I’m not saying you need to be an expert in advanced calculus to do machine learning…
BUT, there is a big difference between someone that does vs someone that does NOT have a good foundation in stats when it comes to getting & explaining business results.
My thought process back in the day was to obtain a great foundation in stats and machine learning at the same time.
So here’s what helped me. I read a ton of books.
Here are the 3 books that helped me learn data science the most...
1. R for Data Science (Wickham & Grolemund) r4ds.had.co.nz
K-means is an essential algorithm for Data Science.
But it's confusing for beginners.
Let me demolish your confusion:
1. K-Means
K-means is a popular unsupervised machine learning algorithm used for clustering. It's a core algorithm used for customer segmentation, inventory categorization, market segmentation, and even anomaly detection.
2. Unsupervised:
K-means is an unsupervised algorithm used on data with no labels or predefined outcomes. The goal is not to predict a target output, but to explore the structure of the data by identifying patterns, clusters, or relationships within the dataset.
Principal Component Analysis (PCA) is the gold standard in dimensionality reduction.
But almost every beginner struggles understanding how it works (and why to use it).
In 3 minutes, I'll demolish your confusion:
1. What is PCA?
PCA is a statistical technique used in data analysis, mainly for dimensionality reduction. It's beneficial when dealing with large datasets with many variables, and it helps simplify the data's complexity while retaining as much variability as possible.
2. How PCA Works:
PCA has 5 steps; Standardization, Covariance Matrix Computation, Eigen Vector Calculation, Choosing Principal Components, and Transforming the data.
🚨BREAKING: New Python library for Bayesian Marketing Mix Modeling and Customer Lifetime Value
It's called PyMC Marketing.
This is what you need to know: 🧵
1. What is PyMC Marketing?
PyMC-Marketing is a state-of-the-art Bayesian modeling library that's designed for Marketing Mix Modeling (MMM) and Customer Lifetime Value (CLV) prediction.
2. Benefits
- Incorporate business logic into MMM and CLV models
- Model carry-over effects with adstock transformations
- Understand the diminishing returns
- Incorporate time series and decay
- Causal identification
In 3 minutes, I'll share 3 weeks of research on Random Forest.
Let's go:
1. What is a Random Forest?
Random Forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Each tree in the random forest gives a prediction, and the most voted prediction is considered as the final result.
2. Bagging (Bootstrap Aggregations):
Each tree is trained on a random subset of the data (sampling of data points) instead of the entire training dataset. This technique is called "bootstrap aggregating" or "bagging".