An Introduction to Statistical Learning with Applications in R - Gareth J.et al. Python Machine Learning- Sebastian Raschka Programming Collective Intelligence (集体编程智慧) - Toby Segaran 机器学习 - 周志华 统计学习方法 - 李航 最近我阅读了上面的书籍,想和大家分享一下我的主观评价。在每本书的总评...
and other hardware. Traditionally, to program these devices, you had to use low-level languages like assembler or C++, and sacrifice a lot of functionality. That all changed with the introduction ofMicroPython, a version of Python 3 crammed into the tiny capacity of smaller physical computing de...
Data Science Courses Big Data Courses Machine Learning Courses Python Courses Statistical Analysis Courses Information Visualization Courses Data Wrangling Courses Data Manipulation Courses MapReduce Courses Overview The Introduction to Data Science class will survey the foundational topics in data...
Introduction to Graph Machine Learning /blog/assets/125_intro-to-graphml/thumbnail.png user clefourrier Introduction to Graph Machine Learning In this blog post, we cover the basics of graph machine learning. We first study what graphs are, why they are used, and how best to represent them...
1. Intro_to_Algorithms https://classroom.udacity.com/courses/cs215 2. Computability, Complexity & Algorithms https://classroom.udacity.com/courses/ud061 3. Data Structures & Algorithms in Python https://classroom.udacity.com/courses/ud513 4. Introduction to Graduate Algorithms https://classroom....
Pythonexamples are provided in all cases, mostly through thepyAudioAnalysislibrary. All examples are also provided inthisgithub repo. With regards to the involved ML methodologies, this article focuses on hand-crafted audio features and traditional statistical classifiers such as SVMs. Deep audio metho...
Some of the Head First learning principles: Make it visual. Images are far more memorable than words alone, and make learning much more effective (up to 89% improvement in recall and transfer studies). It also makes things more understandable. Put the words within or near the graphics they ...
Learn what it takes to become a data scientist. The Introduction to Data Science class will survey the foundational topics in data science, namely: Data Manipulation; Data Analysis with Statistics and Machine Learning; Data Communication with Information
Hands-on Time Series Anomaly Detection using Autoencoders, with Python Data Science Here’s how to use Autoencoders to detect signals with anomalies in a few lines of… Piero Paialunga August 21, 2024 12 min read Machine Learning Feature engineering, structuring unstructured ...
In this chapter, we will tell you a little bit more about what to expect in this book, introduce the key concepts behind deep learning, and train our first models on different tasks. It doesn't matter if you don't come from a technical or a mathematical background (though it's okay ...