π¨ BREAKING: IBM launches a free Python library that converts ANY document to data
Introducing Docling. Here's what you need to know: π§΅
1. What is Docling?
Docling is a Python library that simplifies document processing, parsing diverse formats β including advanced PDF understanding β and providing seamless integrations with the gen AI ecosystem.
2. Document Conversion Architecture
For each document format, the document converter knows which format-specific backend to employ for parsing the document and which pipeline to use for orchestrating the execution, along with any relevant options.
3. PDF Conversion to Markdown
Here is an example of the DocLayNet paper from arXiv, converted into Markdown format by Docling.
4. Core Technology:
Docling includes:
- PDF Backends for parsing
- Layout Analysis Model
- Vision-Based Table Formatter
- OCR for Text
5. Every data analyst, data scientist, and data engineer needs to learn Generative AI
99% of them are overlooking AI. This is a massive opportunity for you.
I'd like to help.
On Wednesday, August 20th, I'm sharing one of my best AI/ML Projects for FREE:
How I built an AI Customer Segmentation Agent with Python:
These 7 statistical analysis concepts have helped me as an AI Data Scientist.
Let's go: π§΅
Step 1: Learn These Descriptive Statistics
Mean, median, mode, variance, standard deviation. Used to summarize data and spot variability. These are key for any data scientist to understand whatβs in front of them in their data sets.
2. Learn Probability
Know your distributions (Normal, Binomial) & Bayesβ Theorem. The backbone of modeling and reasoning under uncertainty. Central Limit Theorem is a must too.
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.
Type 1 and Type 2 errors are confusing. In 3 minutes, I'll demolish your confusion. Let's dive in. π§΅
1. Type 1 Error (False Positive):
This occurs when the pregnancy test tells Tom, the man, that he is pregnant. Obviously, Tom cannot be pregnant, so this result is a false alarm. In statistical terms, it's detecting an effect (in this case, pregnancy) when it actually doesn't exist.
2. Type 2 Error (False Negative):
This happens when Lisa, who is actually pregnant, takes the test, and it tells her that she's not pregnant. The test failed to detect the real condition of pregnancy. In statistical terms, it's failing to detect a real effect (pregnancy) that is there.