Tutorials, Overviews
DataCamp - Easiest Way to Learn Data Science
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Learning R? Take this Intro to R for Data Science Tutorial. 
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Learning Python? Take this Intro to Python for Data Science Tutorial. |
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R Learning Path: From beginner to expert in R in 7 steps
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Latest:
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Implementing MLOps on an Edge Device - Aug 4, 2020.
This article introduces developers to MLOps and strategies for implementing MLOps on edge devices. -
Setting Up Your Data Science & Machine Learning Capability in Python - Aug 4, 2020.
With the rich and dynamic ecosystem of Python continuing to be a leading programming language for data science and machine learning, establishing and maintaining a cost-effective development environment is crucial to your business impact. So, do you rent or buy? This overview considers the hidden and obvious factors involved in selecting and implementing your Python platform. -
5 Apache Spark Best Practices For Data Science - Aug 4, 2020.
Check out these best practices for Spark that the author wishes they knew before starting their project. -
Announcing PyCaret 2.0 - Aug 3, 2020.
PyCaret 2.0 has been released! Find out about all of the updates and see examples of how to use them right here. -
The Machine Learning Field Guide - Aug 3, 2020.
This straightforward guide offers a structured overview of all machine learning prerequisites needed to start working on your project, including the complete data pipeline from importing and cleaning data to modelling and production. -
Know What Employers are Expecting for a Data Scientist Role in 2020 - Aug 3, 2020.
The analysis is done from 1000+ recent Data scientist jobs, extracted from job portals using web scraping.
July:
- Fuzzy Joins in Python with d6tjoin
- R squared Does Not Measure Predictive Capacity or Statistical Adequacy
- Scaling Computer Vision Models with Dataflow
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Awesome Machine Learning and AI Courses
Check out this list of awesome, free machine learning and artificial intelligence courses with video lectures. - A Complete Guide To Survival Analysis In Python, part 3
- 5 Big Trends in Data Analytics
- A Tour of End-to-End Machine Learning Platforms
- First Steps of a Data Science Project
- Why You Should Get Google’s New Machine Learning Certificate
- 5 Fantastic Natural Language Processing Books
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Essential Resources to Learn Bayesian Statistics
If you are interesting in becoming better at statistics and machine learning, then some time should be invested in diving deeper into Bayesian Statistics. While the topic is more advanced, applying these fundamentals to your work will advance your understanding and success as an ML expert. - Building a Content-Based Book Recommendation Engine
- Deep Learning for Signal Processing: What You Need to Know
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Computational Linear Algebra for Coders: The Free Course
Interested in learning more about computational linear algebra? Check out this free course from fast.ai, structured with a top-down teaching method, and solidify your understanding of an important set of machine learning-related concepts.
- Labelling Data Using Snorkel
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Easy Guide To Data Preprocessing In Python
Preprocessing data for machine learning models is a core general skill for any Data Scientist or Machine Learning Engineer. Follow this guide using Pandas and Scikit-learn to improve your techniques and make sure your data leads to the best possible outcome.
- Better Blog Post Analysis with googleAnalyticsR
- Powerful CSV processing with kdb+
- Apache Spark Cluster on Docker
- Building a REST API with Tensorflow Serving (Part 2)
- Data Mining and Machine Learning: Fundamental Concepts and Algorithms: The Free eBook
- Recurrent Neural Networks (RNN): Deep Learning for Sequential Data
- How to Handle Dimensions in NumPy
- Demystifying Statistical Significance
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Wrapping Machine Learning Techniques Within AI-JACK Library in R
The article shows an approach to solving problem of selecting best technique in machine learning. This can be done in R using just one library called AI-JACK and the article shows how to use this tool.
- Free From Stanford: Ethical and Social Issues in Natural Language Processing
- Before Probability Distributions
- 3 Advanced Python Features You Should Know
- Understanding How Neural Networks Think
- Apache Spark on Dataproc vs. Google BigQuery
- Building a REST API with Tensorflow Serving (Part 1)
- Clustering Uber Rideshare Data
- A Complete Guide To Survival Analysis In Python, part 2
- Auto Rotate Images Using Deep Learning
- Foundations of Data Science: The Free eBook
- 7 Signs you are data literate
- PyTorch LSTM: Text Generation Tutorial
- 5 Things You Don’t Know About PyCaret
- Understanding Time Series with R
- Pull and Analyze Financial Data Using a Simple Python Package
- Spam Filter in Python: Naive Bayes from Scratch
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Free MIT Courses on Calculus: The Key to Understanding Deep Learning
Calculus is the key to fully understanding how neural networks function. Go beyond a surface understanding of this mathematics discipline with these free course materials from MIT.
- Some Things Uber Learned from Running Machine Learning at Scale
- A Complete Guide To Survival Analysis In Python, part 1
- PyTorch for Deep Learning: The Free eBook
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A Layman’s Guide to Data Science. Part 3: Data Science Workflow
Learn and appreciate the typical workflow for a data science project, including data preparation (extraction, cleaning, and understanding), analysis (modeling), reflection (finding new paths), and communication of the results to others.
- Exploratory Data Analysis on Steroids
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Deploy Machine Learning Pipeline on AWS Fargate
A step-by-step beginner’s guide to containerize and deploy ML pipeline serverless on AWS Fargate.
- Generating cooking recipes using TensorFlow and LSTM Recurrent Neural Network: A step-by-step guide
- Feature Engineering in SQL and Python: A Hybrid Approach
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Getting Started with TensorFlow 2
Learn about the latest version of TensorFlow with this hands-on walk-through of implementing a classification problem with deep learning, how to plot it, and how to improve its results.
- PyTorch Multi-GPU Metrics Library and More in New PyTorch Lightning Release
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Speed up your Numpy and Pandas with NumExpr Package
We show how to significantly speed up your mathematical calculations in Numpy and Pandas using a small library.
- Data Cleaning: The secret ingredient to the success of any Data Science Project
