Data Science and Machine Learning in Python: Linear models

Data Science and Machine Learning in Python: Linear models, available at $64.99, has an average rating of 4.75, with 137 lectures, based on 38 reviews, and has 266 subscribers.

You will learn about Implement all our models from scratch, step by step. You will learn every detail of their theory and practice. Fundamentally understand the most popular machine learning algorithms. Master the main machine learning libraries in Python: scikit-learn, numpy, pandas, matplotlib, etc. Understand the data science workflow and how to solve a prediction problem from beginning to end. Diagnose and solve problems in our models. You'll be the person your colleagues will seek when their models fail. This course is ideal for individuals who are Students interested in landing a job in the field of Data Science / Machine Learning. or Professionals who want to apply predictive modelling to solve their hardest business problems. or Machine learning practitioners who want to develop a deep understanding of how their algorithms work. It is particularly useful for Students interested in landing a job in the field of Data Science / Machine Learning. or Professionals who want to apply predictive modelling to solve their hardest business problems. or Machine learning practitioners who want to develop a deep understanding of how their algorithms work.

Enroll now: Data Science and Machine Learning in Python: Linear models

Summary

Title: Data Science and Machine Learning in Python: Linear models

Price: $64.99

Average Rating: 4.75

Number of Lectures: 137

Number of Published Lectures: 137

Number of Curriculum Items: 137

Number of Published Curriculum Objects: 137

Original Price: €219.99

Quality Status: approved

Status: Live

What You Will Learn

Implement all our models from scratch, step by step. You will learn every detail of their theory and practice.
Fundamentally understand the most popular machine learning algorithms.
Master the main machine learning libraries in Python: scikit-learn, numpy, pandas, matplotlib, etc.
Understand the data science workflow and how to solve a prediction problem from beginning to end.
Diagnose and solve problems in our models. You'll be the person your colleagues will seek when their models fail.

Who Should Attend

Students interested in landing a job in the field of Data Science / Machine Learning.
Professionals who want to apply predictive modelling to solve their hardest business problems.
Machine learning practitioners who want to develop a deep understanding of how their algorithms work.

Target Audiences

Students interested in landing a job in the field of Data Science / Machine Learning.
Professionals who want to apply predictive modelling to solve their hardest business problems.
Machine learning practitioners who want to develop a deep understanding of how their algorithms work.

Why study data science?

Companies have a problem: they collect and store huge amounts of data on a daily basis. The problem is that they don’t have the tools and capabilities to extract knowledge and make decisions from that data. But that is changing. For some years now, the demand for data scientists has grown exponentially. So much so, that the number of people with these skills is not enough to fill all the job openings. A basic search on Glassdoor or Indeed will reveal to you why data scientist salaries have grown so much in recent years.

Why this course?

Almost every course out there is either too theoretical or too practical. University courses don’t usually develop the skills needed to tackle data science problems from scratch, nor do they teach you how to use the necessary software fluently. On the other hand, many online courses and bootcamps teach you how to use these techniques without getting a deep understanding of them, going through the theory superficially.

Our course combines the best of each method. On the one hand, we will look at where these methods come from and why they are used, understanding why they work the way they do. On the other, we will program these methods from scratch, using the most popular data science and machine learning libraries in Python. Only when you have understood exactly how each algorithm works, we will learn how to use them with advanced Python libraries.

Course content

Introduction to machine learning and data science.
Simple linear regression. We will learn how to study the relationship between different phenomena.
Multiple linear regression. We will create models with more than one variable to study the behavior of a variable of interest.
Lasso regression. Advanced version of multiple linear regression with the ability to filter the most useful variables.
Ridge regression. A more stable version of multiple linear regression.
Logistic regression. Most popular classification and detection algorithm. It will allow us to study the relationship between different variables and certain object classes.
Poisson regression. Algorithm that will allow us to see how several variables affect the number of times an event occurs.
Central concepts in data science (overfitting vs underfitting, cross-validation, variable preparation, etc).

Any questions? Remember that we have a 30-day full money-back guarantee. No risk for you. That’s how convinced we are that you will love the course.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome

Lecture 2: Paradigms of machine learning

Lecture 3: Data and datapoints

Lecture 4: What is a model?

