Description
“Machine Learning in Python: 43 Algorithms Commonly Used” is a training course—published by the online academy Udemy—that introduces widely used machine learning algorithms and their real-world applications. In this course, you will learn how to implement machine learning techniques using Python and popular libraries, while deepening your understanding of the mathematical principles and logic underlying various models. You will explore fundamental supervised learning algorithms—such as Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, and k-Nearest Neighbors—as well as unsupervised learning methods, including clustering and dimensionality reduction. You will also cover data preprocessing, feature selection, model training, evaluation, and performance optimization, learning how to select the most appropriate algorithm for different types of problems. Throughout the course, you will learn to choose the right algorithm for a given task and justify your selection using a five-question framework applicable to all 43 algorithms.
You will build and compare all 43 machine learning algorithms in Python—utilizing libraries such as scikit-learn, XGBoost, LightGBM, CatBoost, and PyTorch—with each demonstrated via an executable notebook. Additionally, you will complete two end-to-end projects: a credit risk scorecard with defined governance, and a real-time credit card fraud detection system featuring an alert queue, drift monitoring, and reason codes. You will learn how to identify errors that subtly degrade models—such as data leakage, miscalibration, and drift—as well as metrics that may appear sound but actually mask complete failure in critical areas. Through practical examples and hands-on coding, you will gain the skills to build predictive models, analyze datasets, and extract meaningful insights from data. Whether you are a Python developer, an aspiring data scientist, a student, or a tech enthusiast, this course provides a valuable foundation for understanding and applying machine learning algorithms to real-world challenges.
What you will learn in Machine Learning in Python: 43 Algorithms Commonly Used:
- Choose the right algorithm for a problem and justify that choice to someone entitled to overrule you, using a five-question framework applied to all 43.
- Build and compare 43 machine learning algorithms in Python with scikit-learn, XGBoost, LightGBM, CatBoost and PyTorch, each from a runnable notebook.
- Ship two end-to-end projects: a governed credit risk scorecard, and a real-time card fraud system with alert queues, drift monitoring and reason codes.
- Spot the failures that quietly ruin models — leakage, miscalibration, drift, and metrics that look healthy while hiding total failure on what matters.
- and …
Course specifications
Publisher: Udemy
Instructors: Real Numbers
Language: English
Level: Introductory to Advanced
Number of Lessons: 111
Duration: 18 hours and 39 minutes
Course topics
Machine Learning in Python: 43 Algorithms Commonly Used Prerequisites
Ability to read Python — you do not need to be fluent, and every notebook runs top to bottom unmodified
School-level algebra. No calculus or linear algebra background is assumed
A laptop that can install Python 3.10 or later. Everything runs on CPU — no GPU required anywhere
No prior machine learning experience; Sections 1 and 2 build the vocabulary from scratch
Pictures
Machine Learning in Python: 43 Algorithms Commonly Used introduction video
Installation guide
After Extract, watch with your favorite Player.
English subtitle
Quality: 720p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
2.4 GB