Python for Machine Learning Certification Master course

Install python 3.7.6 or higher on Windows 64-bit, enable pip, and set up essential libraries. Build data apps with streamlit using pandas, matplotlib, and scikit-learn via a hello world example.

Open Visual Studio Code and create a project folder with a data subfolder. Create a Python file named app.py and place salary_data.csv containing years of experience and salary data.

learn how to install six essential python libraries for a data science project using pip, including streamlit, pandas, numpy, scikit-learn, and matplotlib, and verify versions.

Build and run a salary predictor app using stream and pandas, with a sidebar navigation of home, prediction, and contribute pages and radio buttons for seamless switching.

Display an image on the home page and load a data table from a csv using pandas, with a checkbox to show or hide salary and experience data.

Import libraries and prepare data from a csv to build non-interactive and interactive scatter plots. Set axis labels for years of experience and salary, and render graphs in the app.

Learn to convert a non-interactive scatter graph into an interactive plot by configuring the x and y axes, ranges, and layout, and debugging code to render markers correctly.

Add a slider to connect a data filter with non-interactive and interactive plots, enabling years of experience based filtering for clearer insights.

Students learn to predict salary from years of experience using linear regression in Python, applying scikit-learn, plotting results, and using a predict button for on-demand estimates.

Swap the home page image by replacing the file in the data folder and saving with ctrl+s, then revert to the original image by reloading the previous image file.

Add primary data to a csv, submit salary and experience via a form, and visualize results with plots and a linear regression salary predictor.

Learn practical tips to troubleshoot python streamlit apps by ensuring python and the scripts directory are in your system path and setting environment variables so your computer recognizes installed libraries.

Set up the project in Visual Studio Code, run Streamlit to launch a host, and explore animation and plotting demos while reviewing included code for UI in machine learning.

Build and run a streamline Python data app, set a title, auto update, load iris, breast cancer, and wine datasets, and add a sidebar with dropdowns and random forest classifier.

Load and prepare multiple datasets using sklearn, including iris and breast cancer, and extract features and labels. Inspect shapes and determine the number of classes with numpy.

Select a dataset and keys to configure classifiers, create parameter dictionaries, and use sliders to explore outputs for random forest and SVM in a practical machine learning workflow.

Explore building a streamlit classifiers workflow by importing neighbors and random forest classifiers, wiring data, and debugging errors like undefined names to obtain a working demo.

Train and test a classifier on breast cancer, iris, and Lyme datasets, split data with test size 0.2, fit the model, predict, and evaluate accuracy score.

learn to plot and visualize high-dimensional data using principal component analysis, build pipelines, and compare classifiers and datasets with interactive plots.

Create a Python BMI calculator web app that accepts weight in kilograms and height in units such as centimeters, meters, and feet, computes BMI, and shows categories with color feedback.