Lecture 5: Tools and libraries for machine learning

Lecture 6: Complete code

Lecture 7: Data Science and Machine Learning series

Lecture 8: Our complete course catalog

Lecture 9: Follow us on social media

Chapter 2: Simple linear regression

Lecture 1: Quick review of derivative functions

Lecture 2: Simple linear regression

Lecture 3: Model assumptions

Lecture 4: Finding the best fitting model

Lecture 5: Maximum likelihood estimation

Lecture 6: Maximum likelihood estimate of β₀

Lecture 7: Maximum likelihood estimate of β₁

Lecture 8: Maximum likelihood estimate of σ²

Lecture 9: Interpreting the obtained parameters

Lecture 10: Evaluating goodness of fit

Lecture 11: Ordinary Least squares

Lecture 12: Regression models without intercept

Lecture 13: Checking the model's assumptions

Lecture 14: Link to the code notebook

Lecture 15: Finding the best fitting model – Coding 1

Lecture 16: Finding the best fitting model – Coding 2

Lecture 17: Finding the best fitting model – Coding 3

Lecture 18: Finding the best fitting model – Coding 4

Lecture 19: Finding the best fitting model – Coding 5

Lecture 20: Finding the best fitting model – Coding 6

Chapter 3: Multiple linear regression

Lecture 1: Multiple linear regression

Lecture 2: Find the β parameters of our model

Lecture 3: Link to the code notebook

Lecture 4: Creating our first MLR model – Code 1

Lecture 5: Creating our first MLR model – Code 2

Lecture 6: Creating our first MLR model – Code 3

Lecture 7: Creating our first MLR model – Code 4

Lecture 8: Categorical and dummy variables

Lecture 9: Interpreting the effect of dummy variables

Lecture 10: Feature interactions

Lecture 11: Multiple linear regression with categorical variables – Code 1

Lecture 12: Multiple linear regression with categorical variables – Code 2

Lecture 13: Feature standardization

Lecture 14: Ranking predictive variables – Code

Lecture 15: Multicollinearity

Lecture 16: Variance Inflation Factor (VIF)

Lecture 17: Treating multicollinearity – Code 1

Lecture 18: Treating multicollinearity – Code 2

Lecture 19: Treating multicollinearity – Code 3

Chapter 4: Diamond price prediction

Lecture 1: Link to the code notebook

Lecture 2: The data science life cycle

Lecture 3: Diamond price prediction – Code 1

Lecture 4: Diamond price prediction – Code 2

Lecture 5: Training, validation and test datasets

Lecture 6: Diamond price prediction – Code 3

Lecture 7: Diamond price prediction – Code 4

Lecture 8: Diamond price prediction – Code 5

Lecture 9: Target and predictor variable transforms

Lecture 10: Diamond price prediction – Code 6

Lecture 11: Diamond price prediction – Code 7

Lecture 12: Mutual information

Lecture 13: Diamond price prediction – Code 8

Lecture 14: Scikit-learn and the Pipeline class

Lecture 15: The ColumnTransformer class

Lecture 16: The TransformedTargetRegressor class

Lecture 17: Diamond price prediction – Code 9

Lecture 18: Diamond price prediction – Code 10

Lecture 19: Interpreting the coefficients of a model with transformed predictors

Lecture 20: Interpreting the coefficients of a model with transformed target variable

Lecture 21: Interpreting the coefficients of a model with transformed target and predictor

Lecture 22: Interpreting the coefficients of dummy predictors with transformed targets

Lecture 23: Diamond price prediction – Code 11

Chapter 5: Polynomial regression

Lecture 1: Overfitting and generalization

Lecture 2: Polynomial regression

Lecture 3: Link to the code notebook

Lecture 4: Polynomial regression – code 1

Lecture 5: Polynomial regression – code 2

Lecture 6: Interaction terms

Lecture 7: Polynomial regression – code 3

Lecture 8: Overfitting in polynomial regression

Lecture 9: Structural multicollinearity

Lecture 10: Polynomial regression – code 4

Lecture 11: Polynomial regression – code 5

Lecture 12: Polynomial regression – code 6

Chapter 6: Ridge regression

Lecture 1: Link to the code notebook

Lecture 2: Ridge regression 1

Lecture 3: Ridge regression 2

Lecture 4: Ridge regression 3

Lecture 5: Ridge regression 4

Lecture 6: Ridge regression 5

Lecture 7: Ridge regression – Code 1

Lecture 8: Ridge regression – Code 2

Lecture 9: Ridge regression – Code 3

Lecture 10: Ridge regression – Code 4

Lecture 11: Ridge regression – Code 5

Instructors

Escape Velocity Labs
Hands-on, comprehensive AI courses

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4 stars: 6 votes
5 stars: 27 votes

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