Udemy – Python & ChatGPT for A-Z Data Science and Machine Learning 2023-10

Udemy – Python & ChatGPT for A-Z Data Science and Machine Learning 2023-10 Downloadly IRSpace

Aug 16, 2025 - 07:03 Updated: Aug 17, 2025 - 19:39

0 0

Udemy – Python & ChatGPT for A-Z Data Science and Machine Learning 2023-10

Python & ChatGPT for AZ Data Science and Machine Learning Course. This course provides a comprehensive learning path to master the fundamental and advanced concepts of this field. Combining the power of Python programming with the capabilities of ChatGPT, this course takes you from the initial stages of data collection and cleaning to advanced analytics and building machine learning models. Participants will be introduced to essential Python libraries such as pandas, numpy, matplotlib, seaborn, and scikit-learn, and will gain the skills necessary to process data, engineer features, and run various supervised and unsupervised learning models. Practical projects in this course include implementing algorithms such as linear regression, random forest, XGBoost, and KMeans clustering. Interactive exercises with ChatGPT also help to deepen understanding of concepts and troubleshoot bugs. This course is designed for those who want to learn the skills needed to solve real-world problems in data science and machine learning in a practical, project-based way.

What you will learn:

Use ChatGPT to generate the exact Python code needed for any task of your data analysis and data science workflow, and even for Machine Learning.
Gain the skills necessary to effectively clean raw data, cover techniques for handling missing values, deal with different data types, and manage outliers, etc.
Master data manipulation by learning basic techniques like sorting, filtering, merging, concatenation, and more using Python’s pandas library.
Learn exploratory data analysis techniques including frequencies, percentages, group-by operations, pivot tables, cross-tabulations, and variable relationships.
Enter the world of data preprocessing with hands-on experience in feature engineering, feature selection, and scaling to prepare datasets for ML models.
Apply your knowledge through a series of practical projects, strengthening your understanding of each step in the data science workflow.
Gain expertise in building and evaluating supervised regression models, including linear regression, random forest, decision tree, xgboost, and more.
Gain practical skills in deploying supervised classification models, covering algorithms such as logistic regression, random forest, KNN, and lightgbm.
Explore unsupervised learning by understanding and implementing clustering models like KMeans to discover hidden patterns in data.
Learn Python syntax, data types, variables, and operators to build simple programs and perform basic functions.
Master the use of essential Python libraries for data science, including pandas, numpy, seaborn, matplotlib, scikit-learn, and scipy.
Test your knowledge and reinforce learning through a series of seven-tiered quizzes that cover different aspects of data science workflows.
Learn to regulate program flow, use loops and conditional statements such as if, elif, and else.
Experience ChatGPT integration to enhance your understanding of data science applications through interactive conversations and real-world problem solving.
Gain proficiency in using Python lists, dictionaries, tuples, and sets.