Mastering Machine Learning Algorithms using Python
Год выпуска: July 2024
Производитель: Published by Packt Publishing via O'Reilly Learning
Сайт производителя:
https://learning.oreilly.com/course/mastering-machine-learning/9781836646716/
Автор: Manas Dasgupta
Продолжительность: 28h 35m
Тип раздаваемого материала: Видеоурок
Язык: Английский + субтитры
Описание:
Embark on a comprehensive journey into the world of machine learning with this expertly designed course. Begin with foundational principles, terminology of machine learning. Explore various types of machine learning problems, including regression and classification, and understand the crucial role of data.
Progressing further, the course covers essential statistical techniques and Python programming. Learn to work with different data types, perform descriptive statistics, and understand probability theory and hypothesis testing. The Python modules span from basic syntax to advanced data manipulation with libraries like NumPy and pandas, providing a solid foundation for data analysis and model building.
The course culminates with hands-on projects and case studies, blending theoretical knowledge with practical experience. You'll implement algorithms such as linear and logistic regression, Naive Bayes, decision trees, random forests, and support vector machines. The deep learning section offers a glimpse into neural networks. Through these projects, you'll gain the skills to optimize and evaluate models, ensuring their effectiveness in real-world scenarios.
What you will learn
• Implement regression and classification algorithms
• Perform exploratory data analysis and data preprocessing
• Utilize Python libraries for data manipulation and visualization
• Build and optimize machine learning models
• Understand the principles of deep learning
• Develop skills to solve real-world machine learning problems
Содержание
Chapter 01 Introduction to Machine Learning
Chapter 02 Statistical Techniques
Chapter 03 Learning Python
Chapter 04 Exploratory Data Analysis
Chapter 05 Linear Regression
Chapter 06 Logistic Regression
Chapter 07 Naive Bayes Classification Algorithm
Chapter 08 Decision Tree Classifier
Chapter 09 Random Forest Ensemble
Chapter 10 Support Vector Machine
Chapter 11 Dimensionality Reduction - Principal Component Analysis (PCA)
Chapter 12 Unsupervised Learning using K-Means Clustering
Chapter 13 Introduction to Deep Learning
Файлы примеров: отсутствуют
Формат видео: MP4
Видео: AVC, 1920x1080, 16:9, 30.000 fps, 3 000 kb/s (0.017 bit/pixel)
Аудио: AAC, 44.1 KHz, 2 channels, 128 kb/s, CBR
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