YOLACT : A simple, fully convolutional model for real-time instance segmentation.

Source Code ———»»» https://github.com/dbolya/yolact
Paper Link ————»»» https://arxiv.org/pdf/1904.02689.pdf

#machineLearning #python #pytorch #realtime #instance-segmentation #AI #DataScience

CS109 Data Science- Harvard:
Predicting Hubway Stations Status by Lauren Alexander, Gabriel Goulet-Langlois, Joshua Wolff

Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries.

We will be using Python for all programming assignments and projects. All lectures will be posted here and should be available 24 hours after meeting time.

#DataScience #Course #Training #Harvard

Everybody’s Talkin’: Let Me Talk as You Want :
A method to edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video.

Source Code ———»»» https://wywu.github.io/projects/EBT/EBT.html
Paper Link ————»»» https://arxiv.org/pdf/2001.05201.pdf

#machineLearning #AI #DataScience

Synergetic Reconstruction from 2D Pose and 3D Motion for Wide-Space Multi-Person Video Motion Capture in the Wild
A markerless motion capture method with spatiotemporal accuracy and smoothness from multiple cameras, even in wide and multi-person environments.

Source Code ———»»» http://www.ynl.t.u-tokyo.ac.jp/research/vmocap-syn/
Paper Link ————»»» https://arxiv.org/pdf/2001.05613.pdf

#machineLearning #AI #DataScience

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A Complete Pandas Glossary for Data Science
Your go-to resource to learn Pandas fundamentals


#python #pandas #DataScience #machineLearning #tutorial

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Data Science Essentials - By Microsoft
Explore data visualization and exploration concepts with experts from MIT and Microsoft, and get an introduction to machine learning.

🔥 Free Training 🔥 —————— Pay only if you want a certificate

#DataScience #tutorial #beginners #free #Course

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CS109 Data Science - Harvard 🔥 Free Training 🔥

Prerequisites: Python (Only Python is used throughout the course), fundamental knowledge of how the data science libraries work.

About the course:
This data science course offered by Harvard isn’t on a platform like edX or Coursera and doesn’t provide any certification. This might be one of the best courses for beginners to get started with data science and is completely free.
The course contains a list of videos, lecture slides, lab videos & notebook and is a 13-week intensive program that wrapping up course content: (Listed in order of the course chronology)

* Web Scraping, Regular Expressions, Data Reshaping, Data Cleanup, Pandas.
* Exploratory Data Analysis
* Pandas, SQL and the Grammar of Data
* Statistical Models
* Storytelling and Effective Communication
* Bias and Regression (With more regression)
* Classification, k-Nearest Neighbors, Cross-Validation, Dimensionality Reduction, PCA, MDS
* SVM, Evaluation
* Decision Trees and Random Forests
* Ensemble Methods and Best Practices
* Best Practices and Recommendations
* MapReduce, Spark
* Bayes Theorem, Bayesian Methods and Text Data
* Clustering
* Effective Presentations
* Experimental Design
* Deep Networks
* Guest Lecture: Building Data Science
#DataScience #tutorial #beginners #free #Course

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Essential Math for Machine Learning: Python Edition
Learn the essential mathematical foundations for machine learning and artificial intelligence.


🔥 Free Training 🔥 —————— Pay only if you want a certificate

#python #DataScience #tutorial #beginners #free #maths #machineLearning

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Mathematics for Machine Learning - by Garrett Thomas
Machine learning uses tools from a variety of mathematical fields. This document is an attempt to
provide a summary of the mathematical background needed for an introductory class in machine
learning, which at UC Berkeley is known as CS 189/289A.

🔥 Free eBook 🔥

#DataScience #ebook #beginners #free #machineLearning

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Introduction to Data Science
Data Science is the hottest field of the century. Learn more about why data science, artificial intelligence (AI) and machine learning are revolutionizing the way people do business and research around the world. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.


🔥 Free Training 🔥

#DataScience #tutorial #beginners #free #machineLearning #Course

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Data Science Tools
Get started with some of the most popular tools for collaborative data science, including RStudio IDE, Jupyter Notebooks, Apache Zeppelin notebooks, and IBM Watson Studio. Use the tools directly on Skills Network Labs, a cloud lab environment that brings powerful open data science tools together so you can analyze, visualize, explore, clean data, run models and create apps.

🔥 Free Training 🔥

#DataScience #tutorial #beginners #free #machineLearning #Course #tools

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Data Science Methodology
This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.
Accordingly, in this course, you will learn:
* The major steps involved in tackling a data science problem.
* The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
* How data scientists think!

