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. |
Check also these fantastic posts:
R Learning Path: From beginner to expert in R in 7 steps
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Latest:
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How to Get Started With Algorithmic Finance - Jan 23, 2020.
Algorithmic finance has been around for decades as a money-making tool, and it's not magic. Learn about some practical strategies along with and introduction to code you can use to get started. -
NLP Year in Review — 2019 - Jan 23, 2020.
In this blog post, I want to highlight some of the most important stories related to machine learning and NLP that I came across in 2019. -
The Data Science Interview Study Guide - Jan 22, 2020.
Preparing for a job interview can be a full-time job, and Data Science interviews are no different. Here are 121 resources that can help you study and quiz your way to landing your dream data science job. -
The 5 Most Useful Techniques to Handle Imbalanced Datasets - Jan 22, 2020.
This post is about explaining the various techniques you can use to handle imbalanced datasets. -
Random Forest — A Powerful Ensemble Learning Algorithm - Jan 22, 2020.
The article explains the Random Forest algorithm and how to build and optimize a Random Forest classifier. -
Explaining Black Box Models: Ensemble and Deep Learning Using LIME and SHAP - Jan 21, 2020.
This article will demonstrate explainability on the decisions made by LightGBM and Keras models in classifying a transaction for fraudulence, using two state of the art open source explainability techniques, LIME and SHAP. -
We Created a Lazy AI - Jan 20, 2020.
This article is an overview of how to design and implement reinforcement learning for the real world. -
Microsoft Introduces Project Petridish to Find the Best Neural Network for Your Problem - Jan 20, 2020.
The new algorithm takes a novel approach to neural architecture search. -
10 Python String Processing Tips & Tricks - Jan 20, 2020.
Pursuing a text analytics path but don't know where to start? Try this string processing primer to first gain an understanding of using Python to manipulate and process strings at a basic level. -
Handling Trees in Data Science Algorithmic Interview - Jan 16, 2020.
This post is about fast-tracking the study and explanation of tree concepts for the data scientists so that you breeze through the next time you get asked these in an interview. -
Disentangling disentanglement: Ideas from NeurIPS 2019 - Jan 15, 2020.
This year’s NEURIPS-2019 Vancouver conference recently concluded and featured a dozen papers on disentanglement in deep learning. What is this idea and why is it so interesting in machine learning? This summary of these papers will give you initial insight in disentanglement as well as ideas on what you can explore next. -
Classify A Rare Event Using 5 Machine Learning Algorithms - Jan 15, 2020.
Which algorithm works best for unbalanced data? Are there any tradeoffs? -
Geovisualization with Open Data - Jan 15, 2020.
In this post I want to show how to use public available (open) data to create geo visualizations in python. Maps are a great way to communicate and compare information when working with geolocation data. There are many frameworks to plot maps, here I focus on matplotlib and geopandas (and give a glimpse of mplleaflet). -
Survey Segmentation Tutorial - Jan 14, 2020.
Learn the basics of verifying segmentation, analyzing the data, and creating segments in this tutorial. When reviewing survey data, you will typically be handed Likert questions (e.g., on a scale of 1 to 5), and by using a few techniques, you can verify the quality of the survey and start grouping respondents into populations. -
Decision Tree Algorithm, Explained - Jan 14, 2020.
All you need to know about decision trees and how to build and optimize decision tree classifier. -
Idiot’s Guide to Precision, Recall, and Confusion Matrix - Jan 13, 2020.
Building Machine Learning models is fun, but making sure we build the best ones is what makes a difference. Follow this quick guide to appreciate how to effectively evaluate a classification model, especially for projects where accuracy alone is not enough. -
Uber Creates Generative Teaching Networks to Better Train Deep Neural Networks - Jan 13, 2020.
The new technique can really improve how deep learning models are trained at scale. -
An Introductory Guide to NLP for Data Scientists with 7 Common Techniques - Jan 9, 2020.
Data Scientists work with tons of data, and many times that data includes natural language text. This guide reviews 7 common techniques with code examples to introduce you the essentials of NLP, so you can begin performing analysis and building models from textual data. -
The Book to Start You on Machine Learning - Jan 9, 2020.
This book is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context. -
Stock Market Forecasting Using Time Series Analysis - Jan 9, 2020.
