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
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
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
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
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