Machine Learning 101 with Scikit-learn and StatsModels from Udemy

Are you an aspiring data scientist determined to achieve professional success?

Are you ready and willing to master the most valuable skills that will skyrocket your data science career?

Great. You’ve come to the right place.

This course will provide you with the solid Machine Learning knowledge that will help you reach your dream job destination.

That’s right. Machine Learning is one of the fundamental skills you need to become a data scientist. It is the stepping stone that will help you understand deep learning and modern data analysis techniques.

In this course, we will explore the three most fundamental machine learning topics:

Linear regression
Logistic regression
Cluster analysis

Surprised? Even neural networks geeks (like us) can’t help, but admit that it’s these 3 simple methods - linear regression, logistic regression and clustering that data science actually revolves around.

So, in this course, we will make an otherwise complex subject matter easy to understand and apply in practice.

Of course, there is only one way to teach these skills in the context of data science - to accompany statistics theory with practical application of these quantitative methods in Python.

And that’s precisely what we are after. Theory and practice go hand in hand here.

We have developed this course with not one but two machine learning libraries – StatsModels and sklearn. As our practical experience showed us, they have different use cases and should be used together rather than independently.

Yet another advantage of taking this course? We are very conscious that data science theory is often overlooked.You can’t teach someone to run before they know how to walk. That’s why we will start slowly and continue by building complex ML models.

But don’t assume you’ll be bored by theory.

On the contrary. We have prepared a course that will get you results and will foster your interest in the subject matter, as it will show you that machine learning is something you can do, too (with the right teacher by your side).

Well, we hope you are as excited as we are, as this course is the door that can open countless opportunities in the data science world for you. This is a course you’ll be actually eager to complete.

On top of that we are happy to offer a 30-day money back guarantee. No risk for you. The content of the course is so outstanding , that this is a no-brainer for us We are 100% certain you will love it.

Why wait any longer? Every day is a missed opportunity.

Click the “Buy Now” button and let’s start (machine) learning together.

What's inside

Learning objectives

You will gain confidence when working with 2 of the leading ml packages - statsmodels and sklearn
You will learn how to perform a linear regression
You will become familiar with the ins and outs of a logistic regression

You will excel at carrying out cluster analysis (both flat and hierarchical)
You will learn how to apply your skills to real-life business cases
You will be able to comprehend the underlying ideas behind ml models

You will gain confidence when working with 2 of the leading ml packages - statsmodels and sklearn
You will learn how to perform a linear regression
You will become familiar with the ins and outs of a logistic regression
You will excel at carrying out cluster analysis (both flat and hierarchical)
You will learn how to apply your skills to real-life business cases
You will be able to comprehend the underlying ideas behind ml models

Syllabus

Introduction

What Does the Course Cover?

Setting Up The Working Environment

Setting Up the Environment - An Introduction (Do Not Skip, Please)!

Why Python and Why Jupyter?

Installing Anaconda

The Jupyter Dashboard - Part 1

The Jupyter Dashboard - Part 2

Jupyter Shortcuts

The Jupyter Dashboard

Installing sklearn

Installing Packages - Exercise

Installing Packages - Solution

Linear Regression with StatsModels

Introduction to Regression Analysis

The Linear Regression Model

Correlation vs Regression

Geometrical Representation

Python Packages Installation

Simple Linear Regression in Python

Simple Linear Regression in Python - Exercise

What is Seaborn?

What Does the StatsModels Summary Regression Table Tell us?

SST, SSR, and SSE

The Ordinary Least Squares (OLS)

Goodness of Fit: The R-Squared

The Multiple Linear Regression Model

Multiple Linear Regression

Adjusted R-Squared

Multiple Linear Regression - Exercise

F-Statistic and F-Test for a Linear Regression

Assumptions of the OLS Framework

A1: Linearity

A2: No Endogeneity

A3: Normality and Homoscedasticity

A4: No Autocorrelation

A5: No Multicollinearity

Dealing with Categorical Data

Dealing with Categorical Data - Exercise

Making Predictions

Linear Regression with Sklearn

What is sklearn?

