Code-Base-for-Research

This is coding samples which can be used for research purpose

Don't forget to hit the ⭐ if you like this repo.

Machine Learning

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine learning algorithms are used in a wide range of applications, such as image and speech recognition, natural language processing, and control systems, and have seen increasing success in areas such as healthcare, finance, and advertising. There are several types of machine learning, including:

Supervised learning: The computer is provided with example inputs and their corresponding outputs, and the goal is to learn a general rule that maps inputs to outputs. For example, a supervised learning algorithm for email filtering may be trained on a dataset of labeled emails as spam or not-spam and can use that training to classify new emails.
Unsupervised learning: The computer is not provided with example outputs, and the goal is to discover interesting structure in the input data, such as grouping similar examples together. For example, an unsupervised learning algorithm for customer segmentation may be trained on a dataset of customer information and finds patterns that group customers into similar segments.
Reinforcement learning: The computer learns to make a sequence of decisions. The learning algorithm is provided with feedback in the form of rewards or penalties, in a process called trial-and-error, to improve its future decision making.
Semi-supervised learning: It's a combination of supervised and unsupervised, where large amount of unlabelled data and a small amount of labeled data is used.

To give a simple example, consider a spam filter for email. A spam filter uses machine learning to classify emails as spam or not spam. The filter is trained on a dataset of labeled emails (example inputs with corresponding outputs), and it uses that training to classify new, previously unseen emails. In this case, the inputs are the emails, and the outputs are "spam" or "not spam." Once the filter has been trained, it can classify new emails as they arrive, with minimal human intervention.

Deep learning

Deep learning is a subfield of machine learning that is inspired by the structure and function of the brain, specifically the neural networks. These neural networks are made up of layers of interconnected nodes, called artificial neurons, which are used to analyze and process complex data, such as images and sound. Deep learning algorithms can be used for a wide range of tasks, including image and speech recognition, natural language processing, and decision making. There are several types of deep learning algorithms, each with its own strengths and use cases. Some of the most popular types include:

Convolutional Neural Networks (CNNs): These are commonly used for image and video analysis tasks, such as object recognition, facial recognition, and image segmentation.
Recurrent Neural Networks (RNNs): These are used for sequential data, such as time series or natural language processing tasks, such as language translation or text-to-speech synthesis.
Generative Adversarial Networks (GANs): These are used for generative tasks, such as creating new images, text, or music that resemble existing data.
Autoencoders: These are neural networks that are trained to reconstruct their input, allowing them to learn a compact representation of the data, useful for dimensionality reduction and unsupervised feature learning
Transformer Networks: These are neural networks architectures that excel at handling sequential data, they are mainly used in natural language processing tasks, and have shown excellent results in many fields such as text-to-speech, machine translation, etc.
Self-Organizing Maps: These networks try to project high-dimensional data into lower-dimensional representations while preserving the topological properties of the data. Each type of algorithm has its own strengths and weaknesses and can be used for different types of tasks. The choice of which algorithm to use for a specific task will depend on the nature of the data and the specific problem that needs to be solved.

Feature Selection

Feature selection is a process in data science that involves selecting a subset of relevant features (variables, predictors, descriptors, etc.) from a larger set of features to use in building a predictive model. The goal of feature selection is to select a set of features that are most relevant and useful for the model, while minimizing the number of features that are included. There are several reasons why feature selection is important in data science:

Reduce overfitting: By reducing the number of features in the model, you can reduce the risk of overfitting, which occurs when the model is too complex and fits the training data too well, but does not generalize well to new data.
Improve model interpretability: By selecting a smaller set of features, you can make the model more interpretable and easier to understand.
Improve model performance: By selecting the most relevant and useful features, you can improve the performance of the model, such as its accuracy or speed. There are several techniques that can be used for feature selection in data science, including univariate selection, feature importance, and recursive feature elimination.

Data Visualization

There are several techniques you can use to visualize data using Python. Some common options include:

Matplotlib: Matplotlib is a popular library for data visualization in Python. It provides a range of plotting functions that can be used to create a variety of graphs, including line plots, scatter plots, bar plots, histograms, and pie charts.
Seaborn: Seaborn is a library for data visualization in Python that is built on top of Matplotlib. It provides a range of advanced plotting functions and features, such as heatmaps, pair plots, and violin plots.
Plotly: Plotly is an open-source library for data visualization in Python that provides a range of interactive plots and charts. It is particularly useful for creating online dashboards or interactive visualizations that can be shared and embedded in websites.
Bokeh: Bokeh is a library for data visualization in Python that is designed to create interactive plots and charts. It is particularly useful for creating web-based visualizations that can be easily embedded in websites.
Altair: Altair is a library for data visualization in Python that is based on the Vega-Lite visualization grammar. It is particularly useful for creating interactive visualizations that can be easily customized and exported to various formats.

The source of the following machine learning topics map is this wonderful blog post

Contribution 🛠️

Please create an Issue for any improvements, suggestions or errors in the content.

goldboy225 / code-base-for-research Goto Github PK