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30-Day Money-Back Guarantee

This course includes:

  • 5 hours on-demand video
  • 27 articles
  • 7 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Business Business Analytics & Intelligence Statistical Modeling

Machine Learning 101 with Scikit-learn and StatsModels

New to machine learning? This is the place to start: Linear regression, Logistic regression & Cluster Analysis
Bestseller
Rating: 4.6 out of 54.6 (404 ratings)
7,872 students
Created by 365 Careers
Last updated 6/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • 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
Curated for the Udemy for Business collection

Requirements

  • Basic coding skills in Python

Description

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!

Who this course is for:

  • This course is for you, if you want to become a successful data scientist
  • This course is great if you want to get acquainted with the fundamental machine learning methods
  • This course is ideal for you, if you are a just getting started and want to gradually build up valuable skills in machine learning and data science

Featured review

Gregg Koumbis
Gregg Koumbis
106 courses
23 reviews
Rating: 5.0 out of 57 months ago
Superb course presented in an iterative fashion. Each lesson stands alone, but also is a step to deeper learning. The lessons first provide a basic understanding of the concepts and build up to practical applications. Many of the topics include experiential learning with actual hands-on projects that use real datasets and popular programs and algorithms. Well done 365!

Course content

8 sections • 102 lectures • 5h 17m total length

  • Preview03:55

  • Setting Up the Environment - An Introduction (Do Not Skip, Please)!
    00:50
  • Why Python and Why Jupyter?
    04:53
  • Why Python and Why Jupyter?
    2 questions
  • Installing Anaconda
    03:03
  • The Jupyter Dashboard - Part 1
    02:27
  • The Jupyter Dashboard - Part 2
    05:14
  • Jupyter Shortcuts
    00:08
  • The Jupyter Dashboard
    3 questions
  • Installing sklearn
    01:18
  • Installing Packages - Exercise
    00:13
  • Installing Packages - Solution
    00:15

  • Preview01:27
  • Introduction to Regression Analysis
    1 question
  • Preview05:50
  • The Linear Regression Model
    2 questions
  • Correlation vs Regression
    01:43
  • Correlation vs Regression
    1 question
  • Geometrical Representation
    01:25
  • Geometrical Representation
    1 question
  • Python Packages Installation
    04:39
  • Simple Linear Regression in Python
    07:11
  • Simple Linear Regression in Python - Exercise
    00:39
  • What is Seaborn?
    01:21
  • What Does the StatsModels Summary Regression Table Tell us?
    05:47
  • What Does the StatsModels Summary Regression Table Tell us?
    3 questions
  • SST, SSR, and SSE
    03:37
  • SST, SSR, and SSE
    1 question
  • The Ordinary Least Squares (OLS)
    03:13
  • The Ordinary Least Squares (OLS)
    1 question
  • Goodness of Fit: The R-Squared
    05:30
  • Goodness of Fit: The R-Squared
    2 questions
  • The Multiple Linear Regression Model
    02:55
  • Multiple Linear Regression
    1 question
  • Adjusted R-Squared
    06:00
  • Adjusted R-Squared
    3 questions
  • Multiple Linear Regression - Exercise
    00:03
  • F-Statistic and F-Test for a Linear Regression
    02:01
  • Assumptions of the OLS Framework
    02:21
  • Assumptions of the OLS Framework
    1 question
  • A1: Linearity
    01:50
  • A1: Linearity
    2 questions
  • A2: No Endogeneity
    04:09
  • A2: No Endogeneity
    1 question
  • A3: Normality and Homoscedasticity
    05:47
  • A4: No Autocorrelation
    03:31
  • A4: No Autocorrelation
    2 questions
  • A5: No Multicollinearity
    03:26
  • A5: No Multicollinearity
    1 question
  • Dealing with Categorical Data
    06:43
  • Dealing with Categorical Data - Exercise
    00:03
  • Making Predictions
    03:29

