Dive into a comprehensive 5-hour video series on applying Python to real-world Data Science projects. Master essential techniques including building classification and linear regression models, comparing machine learning classifiers, hyperparameter tuning, and utilizing Google Colab for practical data science tasks. Explore advanced topics such as Principal Component Analysis (PCA) for high-dimensional data, ROC curve plotting, web scraping with Pandas, and computational drug discovery. Gain proficiency in exploratory data analysis using Pandas Profiling, learn to leverage the low-code PyCaret library, and discover how to stack machine learning models. Perfect for aspiring data scientists, this series covers everything from building your first machine learning model to mastering Python for data science applications.
Overview
Syllabus
Machine Learning in Python: Building a Classification Model.
Compare Machine Learning Classifiers in Python.
Hyperparameter Tuning of Machine Learning Model in Python.
Practical Introduction to Google Colab for Data Science.
File Handling in Google Colab for Data Science.
Machine Learning in Python: Building a Linear Regression Model.
Machine Learning in Python: Principal Component Analysis (PCA) for Handling High-Dimensional Data.
How to Plot an ROC Curve in Python | Machine Learning in Python.
Easy Web Scraping in Python using Pandas for Data Science.
Data Science for Computational Drug Discovery using Python (Part 1).
Pandas Profiling for Data Science (Quick and Easy Exploratory Data Analysis).
Exploratory Data Analysis in Python using pandas.
Quick tour of PyCaret (a low-code machine learning library in Python).
Bioinformatics Project from Scratch - Drug Discovery Part 1 (Data Collection and Pre-Processing).
How to stack machine learning models in Python.
How to Master Python for Data Science.
Build your first machine learning model in Python.
Taught by
Data Professor
