Foundations of ML

- Overview

Machine learning (ML) uses programmed algorithms that receive and analyze input data to predict output values within acceptable ranges. As new data is fed to these algorithms, they learn and optimize their operations to improve performance, developing "intelligence" over time.

ML algorithms are vital for a variety of tasks related to classification, predictive modeling, and analysis of data. There are four types of ML algorithms: supervised, semi-supervised, unsupervised, and reinforcement. 

Choosing the right ML algorithm depends on several factors, including but not limited to: data size, quality, and diversity, and what answers a business hopes to derive from that data. 

Other considerations include accuracy, training time, parameters, data points, and more. Therefore, choosing the right algorithm is a combination of business requirements, specifications, experimentation, and available time.

Even the most experienced data scientist can't tell you which algorithm will perform best without experimenting with other algorithms. However, we've compiled a "cheat sheet" of machine learning algorithms to help you find the best one for your specific challenge.

Please refer to the following for more details:

• Wikipedia: Machine Learning
• Wikipedia: Outline of Machine Learning

- The Components of A ML Model

There are four basic types of ML: supervised learning, unsupervised learning, semisupervised learning and reinforcement learning. The type of algorithm data scientists choose depends on the nature of the data.

ML is a set of algorithms learned from data and/or experiences, rather than being explicitly programmed. Each task requires a different set of algorithms, and these algorithms detect patterns to perform certain tasks. 

The ML workflow is pretty simple: 

• You have data which contains patterns.
• You supply it to a ML algorithm which finds the patterns and generates a model.
• The model recognizes these patterns when presented with new data.

The three components that make a ML model are:

• Representation: How you want to look at your data.
• Evaluation: How good models are differentiated; how programs are evaluated.
• Optimization: The process for finding good models; how programs are generated.

- Basic Concepts of Machine Learning (ML)

Basic concepts of Machine Learning (ML) include: data preprocessing, model selection, training, evaluation, supervised learning, unsupervised learning, reinforcement learning, features, labels, algorithms like linear regression, decision trees, and neural networks, and the idea of optimizing a model to minimize errors based on training data; essentially, it's the ability for a computer to learn patterns from data without explicit programming, allowing it to make predictions or decisions on new data. 

Key concepts to understand:

• Data: The foundation of ML, where data is split into training sets (used to train the model), validation sets (used to tune hyperparameters), and testing sets (used to evaluate the model's performance on unseen data). 
• Features: Individual attributes or characteristics extracted from the data that the model learns from. 
• Labels: The target values or desired outputs associated with the data, used in supervised learning. 
• Algorithms: Mathematical equations that the model uses to learn patterns from the data, like linear regression for predicting continuous values or decision trees for classification. Algorithms play a central role in machine learning. There are four types of machine learning algorithms: supervised, unsupervised, semi-supervised, and reinforced.
• Training: The process of feeding data to the model, allowing it to adjust internal parameters to improve its ability to make accurate predictions. 
• Model evaluation: Measuring how well the trained model performs on new data using metrics like accuracy, precision, recall, or mean squared error.
• Clustering: Clustering is a fundamental task in machine learning, data mining, and signal processing.
• Neural networks: Neural networks are a subset of deep learning that mimic the human brain through algorithms. They have four major components: inputs, weights, a bias or threshold, and an output.
• Decision trees: Decision trees are a popular tool for classification and prediction problems in machine learning. They describe rules that can be interpreted by humans and applied in a knowledge system such as databases.
• Linear regression: Linear regression is one of the fundamental algorithms in machine learning. It's based on simple mathematics and works on the principle of formula of a straight line, mathematically denoted as y = mx + c.
 

- The Ten Main ML Disciplines

Machine learning (ML) is a type of artificial intelligence (AI) that focuses on building computer systems that learn from data. ML encompasses a broad range of techniques that enable software applications to improve their performance over time. 

Machine learning algorithms are trained to find relationships and patterns in data. They use historical data as input to make predictions, classify information, cluster data points, reduce dimensionality, and even help generate new content, as new ML applications such as ChatGPT demonstrate.

The ten methods are the main disciplines in ML. Most ML algorithms fall into one of these categories:

• Regression
• Classification
• Clustering
• Dimensionality Reduction
• Ensemble Methods
• Neural Nets and Deep Learning
• Transfer Learning
• Reinforcement Learning
• Natural Language Processing
• Word Embeddings

[Machine Learning Cheat Sheet]

- ML Algorithms in Python

Machine learning (ML) is the concept of programming a machine to learn from experience and from different examples without being explicitly programmed. It is an application of artificial intelligence (AL) that allows machines to learn on their own. 

ML algorithms are a combination of mathematics and logic that adjust themselves to perform more incrementally as input data changes. 

As a general-purpose, easy-to-learn and understand language, Python can be used for a variety of development tasks. It is capable of many ML tasks, which is why most algorithms are written in Python. 

The process of creating a ML algorithm is divided into two parts - the training and testing phases. Although there are many types of ML algorithms, they are divided into the following categories: supervised learning, unsupervised learning, and reinforcement learning. 

There are many different ML algorithms available in Python. Here are a few of the most popular: linear regression, decision trees, support vector machines (SVMs), random forests, K-nearest neighbors (KNN). These are just a few of the many ML algorithms available in Python. The best algorithm to use for a particular problem will depend on the specific data and the desired outcome. 

- Machine Learning Workflow

A machine learning (ML) workflow defines the stages implemented during a ML project. The core of the ML workflow is writing and executing ML algorithms to obtain ML models. 

Machine learning (ML) modeling steps typically involve: data collection, data preprocessing, choosing a ML model, training the model, evaluating its performance, hyperparameter tuning, and finally deploying the model to make predictions; essentially, gathering relevant data, preparing it for analysis, selecting the appropriate model, training it on the data, assessing its accuracy, optimizing settings, and then putting the model into use to make predictions on new data. 

Breakdown of the key steps:

• Data Collection: Gathering the necessary data for training the model, which could involve collecting from various sources like databases, APIs, or manual input.
• Data Preprocessing: Cleaning and preparing the data by handling missing values, outliers, normalization, feature engineering, and data transformation to make it suitable for model training.
• Model Selection: Choosing the appropriate machine learning algorithm based on the problem type (e.g., regression, classification, clustering) and data characteristics.
• Model Training: Feeding the prepared data into the chosen model to allow it to learn patterns and relationships, adjusting internal parameters to optimize predictions.
• Model Evaluation: Assessing the performance of the trained model using metrics like accuracy, precision, recall, F1-score, depending on the task, to identify potential issues and areas for improvement.
• Hyperparameter Tuning: Adjusting the model's configuration parameters (like learning rate, number of hidden layers) to further enhance performance.
• Model Deployment: Integrating the trained model into an application or system to make predictions on new data.