Game Plan for sklearn

Simple Linear Regression with sklearn

Simple Linear Regression with sklearn - Summary Table

A Note on Normalization

Simple Linear Regression with sklearn - Exercise

Multiple Linear Regression with sklearn

Adjusted R-Squared - Exercise

Feature Selection through p-values (F-regression)

A Note on Calculation of P-values with sklearn

Creating a Summary Table with the p-values

Feature Scaling

Feature Selection through Standardization

Making Predictions with Standardized Coefficients

Feature Scaling - Exercise

Underfitting and Overfitting

Training and Testing

Linear Regression - Practical Example

Practical Example (Part 1)

Practical Example (Part 2)

A Note on Multicollinearity

Practical Example (Part 3)

Dummies and VIF - Exercise

Practical Example (Part 4)

Dummy Variables Interpretation - Exercise

Practical Example (Part 5)

Linear Regression - Exercise

Logistic Regression

Introduction to Logistic Regression

A Simple Example of a Logistic Regression in Python

What is the Difference Between a Logistic and a Logit Function?

Your First Logistic Regression

Your First Logistic Regression - Exercise

A Coding Tip (optional)

Going through the Regression Summary Table

Going through the Regression Summary Table - Exercise

Interpreting the Odds Ratio

Dummies in a Logistic Regression

Dummies in a Logistic Regression - Exercise

Assessing the Accuracy of a Classification Model

Assessing the Accuracy of a Classification Model - Exercise

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops skills in linear regression, logistic regression, and cluster analysis that are core to data science work

Builds foundational knowledge in applying machine learning principles to real-world business challenges

Strong use of practical Python examples to reinforce key concepts

Provides a comprehensive overview of the fundamentals of machine learning for data science

Emphasizes the importance of underlying statistical theory to enhance understanding of machine learning models

Course instructors are experienced in data science training

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning 101 with Scikit-learn and StatsModels with these activities:

Review the sklearn library tutorial

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Build a stronger foundation in sklearn to improve your ability to implement machine learning algorithms.

Browse courses on Sklearn

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Read the official sklearn tutorial.
Follow along with the examples provided in the tutorial.
Try implementing some of the algorithms yourself.

Solve practice problems on linear regression.

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Deepen your understanding of linear regression concepts and improve your problem-solving skills.

Browse courses on Linear Regression

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Find practice problems on linear regression online.
Solve the problems using the techniques you've learned in the course.
Check your answers against the provided solutions.

Attend a workshop on machine learning with Python.

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Gain hands-on experience with machine learning techniques and strengthen your Python skills.

Browse courses on Machine Learning

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Find a workshop that covers topics relevant to the course.
Register for the workshop.
Attend the workshop and participate actively.

Two other activities

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Show all five activities

Build a machine learning model to predict customer churn.

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Apply your machine learning skills to a real-world business problem and enhance your project portfolio.

Browse courses on Machine Learning

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Gather data on customer churn.
Clean and prepare the data.
Build a machine learning model to predict customer churn.
Evaluate the performance of the model.

Mentor a junior data scientist.

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Strengthen your understanding of machine learning concepts by teaching them to others.

Browse courses on Mentoring

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Find a junior data scientist who needs mentoring.
Meet with the mentee regularly to provide guidance and support.
Share your knowledge and experience in machine learning.
Provide feedback and encouragement to the mentee.

Career center

Learners who complete Machine Learning 101 with Scikit-learn and StatsModels will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine learning engineers design, develop, and deploy machine learning models. They work on a variety of projects, from developing new algorithms to improving existing ones. This course will provide you with the foundational knowledge in machine learning that is essential for success in this role. You will learn how to build and evaluate machine learning models, and gain experience with two of the most popular machine learning libraries, scikit-learn and StatsModels.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data scientists use their expertise in statistics, machine learning, and data analysis to solve complex problems. They work on a variety of projects, from developing new products to improving customer service. This course will give you a solid foundation in machine learning, one of the most important skills for data scientists. By mastering the concepts of linear and logistic regression, and cluster analysis, you will be able to tackle complex data science problems and develop innovative solutions.

See salaries and explore the career path for Data Scientist

Data Analyst

Data analysts help organizations make data-driven decisions and uncover insights from their data. They use their skills in data analysis, visualization, and communication to translate complex data into actionable insights. Completing this course can help you build a strong foundation in machine learning, a key skill for data analysts. By mastering the concepts of linear and logistic regression, and cluster analysis, you will be well-equipped to handle complex data analysis tasks and derive meaningful insights from data.

