May2019 Jun Tutorials, Overviews
All (47) | Courses, Education (1) | Meetings (2) | News (3) | Opinions (13) | Top Stories, Tweets (6) | Tutorials, Overviews (21) | Webcasts & Webinars (1)
- Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS
- Jun 17, 2019.
Data science jobs continue to grow in 2019, and this report shares the change and spread of jobs by software over recent years.
- How to Use Python’s datetime
- Jun 17, 2019.
Python's datetime package is a convenient set of tools for working with dates and times. With just the five tricks that I’m about to show you, you can handle most of your datetime processing needs.
- The Machine Learning Puzzle, Explained
- Jun 17, 2019.
Lots of moving parts go into creating a machine learning model. Let's take a look at some of these core concepts and see how the machine learning puzzle comes together.
- Become a Pro at Pandas, Python’s Data Manipulation Library
- Jun 13, 2019.
Pandas is one of the most popular Python libraries for cleaning, transforming, manipulating and analyzing data. Learn how to efficiently handle large amounts of data using Pandas.
- Scalable Python Code with Pandas UDFs: A Data Science Application
- Jun 13, 2019.
There is still a gap between the corpus of libraries that developers want to apply in a scalable runtime and the set of libraries that support distributed execution. This post discusses how to bridge this gap using the the functionality provided by Pandas UDFs in Spark 2.3+
- All Models Are Wrong – What Does It Mean?
- Jun 12, 2019.
During your adventures in data science, you may have heard “all models are wrong.” Let’s unpack this famous quote to understand how we can still make models that are useful.
- Overview of Different Approaches to Deploying Machine Learning Models in Production
- Jun 12, 2019.
Learn the different methods for putting machine learning models into production, and to determine which method is best for which use case.
- How to Automate Hyperparameter Optimization
- Jun 12, 2019.
A step-by-step guide into performing a hyperparameter optimization task on a deep learning model by employing Bayesian Optimization that uses the Gaussian Process. We used the gp_minimize package provided by the Scikit-Optimize (skopt) library to perform this task.
- 3 Main Approaches to Machine Learning Models
- Jun 11, 2019.
Machine learning encompasses a vast set of conceptual approaches. We classify the three main algorithmic methods based on mathematical foundations to guide your exploration for developing models.
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If you’re a developer transitioning into data science, here are your best resources
- Jun 11, 2019.
This article will provide a background on the data scientist role and why your background might be a good fit for data science, plus tangible stepwise actions that you, as a developer, can take to ramp up on data science. - 5 Ways to Deal with the Lack of Data in Machine Learning
- Jun 10, 2019.
Effective solutions exist when you don't have enough data for your models. While there is no perfect approach, five proven ways will get your model to production.
- Choosing an Error Function
- Jun 10, 2019.
The error function expresses how much we care about a deviation of a certain size. The choice of error function depends entirely on how our model will be used.
- Random Forests vs Neural Networks: Which is Better, and When?
- Jun 7, 2019.
Random Forests and Neural Network are the two widely used machine learning algorithms. What is the difference between the two approaches? When should one use Neural Network or Random Forest?
- PyViz: Simplifying the Data Visualisation Process in Python
- Jun 6, 2019.
There are python libraries suitable for basic data visualizations but not for complicated ones, and there are libraries suitable only for complex visualizations. Is there a single library that handles both these tasks efficiently? The answer is yes. It's PyViz
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Jupyter Notebooks: Data Science Reporting
- Jun 6, 2019.
Jupyter does bring us some benefits of being able to organize code but many of us still find ourselves with messy and unnecessary code chunks. Here are some ways including a NEW EXTENSION that anyone can use to begin organizing your code on your notebooks. - Mongo DB Basics
- Jun 5, 2019.
Mongo DB is a document oriented NO SQL database unlike HBASE which has a wide column store. The advantage of Document oriented over relation type is the columns can be changed as an when required for each case as opposed to the same column name for all the rows.
- The Whole Data Science World in Your Hands
- Jun 5, 2019.
Testing MatrixDS capabilities on different languages and tools: Python, R and Julia. If you work with data you have to check this out.
- How to choose a visualization
- Jun 4, 2019.
Visualizations based on the structure of data are needed during analysis, which might be different than for the end user. A new guide for choosing the right visualization helps you flexibly understand the data first.
- Separating signal from noise
- Jun 4, 2019.
When we are building a model, we are making the assumption that our data has two parts, signal and noise. Signal is the real pattern, the repeatable process that we hope to capture and describe. The noise is everything else that gets in the way of that.
- The Hitchhiker’s Guide to Feature Extraction
- Jun 3, 2019.
Check out this collection of tricks and code for Kaggle and everyday work.
- 7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition
- Jun 3, 2019.
This is the second part of this new learning path series for mastering machine learning with Python. Check out these 7 steps to help master intermediate machine learning with Python!