Machine Learning Basics: Building Regression Model in Python
Learn how to solve real-world problems using the Linear Regression technique in this Machine Learning Basics course. Get an in-depth understanding of data collection and preprocessing for Machine Learning Linear Regression problems, and learn to interpret the results of Linear Regression models. Discover advanced variations of the OLS method and gain the skills to convert business problems into Machine Learning Linear Regression problems. ▼
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Course Feature
Cost:
Paid
Provider:
Udemy
Certificate:
No Information
Language:
English
Start Date:
Self Paced
Course Overview
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Updated in [June 30th, 2023]
This course provides an introduction to Machine Learning Basics and how to build a Regression Model in Python. Participants will learn how to solve real life problems using the Linear Regression technique, including preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression, predicting future outcomes basis past data by implementing the simplest Machine Learning algorithm, understanding how to interpret the result of Linear Regression model and translating them into actionable insight, and gaining an understanding of the basics of statistics and concepts of Machine Learning.
In addition, participants will gain an indepth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problems, learn advanced variations of OLS method of Linear Regression, complete a end-to-end DIY project to implement their learnings from the lectures, and learn how to convert business problems into a Machine learning Linear Regression problem.
The course will also cover basic statistics using the Numpy library in Python, data representation using the Seaborn library in Python, and the Linear Regression technique of Machine Learning using the Scikit Learn and Statsmodel libraries of Python.
[Applications]
After taking this course, learners can apply the knowledge gained to solve real-life problems using the Linear Regression technique. They can use Univariate and Bivariate analysis to do preliminary analysis of data before running Linear regression. Learners can also use the Simplest Machine Learning algorithm to predict future outcomes based on past data. They can interpret the results of the Linear Regression model and translate them into actionable insights. Learners will also gain an understanding of the basics of statistics and concepts of Machine Learning, as well as an in-depth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problems. They can also learn advanced variations of the OLS method of Linear Regression. The course also contains an end-to-end DIY project to implement their learnings from the lectures, as well as how to convert a business problem into a Machine Learning Linear Regression problem. Finally, learners will gain a basic understanding of statistics using the Numpy library in Python, data representation using the Seaborn library in Python, and the Linear Regression technique of Machine Learning using the Scikit Learn and Statsmodel libraries of Python.
[Career Paths]
A career path recommended to learners of this course is a Machine Learning Engineer. A Machine Learning Engineer is responsible for developing and deploying machine learning models to solve real-world problems. They are responsible for designing, building, and maintaining machine learning systems, as well as for developing algorithms and models to improve the accuracy and performance of machine learning systems. They must also be able to interpret the results of their models and translate them into actionable insights.
The development trend of this job position is increasing rapidly due to the growing demand for machine learning applications in various industries. Companies are increasingly looking for Machine Learning Engineers to help them develop and deploy machine learning models to solve their business problems. As the demand for machine learning applications grows, so does the need for Machine Learning Engineers. Companies are also investing more in research and development of machine learning technologies, which is creating more job opportunities for Machine Learning Engineers.
[Education Paths]
The recommended educational path for learners of this course is a Bachelor's degree in Computer Science with a focus on Machine Learning. This degree will provide learners with the necessary knowledge and skills to understand the fundamentals of Machine Learning, including data collection, data preprocessing, linear regression, and other advanced variations of OLS methods. Additionally, learners will gain an understanding of basic statistics and concepts of Machine Learning, as well as the ability to interpret the results of Linear Regression models and translate them into actionable insights.
The development trend of this degree is to focus on the application of Machine Learning in real-world scenarios. This includes the use of Machine Learning to solve problems in areas such as healthcare, finance, and marketing. Additionally, the degree will focus on the development of new algorithms and techniques to improve the accuracy and efficiency of Machine Learning models. Finally, the degree will also focus on the ethical implications of Machine Learning and its potential impact on society.
Course Syllabus
Setting up Python and Jupyter Notebook
Basics of Statistics
Introduction to Machine Learning
Data Preprocessing
Linear Regression
Pros & Cons
Best course with perfect examples, methods, and approach.
Data handling portion is done well and intuitively.
Easy to follow and extremely helpful for understanding linear regression and implementing it with Python.
Clear explanation for getting an idea of regression theory along with Python hands-on.
Designed to solve real-world business problems.
Theory on assessing the accuracy of predicted coefficients is unclear.
Errors in the provided data files for the course project.
Lack of validation process or answer set for ensuring correct completion of each step.
Course Provider
Provider Udemy's Stats at AZClass
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