Learning There are many algorithms used in supervised learning each suited to different types of problems. Some of the most commonly used supervised learning algorithms are: 1. Linear Regression This is one of the simplest ways to predict numbers using a straight line. It helps find the relationship between input and output. 2. 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. 3. Decision Trees A model that makes decisions by asking a series of simple questions， but they have become increasingly important in real-world applications，。

Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. In simple words， demand or stock prices. Module 7: Deployment of ML Models The trained ML model must be integrated into an application or service to make its predictions accessible. APIs allow other applications or systems to access the ML model's functionality and integrate them into larger workflows. MLOps ensure they are deployed。

smarter model. There are mainly two types of ensemble learning: Module 3: Unsupervised learning Unsupervised learning are again divided into three main categories based on their purpose:  Unsupervised learning 1. Clustering Clustering algorithms 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: 2. Dimensionality Reduction Dimensionality reduction is used to simplify datasets by reducing the number of features while retaining the most important information. 3. Association Rule Find patterns between items in large datasets typically in market basket analysis . Module 4: Reinforcement Learning Reinforcement learning interacts with environment and learn from them based on rewards. Reinforcement Learning 1. Model-Based Methods These methods use a model of the environment to predict outcomes and help the agent plan actions by simulating potential results. 2. Model-Free Methods The agent learns directly from experience by interacting with the environment and adjusting its actions based on feedback. Module 5: Semi Supervised Learning It uses a mix of labeled and unlabeled data making it helpful when labeling data is costly or it is very limited. Semi Supervised Learning Module 6: Forecasting Models Forecasting models analyze past data to predict future trends。