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data-science-studies's Introduction

MasterHead

What's shakin', bacon? 👋

CSFelix here, a Brazilian Data Scientist :octocat:

Coding

csfelix

  • 💬 Talk? Yeah, we can talk about Python, Julia, R, SQL, Neo4J and Cypher, Data Science, Data Visualization, Machine Learning, Deep Learning and stuff like that...

  • 🔭 I'm currently working on Data Science Projects, such as Recommendation Systems, and improving my Julia Programming Language Skills

  • 🌐 Desiring to reach me? Consider using one of these languages: Brazilian Portuguese (native), American English (fluent) and French (beginner).

  • 🤔 You can find out more about me and my projects on my Personal Portfolio: CSFelix.io.



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💼 Working For

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📇 Connect with me

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data-science-studies's Issues

5 - Data Wrangling and Data Cleaning

✔️ Tasks

  • ✔️ Dealing with Missing Values
  • ✔️ Dealing with Categorical Variables
  • ✔️ Scale, Standardization and Normalization
  • ✔️ Parsing Dates
  • ✔️ Character Encoding
  • ✔️ Inconsistent Data Entry
  • ✔️ Duplicated Values

10 - Computer Vision Basics

✔️ Tasks

  • ✔️ Simple CNN
  • ✔️ Convolutional and ReLU
  • ✔️ Maximum Pooling
  • ✔️ Sliding Window
  • ✔️ Custom Convnets
  • ✔️ Data Augmentation

7 - Machine Learning

✔️ Tasks

  • ✔️ Data Leakage
  • ✔️ Fit VS Transform VS Fit_Transform
  • ✔️ Pipelines
  • ✔️ Cross-Validation
  • ✔️ XGBoost
  • ✔️ Stats Model
  • ✔️ Linear and Logistic Regressions
  • ✔️ Decision Trees and Random Forests
  • ✔️ Bonus: Optuna and Lazy Predictions Packages

9 - Deep Learning Basics

✔️ Tasks

  • ✔️ Basic Keras and Tensor Flow
  • ✔️ How to Display Activation Functions Plots
  • ✔️ Underfitting and Overfitting
  • ✔️ Dropout and Batch Normalization Layers
  • ✔️ Binary Classification
  • ✔️ LSTM and GRU

2 - Data Visualization

✔️ Tasks

  • ✔️ Plots
  • ✔️ Pandas Fast Plots
  • ✔️ Data Visualization Principles

3 - Statistic, Probability and Hypothesis Testing

✔️ Tasks

  • ✔️ Types of Variables
  • ✔️ Categorical Features Statistics I
  • ✔️ Numerical Features Statistics I
  • ✔️ Numerical Features Statistics II
  • ✔️ Hypothesis Testing
  • ✔️ Bonus - Useful Tools

6 - Feature Engineering

✔️ Tasks

  • ✔️ Mutual Information
  • ✔️ Target Encoding
  • ✔️ Label Encoding
  • ✔️ PyOD with a Single Model
  • ✔️ PyOD with Combined Models
  • ✔️ Dimensionality Reduction I: Principal Component Analysis (PCA)
  • ✔️ Dimensionality Reduction II: t-SNE
  • ✔️ Dimensionality Reduction III: UMAP
  • ✔️ Clustering I: K-Means Clustering
  • ✔️ Clustering II: Hierarchical

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