See salaries and explore the career path for Data Analyst

Business Analyst

Business analysts help organizations improve their performance by identifying and solving problems. They use their skills in data analysis, process improvement, and stakeholder management to develop and implement solutions that meet the needs of the business. This course can help you develop the machine learning skills that are increasingly in demand for business analysts. By learning how to build and evaluate machine learning models, you will be able to gain insights from data and make better decisions.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations research analysts use mathematical and analytical techniques to solve complex problems in a variety of industries. They work on projects such as improving supply chain efficiency, optimizing production schedules, and designing new products. This course can help you develop the machine learning skills that are increasingly in demand for operations research analysts. By learning how to build and evaluate machine learning models, you will be able to solve complex problems and improve decision-making.

See salaries and explore the career path for Operations Research Analyst

Market Researcher

Market researchers collect and analyze data to understand consumer behavior and trends. They use this information to help businesses make better decisions about product development, marketing, and pricing. This course can help you develop the machine learning skills that are increasingly in demand for market researchers. By learning how to build and evaluate machine learning models, you will be able to gain insights from data and make better predictions about consumer behavior.

See salaries and explore the career path for Market Researcher

Financial Analyst

Financial analysts use data to evaluate investments and make recommendations to clients. They work on a variety of projects, from developing investment strategies to managing portfolios. This course can help you develop the machine learning skills that are increasingly in demand for financial analysts. By learning how to build and evaluate machine learning models, you will be able to gain insights from data and make better investment decisions.

See salaries and explore the career path for Financial Analyst

Risk Analyst

Risk analysts identify and assess risks to an organization. They work on a variety of projects, from developing risk management plans to implementing risk mitigation strategies. This course can help you develop the machine learning skills that are increasingly in demand for risk analysts. By learning how to build and evaluate machine learning models, you will be able to gain insights from data and make better decisions about risk.

See salaries and explore the career path for Risk Analyst

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work on a variety of projects, from developing insurance products to pricing financial instruments. This course can help you develop the machine learning skills that are increasingly in demand for actuaries. By learning how to build and evaluate machine learning models, you will be able to gain insights from data and make better decisions about risk.

See salaries and explore the career path for Actuary

Statistician

Statisticians collect, analyze, and interpret data. They work on a variety of projects, from designing experiments to developing statistical models. This course can help you develop the machine learning skills that are increasingly in demand for statisticians. By learning how to build and evaluate machine learning models, you will be able to gain insights from data and make better decisions.

See salaries and explore the career path for Statistician

Data Engineer

Data engineers design and build the infrastructure that stores and processes data. They work on a variety of projects, from developing data pipelines to managing data warehouses. This course may be helpful for data engineers who want to learn more about machine learning. By learning how to build and evaluate machine learning models, data engineers can gain insights from data and improve the performance of their data pipelines.

See salaries and explore the career path for Data Engineer

Software Engineer

Software engineers design, develop, and maintain software applications. They work on a variety of projects, from developing new features to fixing bugs. This course may be helpful for software engineers who want to learn more about machine learning. By learning how to build and evaluate machine learning models, software engineers can gain insights from data and improve the performance of their software applications.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

Quantitative analysts use mathematical and statistical techniques to analyze financial data. They work on a variety of projects, from developing trading strategies to managing risk. This course can help you develop the machine learning skills that are increasingly in demand for quantitative analysts. By learning how to build and evaluate machine learning models, you will be able to gain insights from data and make better investment decisions.

See salaries and explore the career path for Quantitative Analyst

Data Architect

Data architects design and manage the architecture of data systems. They work on a variety of projects, from developing data models to implementing data security. This course may be helpful for data architects who want to learn more about machine learning. By learning how to build and evaluate machine learning models, data architects can gain insights from data and improve the performance of their data systems.

See salaries and explore the career path for Data Architect

Database Administrator

Database administrators manage and maintain databases. They work on a variety of projects, from installing and configuring databases to backing up and recovering data. This course may be helpful for database administrators who want to learn more about machine learning. By learning how to build and evaluate machine learning models, database administrators can gain insights from data and improve the performance of their databases.

See salaries and explore the career path for Database Administrator