This repository contains materials, projects, and notes from the "Machine Learning and Data Science with Python" course I recently completed. The course provided an in-depth and practical understanding of key concepts and techniques in machine learning and data science, using the Python programming language.
The course covered a wide range of fundamental and advanced topics in machine learning and data science, with a focus on the practical application of techniques in Python. You can access it (language: portuguese - BR) at Machine Learning e Data Science with Python de A a Z. For additional resources, you can also check out the Mathematical Foundations for Machine Learning course (language: english - USA).
The quality of data is crucial for the effectiveness of machine learning models. I used techniques for data manipulation and cleaning using Pandas, dealing with missing data, duplicates, and data transformations to prepare suitable datasets for analysis.
Effective visualizations help to better understand data and communicate insights clearly. I used libraries like Matplotlib, Seaborn and Plotly to create graphs and plots at high and low level that reveal patterns and trends in the data.
I explored various machine learning algorithms, from basic to advanced, including:
- Linear and Logistic Regression: Models for predicting continuous values and categories;
- Decision Trees and Random Forests: Ensemble techniques to improve predictive accuracy;
- SVM (Support Vector Machines): A robust algorithm for classification and regression;
- KNN (K-Nearest Neighbors): An instance-based model for classification;
- K-Means Clustering: A clustering algorithm for data segmentation;
- Neural Networks and Deep Learning: An introduction to neural network concepts and implementation;
- Natural Language Processing (NLP): Techniques for working with text, including tokenization, stemming, and language models;
- Time Series Analysis: Methods for forecasting sequential data.
Throughout the course, we developed several practical projects that apply the concepts learned to real-world problems, including creating predictive models, data analysis, and developing machine learning applications.
