"Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive."
This subfield focuses on the development of algorithms and data structures that enable computers to learn from data and make predictions or decisions.
Linear Algebra: Basic linear algebra concepts like vectors, matrices, and operations like addition, subtraction, multiplication, and transpose are essential for understanding machine learning algorithms.
Probability Theory: Understanding probability plays a major role in the development of statistical models algorithms. Concepts like Bayes' theorem, conditional probability, and distribution functions are especially relevant.
Statistics: Aspiring data scientists should know about statistical modeling, inference, and distributions. This would include concepts like hypothesis testing, ANOVA, and regression analysis.
Calculus: Machine learning algorithms often involve optimization techniques that are dependent on calculus. Basic concepts like derivatives, integrals, and limits are essential.
Optimization Algorithms: Gradient descent, stochastic gradient descent and other optimization techniques are critical to finding optimal weights and biases for machine learning models.
Programming skills: Understanding the fundamentals of programming in Python, C++, R, and other popular languages can help machine learning enthusiasts build their own algorithms.
Data Warehousing: Understanding how to store, retrieve, and manipulate large amounts of data will assist machine learning professionals with processing big data to create predictive models.
Dimensionality reduction techniques: Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Eigenvalues, and Eigenvectors are essential concepts to understand in the context of reducing data dimensions.
Data Preprocessing: The quality of data feeds directly into the accuracy of machine learning algorithms, understanding data preprocessing techniques like data cleaning and feature scaling can help improve the model.
Supervised and Unsupervised Algorithms: There are various types of machine learning algorithms that can be classified as supervised or unsupervised learning. Understanding the differences between these methods and their applications is essential for starting to learn about Machine learning algorithms.
Decision Trees: This algorithm is a commonly used machine learning algorithm that demonstrates decision making with different outcomes.
Naive Bayes: This is a statistical approach to solve machine learning problems. It is based on naïve probabilistic assumptions, which can be used for classification problems.
K-Means: Clustering is one of the machine learning techniques in which an algorithm will group the data by itself. K-means is the simplest example of this.
Random Forest: This algorithm helps overcome the problem of an underfitting or overfitting model. In this approach, multiple decision trees are built and combined to give a reliable result.
Neural Network: This type of machine learning is inspired by the human brain’s structure and is extensively used in deep learning. Neural networks are used for pattern detection and can be used in robotics, natural language processing, and computer vision.
Support Vector Machines: This algorithm can be used for both supervised and unsupervised learning with its kernel function. It is often used to model complex real-world scenarios such as stock market analysis, handwriting recognition, and natural language processing.
Gradient Boosting Machine: In this technique, a number of WEAK predictors are combined to form a strong model. This problem targets classification and regression problems.
Linear Regression: A supervised learning algorithm used for predicting a continuous outcome based on one or more input variables. It learns a linear relationship between the input and output variables.
Logistic Regression: A binary classification algorithm used for predicting the probability of a binary outcome based on one or more input variables. It learns a linear relationship between the input and output variables.
Decision Tree: A supervised learning algorithm used for both classification and regression tasks. It builds a tree-like structure of decisions based on the input variables and can handle both categorical and continuous data.
Random Forest: A supervised learning algorithm used for classification, regression, and feature selection problems. It builds an ensemble of decision trees and efficiently handles high-dimensional datasets.
Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks. It finds a hyperplane that optimally separates the data points into different classes.
K-Nearest Neighbors (KNN): A supervised learning algorithm used for classification and regression tasks. It predicts the output by finding the K nearest data points in the training set and assigning the most common output value to the new data point.
Naive Bayes: A probabilistic algorithm used for classification problems. It calculates the probability of a new data point belonging to a given class based on the probability of its features appearing in that class.
Neural Networks: A family of algorithms used for both supervised and unsupervised learning tasks. They are inspired by the structure and functions of the human brain and consist of interconnected layers of nodes that learn non-linear relationships between inputs and outputs.
Clustering: An unsupervised learning algorithm used for grouping similar data points together based on their similarity or distance. It is commonly used in customer segmentation, image processing, and anomaly detection.
Dimensionality Reduction: A technique used for reducing the number of input features in a dataset without losing important information. Popular algorithms include Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Linear Discriminant Analysis (LDA), and Factor Analysis.
"the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"Recently, generative artificial neural networks have been able to surpass results of many previous approaches."
"Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine."
"where it is too costly to develop algorithms to perform the needed tasks."
"The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods."
"Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning."
"ML is known in its application across business problems under the name predictive analytics."
"Although not all machine learning is statistically based, computational statistics is an important source of the field's methods."
"the problems are solved by helping machines 'discover' their 'own' algorithms without needing to be explicitly told what to do by any human-developed algorithms."
"Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine."
"development of algorithms by human programmers would be cost-prohibitive"
"generative artificial neural networks have been able to surpass results of many previous approaches."
"Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning."
"Machine-learning approaches have been applied to...medicine."
"helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods."
"where it is too costly to develop algorithms to perform the needed tasks."
"Although not all machine learning is statistically based, computational statistics is an important source of the field's methods."