Probabilistic Graphical Model

Probabilistic Graphical Model
English | Size: 2.19 GB
Category: Tutorial

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

Packt – Spring Boot Fundamentals-XQZT

Packt – Spring Boot Fundamentals-XQZT
English | Size: 2.43 GB
Category: Tutorial

Key Features
Learn how Spring Boot simplifies Java application development
Create and run several Spring Boot applications from scratch
Become well-versed with Spring Boot while using various Spring modules

Packt – Fundamentals of Data Science with Python-

Packt – Fundamentals of Data Science with Python-XQZT
English | Size: 433.11 MB
Category: Tutorial

Implement powerful data science techniques with Python using NumPy, SciPy, Matplotlib, and scikit-learn

Packt – Regression Modeling with Statistics and Machine Learning in Python

Packt – Regression Modeling with Statistics and Machine Learning in Python-ZH
English | Size: 804.01 MB
Category: Tutorial

Key Features
Minimal mathematical jargon. The course focuses on teaching you the most important Python data science concepts and packages, including Pandas
Implement clustering and classification models on data
Gain a thorough grounding in data science and understand which models should be used, and when.