📒 Cuadernos

1. Introducction to AI software	🐍 Python
	🐼 Pandas
	📊 Seaborn
	💡 Scikit-learn

2. Tabular data for clasification Dataset: Titanic	📊 EDA: Simple
	🌳 Decission Tree Classifier
	🌲 Random Forest Classifier
	🌴 Gradient Boosting Classifier
	🧠 Neural Network Classifier

3. Tabular data for regression Dataset: House prices	📊 EDA: Simple
	🌀 EDA: Dimensionality Reduction
	📏 Linear Regression
	📏 Logistic Regression
	✨ Clustering (K-Means, DBSCAN)
	🧠 Neural Network Regression

Next Sessions:

4. 🗄 Create your dataset
   • 🌐 Web scrapping (Beautiful soup)
   • 🖼 Google Images
5. 🔤 Natual Language Processing
   • 🛠 Feature engeeering for NLP
   • 🛍️ Bag of words (CountVectorizer)
   • 📋 Tf-idf (TfidfVectorizer)
   • 💬 Word embeddings
6. 🕐 Time series
   • 🛠 Feature engeeering for time
   • 🔮 ARIMA (Facebook Prophet)
7. 👀 Vision
   • 🖼 Understanding images
   • 🔍 Convolutional Neural Network (Fastai)

🗓️ Agenda

Sesión	Día	Tema	Charla	Reto
1	1 Feb	AI Software	Pepe Palma y Andrés Torrubia
2	8 Feb	Tabular data 1	Projects brainstorming	🍺 Beer Challenge
3	15 Feb	Tabular data 2		🚗 Car pricing
4	22 Feb	NLP		🙃 Sentiment analysis
5	29 Feb	Time series		(aun por decidir)
6	7 Mar	Vision		📷 Marinera o bicicleta
7	14 Mar	Project
8	28 Mar	Project	Biología y sanidad
9	4 Abr	Project
10	25 Abr	Project	Ética en la IA
11	9 May	Project
12	16 May	Project	Comunicación Oral
13	23 May	Demo Day	Vosotros ;)

🛠 Projects

Aqui se irán poniendo los proyectos asignados. Ver ideas

📚 Best resources:

• Kaggle learn (⌛: Quick)
  • Python
  • Pandas
  • Data visualization
  • Intro to machine learning
  • Intermediate machine learning
  • Feature engineering
• Fast.ai (⌛: Medium)
  • Machine Learning
  • Deep learning práctico
  • Deep learning avanzado
  • NLP
• Mlcourse.ai (⌛: Long)
  • Medium articles
• Coursera
  • Competitive data science
• ./missing-semester: Curso sobre sistemas, no IA