Curso de Ciencia Datos de CENTIC

🐼1. Pandas	1.1 Intro to Python
	1.2 Pandas
	1.3 Exercise 📝 Send responses
	1.4 Solution

📊2. Exploratory Data Analysis	2.1 EDA theory
	2.2 EDA theory 2
	2.3 Exercise
	2.4 Exercise solution

📏3. Linear models	3.1 Linear Regression
	3.2 Logistic Regression
	3.3 Logistic Regression NLP
	3.4 Regularization
	3.5 Polynomial regression

🌳4. Tree models	4.1 EDA
	4.2 Decission Tree
	4.3 Random Forest
	4.4 Gradient Boosting
	4.5 Neural Network

🤹5. Unsupervised Learning	5.1 Dimensionality Reduction
	5.2 Clustering

🌐6. Web Scrapping	6.1 Beautiful Soup

🔤7. NLP	7.1 BOW + Logistic Regression
	7.2 TF-IDF, N-Grams
	7.3 Embeddings
	7.4 RNN with Keras

🕐8. Time Series	8.1 TimeSeries with Prophet 1
	8.2 TimeSeries with Prophet 2
	8.3 Ejercicio en Kaggle

🖼️9. Image	9.1 Clasification with Fast.ai
	9.2 Segmentation with Fast.ai

⛏️10. Process Mining	10.1 Process Mining con PM4PY
	10.2 Process Mining con BupaR

🗄️EXTRA	Efficient Pandas (H20 datatable, reduce memory...)
	Big data (Distributed ML, Pyspark)
	GPU ML (RAPIDS, cuDF, cuML)
	ML in production (API,etc)
	Sonido (clasificacion, clasificacion temporal, separar fuentes)

Top 3 best resources:

1. Mlcourse.ai (advanced)
2. Kaggle learn (easy)
3. Fast.ai ML (easy)