  • What is sklearn?
    02:14
  • Game Plan for sklearn
    01:56
  • Simple Linear Regression with sklearn
    05:38
  • Simple Linear Regression with sklearn - Summary Table
    04:49
  • A Note on Normalization
    00:09
  • Simple Linear Regression with sklearn - Exercise
    00:03
  • Multiple Linear Regression with sklearn
    03:10
  • Adjusted R-Squared
    04:45
  • Adjusted R-Squared - Exercise
    00:03
  • Feature Selection through p-values (F-regression)
    04:41
  • A Note on Calculation of P-values with sklearn
    00:13
  • Creating a Summary Table with the p-values
    02:10
  • Multiple Linear Regression - Exercise
    00:03
  • Feature Scaling
    05:38
  • Feature Selection through Standardization
    05:22
  • Making Predictions with Standardized Coefficients
    03:53
  • Feature Scaling - Exercise
    00:03
  • Underfitting and Overfitting
    02:42
  • Training and Testing
    06:54

  • Practical Example (Part 1)
    11:59
  • Practical Example (Part 2)
    06:12
  • A Note on Multicollinearity
    00:14
  • Practical Example (Part 3)
    03:15
  • Dummies and VIF - Exercise
    00:03
  • Practical Example (Part 4)
    08:10
  • Dummy Variables Interpretation - Exercise
    00:14
  • Practical Example (Part 5)
    07:34
  • Linear Regression - Exercise
    00:16

  • Introduction to Logistic Regression
    01:19
  • Preview04:42
  • What is the Difference Between a Logistic and a Logit Function?
    04:00
  • Your First Logistic Regression
    02:48
  • Your First Logistic Regression - Exercise
    00:03
  • A Coding Tip (optional)
    02:26
  • Going through the Regression Summary Table
    04:06
  • Going through the Regression Summary Table - Exercise
    00:03
  • Interpreting the Odds Ratio
    04:30
  • Dummies in a Logistic Regression
    04:32
  • Dummies in a Logistic Regression - Exercise
    00:03
  • Assessing the Accuracy of a Classification Model
    03:21
  • Assessing the Accuracy of a Classification Model - Exercise
    00:02
  • Underfitting and Overfitting
    03:43
  • Testing our Model and Bulding a Confusion Matrix
    05:05
  • Testing our Model and Bulding a Confusion Matrix - Exercise
    00:02

  • Introduction to Cluster Analysis
    03:41
  • Examples of Clustering
    04:31
  • Classification vs Clustering
    02:32
  • Math Concepts Needed to Proceed
    03:19
  • K-Means Clustering
    04:41
  • A Hands on Example of K-Means
    07:48
  • A Hands on Example of K-Means - Exercise
    00:02
  • Categorical Data in Cluster Analysis
    02:50
  • Categorical Data in Cluster Analysis - Exercise
    00:02
  • The Elbow Method or How to Choose the Number of Clusters
    06:11
  • The Elbow Method or How to Choose the Number of Clusters - Exercise
    00:02
  • Pros and Cons of K-Means
    03:23
  • Standardization of Features when Clustering
    04:32
  • Cluster Analysis and Regression Analysis
    01:31
  • Practical Example: Market Segmentation (Part 1)
    06:03
  • Practical Example: Market Segmentation (Part 2)
    06:58
  • What Can be Done with Cluster Analysis?
    04:47
  • EXERCISE: Species Segmentation with Cluster Analysis (Part 1)
    00:03
  • EXERCISE: Species Segmentation with Cluster Analysis (Part 2)
    00:02

  • Preview03:39
  • The Dendrogram
    05:21
  • Heatmaps
    04:34
  • Completing 100%
    00:26

Instructor

365 Careers
Creating opportunities for Business & Finance students
365 Careers
  • 4.5 Instructor Rating
  • 375,482 Reviews
  • 1,280,429 Students
  • 68 Courses

365 Careers is the #1 best-selling provider of finance courses on Udemy. The company’s courses have been taken by more than 1,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings.  

Currently, the firm focuses on the following topics on Udemy:  

1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA

2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics

3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing

4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook

5) Blockchain for Business

All of the company’s courses are:  

Pre-scripted  

Hands-on  

Laser-focused  

Engaging  

Real-life tested  

By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.  

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers’ courses are the perfect place to start. 

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