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Data DaVinci

Welcome to the Data DaVinci! This repository contains code, resources, and materials to guide you through foundational machine learning concepts. Each week focuses on a specific machine learning technique or analysis method, building skills for real-world data analysis.

Week 0: Introduction and Python Basics

Get familiar with Python and essential data science libraries.

  • Topics: Python basics, Jupyter Notebook, Pandas, NumPy, Matplotlib
  • Activities: Setting up environments, basic data exploration on sample datasets

Week 1: Linear and Logistic Regression

Introduction to regression techniques, both linear and logistic, for predicting numerical and categorical outcomes.

  • Topics: Linear regression, logistic regression, regularization
  • Activities: Predicting trends in a dataset, classifying outcomes based on attributes

Week 2: Decision Trees and Random Forests

Introduction to decision tree algorithms and ensemble learning techniques for classification and regression tasks.

  • Topics: Decision trees, random forests, overfitting, feature importance
  • Activities: Building decision trees, evaluating model performance, implementing random forest classifiers for predicting categorical outcomes

Week 3: Neural Networks

Introduction to neural networks, a fundamental deep learning technique, for solving complex classification and regression problems.

  • Topics: Basic structure of neural networks, backpropagation, activation functions
  • Activities: Building and training simple neural networks, tuning hyperparameters, visualizing network performance

Week 4: Time Series Analysis

Explore time series analysis techniques to analyze sequential data, useful for forecasting trends over time.

  • Topics: ARIMA, moving averages, autocorrelation
  • Activities: Time-based forecasting, evaluating model accuracy for trends

Week 5: Sentiment Analysis

Apply Natural Language Processing (NLP) techniques to analyze sentiment from text data such as reviews, comments, or news.

  • Topics: Sentiment analysis, text vectorization, basic NLP
  • Activities: Text sentiment analysis, building a sentiment classifier

Week 6: Project and Wrap-Up

Integrate learned techniques in a final project, applying ML to a real-world problem of your choice.

  • Activities: Final project, presentations, reflection on key takeaways