Top KDnuggets tweets, Apr 24–30: Another 10 Free Must-Read Books for Machine Learning and Data Science; Top #DataScience & #MachineLearning Methods Used in 2018/19
Also: Data Visualization in Python: Matplotlib vs Seaborn; Data Science Project Flow for Startups; Pandas DataFrame Indexing; Best Data Visualization Techniques for small and large data; The most desired skill in #DataScience
Most popular @KDnuggets tweets for Apr 24 - 30 were:
Most Retweeted:
Top #DataScience & #MachineLearning Methods Used in 2018/19:
1. Regression
2. Decision Trees /Rules
3. Clustering
4. Visualization
5. Random Forests
6. Statistics - Descriptive
7. K-Nearest Neighbors
8. Time Series
9. Ensemble Methods
10. Text Mining
https://t.co/w57JAZYRKZhttps://t.co/TPPRanID24
Top #DataScience & #MachineLearning Methods Used in 2018/19:
1. Regression
2. Decision Trees /Rules
3. Clustering
4. Visualization
5. Random Forests
6. Statistics - Descriptive
7. K-Nearest Neighbors
8. Time Series
9. Ensemble Methods
10. Text Mining
https://t.co/w57JAZYRKZhttps://t.co/TPPRanID24
Pandas DataFrame Indexing by @_brohrer_
"The goal of this post is identify a single strategy for pulling data from a DataFrame using the Pandas Python library that is straightforward to interpret and produces reliable results."
https://t.co/LdqxrhVf06https://t.co/4VMYeKaRFV
How to avoid pitfalls when using #MachineLearning for time series forecasting:
“Defining the model to predict the difference in values between time steps rather than the value itself, is a much stronger test of the models predictive powers.” https://t.co/VHtcrM3EjOhttps://t.co/iWsXPan2U4
Google Experiment Destroyed Assumptions of Representation Learning - it showed that unsupervised learning of disentangled representations is impossible without inductive biases both on the learning approaches and the data sets #MachineLearning
https://t.co/JaiWNJXO3Thttps://t.co/WpBRFBqh2p