Time series analysis will be the best tool for forecasting the trend or even future. The trend chart will provide adequate guidance for the investor. So let us understand this concept in great detail and use a machine learning technique to forecast stocks. -
Top 5 must-have Data Science skills for 2020 - Jan 8, 2020.
The standard job description for a Data Scientist has long highlighted skills in R, Python, SQL, and Machine Learning. With the field evolving, these core competencies are no longer enough to stay competitive in the job market. -
Learning SQL the Hard Way - Jan 8, 2020.
Simply put: This post is about installing SQL, explaining SQL and running SQL. -
10 Python Tips and Tricks You Should Learn Today - Jan 8, 2020.
Check out this collection of 10 Python snippets that can be taken as a reference for your daily work. -
A Comprehensive Guide to Natural Language Generation - Jan 7, 2020.
Follow this overview of Natural Language Generation covering its applications in theory and practice. The evolution of NLG architecture is also described from simple gap-filling to dynamic document creation along with a summary of the most popular NLG models. -
Introducing Generalized Integrated Gradients (GIG): A Practical Method for Explaining Diverse Ensemble Machine Learning Models - Jan 7, 2020.
There is a need for a new way to explain complex, ensembled ML models for high-stakes applications such as credit and lending. This is why we invented GIG. -
3 common data science career transitions, and how to make them happen - Jan 6, 2020.
Breaking into a career in Data Science can depend on where you start. See if you fit into one of these three categories of "newbies," and then find out how to make your professional transition into the field. -
H2O Framework for Machine Learning - Jan 6, 2020.
This article is an overview of H2O, a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks. -
How to Convert a Picture to Numbers - Jan 6, 2020.
Reducing images to numbers makes them amenable to computation. Let's take a look at the why and the how using Python and Numpy. -
Beginner’s Guide to K-Nearest Neighbors in R: from Zero to Hero - Jan 3, 2020.
This post presents a pipeline of building a KNN model in R with various measurement metrics. -
Predict Electricity Consumption Using Time Series Analysis - Jan 2, 2020.
Time series forecasting is a technique for the prediction of events through a sequence of time. In this post, we will be taking a small forecasting problem and try to solve it till the end learning time series forecasting alongside.
December:
- Fighting Overfitting in Deep Learning
- 10 Best and Free Machine Learning Courses, Online
- Random Forest vs Neural Networks for Predicting Customer Churn
- Market Basket Analysis: A Tutorial
- What is Data Catalog and Why You Should Care?
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Google’s New Explainable AI Service
Google has started offering a new service for “explainable AI” or XAI, as it is fashionably called. Presently offered tools are modest, but the intent is in the right direction. - The Most In Demand Tech Skills for Data Scientists
- Alternative Cloud Hosted Data Science Environments
- Interpretability part 3: opening the black box with LIME and SHAP
- How to Convert an RGB Image to Grayscale
- Pedestrian Detection Using Non Maximum Suppression Algorithm
- Let’s Build an Intelligent Chatbot
- The Ultimate Guide to Model Retraining
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Build Pipelines with Pandas Using pdpipe
We show how to build intuitive and useful pipelines with Pandas DataFrame using a wonderful little library called pdpipe.
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Plotnine: Python Alternative to ggplot2
Python's plotting libraries such as matplotlib and seaborn does allow the user to create elegant graphics as well, but lack of a standardized syntax for implementing the grammar of graphics compared to the simple, readable and layering approach of ggplot2 in R makes it more difficult to implement in Python. - What just happened in the world of AI?
- Python Dictionary and Dictionary Methods
- Deploying a pretrained GPT-2 model on AWS
- Interpretability: Cracking open the black box, Part 2
- Intro to Grafana: Installation, Configuration, and Building the First Dashboard
- 5 Great New Features in Latest Scikit-learn Release
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10 Free Top Notch Machine Learning Courses
Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.
- 5 Techniques to Prevent Overfitting in Neural Networks
- Enabling the Deep Learning Revolution
- The Rise of User-Generated Data Labeling
- Popular Deep Learning Courses of 2019
- Vega-Lite: A grammar of interactive graphics
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Data Science Curriculum Roadmap
What follows is a set of broad recommendations, and it will inevitably require a lot of adjustments in each implementation. Given that caveat, here are our curriculum recommendations.