🔥 Free Training 🔥

#DataScience #tutorial #beginners #free #machineLearning #Course

Machine Learning Cheat Sheet
Classical equations, diagrams and tricks in machine learning

This cheat sheet is a condensed version of machine learning manual, which contains many classical equations and
diagrams on machine learning, and aims to help you quickly recall knowledge and ideas in machine learning.

#eBook #ML #machineLearning #DataScience #AI #DataMining #DeepLearning #Algorithms #AppliedMathematics

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An Introduction to Machine Learning by Miroslav Kubat

FREE ebook on Machine Learning

 An introduction to machine learning book will get you started with various data science techniques such as decision trees, performance evaluation, among others. It also covers sub-categories such as unsupervised learning, reinforcement learning, and neural networks. Learners can obtain a detailed understanding of various classifiers and algorithms from 17 chapters, thereby making it a good read during the lockdown.

https://link.springer.com/book/10.1007%2F978-3-319-63913-0
#machineLearning #DataScience #eBook

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All of Statistics by Larry Wasserman
A Concise Course in Statistical Inference

FREE ebook on Statistics for Machine Learning

 A proper grasp of statistics is essential for any machine learning enthusiast to succeed in the competitive domain. Consequently, one should focus more on statistics than on the latest fancy techniques. The book — All of Statistics — consists of 24 chapters and covers every topic right from probability to statistical inference and statistical models and methods.

https://link.springer.com/content/pdf/10.1007%2F978-0-387-21736-9.pdf
#machineLearning #DataScience #eBook #statistics

Introduction To Time Series And Forecasting
by Peter J. BrockwellRichard A. Davis

FREE eBook

 Time series analysis has gain popularity due to its use cases in financial data, especially, in companies’ stock prediction, fraud detection in transactions, and more. Besides, it is now becoming an essential technique in data science due to the rise of streaming analytics in numerous business operations. Obtaining real-time insights while also comparing with the historical trends allow companies to quickly make informed decisions. 

The book includes 11 chapters on a wide range of time series techniques such as nonstationary and seasonal time series models, multivariate time series, state-space models, and forecasting methods.

https://link.springer.com/content/pdf/10.1007%2F978-3-319-29854-2.pdf
#machineLearning #DataScience #eBook #statistics

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Principles Of Data Mining
by Principles of Data Mining

 With more than 2.3 million downloads, this is one of the most popular data science books. The process of data mining has increasingly become essential for businesses to achieve rapidly grow by making decisions based on insights. Therefore, this book includes all principles for data mining to discover patterns in a colossal amount of data. Designed to help even the beginners, it covers an introduction to classification like Naive Bayes, Nearest Neighbour, Decision Tree, among others, along with detailed explanations.

https://link.springer.com/content/pdf/10.1007%2F978-1-4471-7307-6.pdf
#machineLearning #DataScience #eBook #statistics

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Neural Networks And Deep Learning
by Charu C. Aggarwal

FREE eBook

 It is a comprehensive book on deep learning for every aspirant as well as working professionals. The book covers both classical as well as the latest models in deep learning, thereby explaining the difference between several techniques and their effectiveness. The book also focuses on the right practices for AI agents to generalise. Besides, it has lessons on various neural networks like RNN, CNN, DRL, other advanced topics in deep learning.

https://link.springer.com/content/pdf/10.1007%2F978-3-319-94463-0.pdf
#machineLearning #DataScience #eBook #deepLearning #neuralNetworks

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Recommender Systems
by Charu C. Aggarwal

Free eBook

 Today, companies are delivering personalised experiences to their users for enhancing customer experience. Such practices are widely common on social media platforms and e-commerce websites, however, now it is being democratised across all business to increase conversion rate. The book has categorised the learning in three sub-section: algorithms and evaluation, recommendations in specific domains and contexts, advanced topics and applications. 

https://link.springer.com/content/pdf/10.1007%2F978-3-319-29659-3.pdf
#machineLearning #DataScience #eBook #algorithm

Computer Vision
Algorithms and Applications
by Richard Szeliski

Free eBook

 Computer vision technology is playing a crucial role in data science by expediting various processes such as analysing medical imaging, assisting in the development of self-driving cars, and determining defects in manufacturing processes. It can be applied in endless processes to simplify the life of humans. However, there are various challenges in computer vision technology while interpreting 3D images as well as delivers biased results. This book explains the ins and outs of the computer vision along with lessons on various techniques.

https://link.springer.com/content/pdf/10.1007%2F978-1-84882-935-0.pdf
#machineLearning #DataScience #eBook #algorithm #computerVision