Description
Introduction to Data Science and Machine Learning. This course provides a comprehensive and balanced introduction to the intersection of data science and machine learning, covering theory, computational methods, and practical applications. The course is designed for beginners to build a strong foundation in fundamental concepts, statistics, and mathematics, and no prior experience is required. Topics include data science fundamentals, data visualization and storytelling, linear and nonlinear regression methods, and an overview of classification techniques such as decision trees, random forests, and neural networks. It also covers unsupervised learning and uncovering hidden patterns with methods such as spectral clustering. Participants will be empowered to solve real-world problems in fields such as engineering and business using Python and its popular libraries. What you will learn includes applying quantitative modeling and data analysis techniques to solve practical problems, effectively communicating findings through data visualization, mastering statistical analysis techniques, applying data science principles to engineering challenges, applying computational tools to analyze big data, gaining a deep understanding of classic machine learning algorithms, implementing these algorithms to build intelligent systems, understanding basic mathematical and statistical principles, and creating effective data visualizations. A review of key algorithms such as regression, neural networks, and clustering methods is also an essential part of this course.
What you will learn
- Understand the fundamental concepts of mathematics, statistics, and data science principles that underpin modern machine learning.
- Creating engaging data visualizations and using storytelling to effectively convey insights.
- Review of machine learning algorithms: Learn key algorithms including Regression, Neural Networks, and Unsupervised Methods such as Clustering.
- Apply programming tools to address real-world problems using Python and its popular libraries.
- Using data-driven techniques to tackle real-world challenges in fields such as engineering, business, and journalism.
This course is suitable for people who:
- Undergraduate and graduate students in engineering, computer science, or related fields who seek to understand data-driven methods.
- Beginners in programming or data science who are interested in data analysis – no prior expertise required.
- Aspiring data scientists and ML engineers looking for a solid foundation in key concepts.
- Professionals in engineering and applied sciences who want to apply data science to real-world problems.
Course details
- Publisher: Udemy
- Instructor: RAHUL RAI
- Training level: Beginner to advanced
- Training duration: 7 hours and 55 minutes
- Number of lessons: 67
Course topics 
Prerequisites for the Introduction to Data Science and Machine Learning course
- Having a solid understanding of basic algebra and logical reasoning is essential for recognizing data patterns and algorithms.
- Interest in Data and Problem Solving: A curious mindset and a willingness to explore how data can address real-world challenges.
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: None
Quality: 720p
Download link
Downloadly
Rapidgator link
File(s) password: www.downloadly.ir
File size
10.2 GB
