Description
Course: Data Science and Machine Learning in Python: Linear Models. Companies face a challenge: they collect and store huge amounts of data every day. The problem is that they do not have the necessary tools and capabilities to extract knowledge and make decisions based on this data. But this situation is changing. The demand for data scientists has increased exponentially over the past few years. Demand is so high that there are not enough people with these skills to fill all the positions. With a simple search on job sites like Indeed, you’ll see why data scientist salaries have risen significantly in recent years.
Almost all the courses in the market are either too theoretical or too practical. College courses typically don’t develop the skills needed to solve data science problems from scratch, nor do they teach you fluency in using the necessary software. On the other hand, many online courses and bootcamps will teach you how to use these techniques without having a deep understanding of them and skimming the theory.
This course combines the best of both methods. On the one hand, we look at the origins of these methods and why they are used, and understand why they work the way they do. Alternatively, we program these methods from scratch using the most popular data science and machine learning libraries in Python. Only once you understand exactly how each algorithm works will we learn how to use them with Python’s advanced libraries.
course content
- Introduction to Machine Learning and Data Science
- Simple Linear Regression: We learn to study the relationship between different phenomena.
- Multiple Linear Regression: We create models with more than one variable to study the behavior of the desired variable.
- Lasso Regression: An advanced version of multiple linear regression with the ability to filter the most useful variables.
- Ridge Regression: A more robust version of multiple linear regression.
- Logistic regression: the most popular classification and diagnostic algorithm. This algorithm allows you to study the relationship between various variables and certain classes of objects.
- Poisson Regression: An algorithm that allows us to see how multiple variables affect the number of times an event occurs.
- Key concepts in data science (overfitting or underfitting, data validation, variable preparation, etc.)
What you will learn on the course:
- We implement all our models from scratch, step by step. You will learn all their theoretical and practical details.
- Fundamental understanding of the most popular machine learning algorithms.
- Knowledge of the main machine learning libraries in Python: scikit-learn, numpy, pandas, matplotlib, etc.
- Understanding the data science workflow and how to solve a forecasting problem from start to finish.
- Diagnose and solve problems in our models. You will be the person your colleagues turn to when their models fail.
Who is this course suitable for?
- Students who are interested in finding jobs in Data Science/Machine Learning.
- Professionals who want to apply predictive modeling to solve their most complex business problems.
- Machine learning practitioners who want to deeply understand how their algorithms work.
Data Science and Machine Learning in Python: Linear Models
- Publisher: Udemy
- Instructor: Escape from the Speed Lab
- Level of training: from beginner to advanced
- Training duration: 12 hours 12 minutes.
- Number of courses: 135
Course topics for January 1, 2024
Prerequisites for Data Science and Machine Learning in Python: Linear Models
- Basic knowledge of Python (variables, loops, classes, etc.)
- Experience with pandas and visualization tools helps, but is not required.
course images
Example video course
installation instructions
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English subtitles
Quality: 720p
Download link
Password for file(s): www.downloadly.ir
size
2.7 GB