Machine Learning & Artificial Intelligence
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Math
Linear Algebra
Linear Algebra is a branch of mathematics that uses symbols and letters to represent numbers and study their relationships.
Calculus
Calculus is a branch of mathematics that studies continuous change, focusing on concepts like rates of change (differentiation) and accumulation of quantities (integration).
Statistics
Statistics is the science of collecting, analyzing, interpreting, and presenting data to understand patterns, make inferences, and draw conclusions.
Probability
Probability is a branch of mathematics that deals with the likelihood of events occurring.
Programming
Python
Python is a high-level, general-purpose programming language known for its readability and versatility. It's widely used in various fields, including data science, web development, and artificial intelligence..
Numpy
NumPy is a fundamental Python library for scientific computing, providing efficient support for large, multi-dimensional arrays and matrices, along with a wide range of mathematical functions to operate on them.
Pandas
Pandas is a powerful Python library for data manipulation and analysis. Pandas offers a wide range of tools for data cleaning, transformation, aggregation, and visualization, making it a cornerstone for data science tasks in Python.
Matplotlib
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It offers a wide range of plot types, customization options, and integration capabilities, making it a versatile tool for data visualization and exploration.
Basic Machine Learning
Linear Regression
Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. It aims to find the best-fitting line that minimizes the discrepancies between predicted and actual output values.
Logistic Regression
Logistic regression is a statistical method used to predict the probability of an event occurring, such as whether a customer will churn or a patient will develop a certain disease. It is particularly useful when the outcome variable is binary (e.g., yes/no, success/failure).
Support Vector Machine (SVM)
A Support Vector Machine (SVM) is a powerful machine learning algorithm used for both classification and regression tasks. It's particularly effective in high-dimensional spaces and when the number of features might exceed the number of samples.
Decision Trees
A decision tree is a machine learning algorithm used for both classification and regression tasks. It resembles a flowchart-like structure where each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label or a continuous value.
Random Forests
A random forest is a powerful machine learning algorithm that combines the strengths of multiple decision trees to create a more accurate and robust model.
Dimensionality Reduction
Dimensionality reduction is the process of reducing the number of features (or dimensions) in a dataset while retaining as much important information as possible.
Clustering (Unsupervised Learning)
Clustering is an unsupervised machine learning technique that groups unlabeled data points based on their similarities, aiming to discover underlying patterns and structures within the data.
Advanced Machine Learning
Neural Networks and Deep Learning
Neural networks, also known as artificial neural networks (ANNs), are a type of machine learning model which consists of interconnected nodes or neurons arranged in layers, allowing them to process information and learn complex patterns. .
Computer Vision
Computer vision is a field of artificial intelligence (AI) that enables computers to understand, analyze, and interpret visual information in the same way humans do. It empowers computers to process, comprehend, and extract meaningful insights from images, videos, and even real-time visual streams.
Recurrent Neural Networks
Recurrent Neural Networks (RNNs) are a type of artificial neural network specifically designed to process sequential data. Unlike traditional feedforward neural networks, which process data in a single pass, RNNs have connections that loop back on themselves, allowing them to "remember" past inputs. This makes them well-suited for tasks involving sequences.
Natural Language Processing
Natural Language Processing (NLP) is a subfield of computer science and artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. It aims to bridge the gap between human communication and machine comprehension.
Autoencoders
Autoencoders are a type of artificial neural network that are designed to efficiently compress (encode) input data down to its essential features, then reconstruct (decode) the original input from this compressed representation.
Generative Adversarial Networks
Generative Adversarial Networks (GANs) are a powerful class of machine learning models that use a competitive approach to generate new data.
Diffusion Models
Diffusion models are a class of generative AI models that have gained significant attention for their ability to generate high-quality images, audio, and other forms of data.
Reinforcement Learning
Reinforcement Learning (RL) is a powerful machine learning technique that trains software agents to make decisions by interacting with an environment. It's inspired by how animals learn through trial and error, using rewards and punishments to guide their behavior.
Tensorflow
TensorFlow is an open-source machine learning library developed by Google. It provides a flexible framework for building and deploying a wide range of machine learning models, including deep neural networks..
Extras
Linux
Linux is a family of open-source Unix-like operating systems based on the Linux kernel, an operating system kernel first released on September 17, 1991, by Linus Torvalds.