Machine-Learning-Basics

Machine Learning Process The machine learning process involves the following steps: 1- Data Preparation: Collect, clean, and preprocess data. 2- Data Visualization and Analysis: Visualize and analyze data to identify patterns and relationships. 3- Feature Engineering: Select and transform relevant variables in the data. 4- Model Selection: Choose the best model for the problem. 5- Model Training: Feed data into the model and adjust parameters to minimize error. 6- Hyperparameter Tuning: Set hyperparameters to optimize model performance. 7- Model Evaluation: Measure accuracy, precision, recall, and other performance metrics. 8- Model Deployment: Integrate the model into an application and set up a pipeline to feed new data. Machine Learning Tutorial This tutorial covers Machine Learning Basics using Python. The repository includes Python notebooks, reference guides, and cheatsheets for the entire Machine Learning process: 1- Data preprocessing and analysis: clean and transform data into a format suitable for analysis using NumPy and Pandas. 2- Data visualization: understand and explore data visually using Matplotlib and Seaborn.