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Learn Marketing Analytics using Python and Machine Learning

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Learn Marketing Analytics using Python and Machine Learning, available at $69.99, has an average rating of 4.35, with 144 lectures, 3 quizzes, based on 73 reviews, and has 687 subscribers.

You will learn about Fundamental Concepts of Machine Learning Master Machine Learning Algorithms and perform hands on sessions Make powerful Machine Learning Models with SKLEARN and develop robust predictive frameworks Understand a business problem and relate to an analytical solution Learn how to convert the analytical insights into a business strategy and communicate the same for maximum impact Perform hands on projects on Retail Marketing Analytics that can be directly showcased in your resume This course is ideal for individuals who are Anyone who would like to learn Machine Learning and develop projects on Marketing Analytics or Anyone who wishes to start a career in Data Science or Students who would like to learn Machine Learning and has knowledge of school level Mathematics or Folks who are not programmers but would like to get started in the field of Data Science and Machine Learning or Anyone looking for hands on Machine Learning Projects or Anyone who wants to learn the art of using data to create powerful and actionable insights and convert into a communicable business strategy It is particularly useful for Anyone who would like to learn Machine Learning and develop projects on Marketing Analytics or Anyone who wishes to start a career in Data Science or Students who would like to learn Machine Learning and has knowledge of school level Mathematics or Folks who are not programmers but would like to get started in the field of Data Science and Machine Learning or Anyone looking for hands on Machine Learning Projects or Anyone who wants to learn the art of using data to create powerful and actionable insights and convert into a communicable business strategy.

Enroll now: Learn Marketing Analytics using Python and Machine Learning

Summary

Title: Learn Marketing Analytics using Python and Machine Learning

Price: $69.99

Average Rating: 4.35

Number of Lectures: 144

Number of Quizzes: 3

Number of Published Lectures: 144

Number of Published Quizzes: 3

Number of Curriculum Items: 147

Number of Published Curriculum Objects: 147

Original Price: ₹7,900

Quality Status: approved

Status: Live

What You Will Learn

  • Fundamental Concepts of Machine Learning
  • Master Machine Learning Algorithms and perform hands on sessions
  • Make powerful Machine Learning Models with SKLEARN and develop robust predictive frameworks
  • Understand a business problem and relate to an analytical solution
  • Learn how to convert the analytical insights into a business strategy and communicate the same for maximum impact
  • Perform hands on projects on Retail Marketing Analytics that can be directly showcased in your resume

Who Should Attend

  • Anyone who would like to learn Machine Learning and develop projects on Marketing Analytics
  • Anyone who wishes to start a career in Data Science
  • Students who would like to learn Machine Learning and has knowledge of school level Mathematics
  • Folks who are not programmers but would like to get started in the field of Data Science and Machine Learning
  • Anyone looking for hands on Machine Learning Projects
  • Anyone who wants to learn the art of using data to create powerful and actionable insights and convert into a communicable business strategy

Target Audiences

  • Anyone who would like to learn Machine Learning and develop projects on Marketing Analytics
  • Anyone who wishes to start a career in Data Science
  • Students who would like to learn Machine Learning and has knowledge of school level Mathematics
  • Folks who are not programmers but would like to get started in the field of Data Science and Machine Learning
  • Anyone looking for hands on Machine Learning Projects
  • Anyone who wants to learn the art of using data to create powerful and actionable insights and convert into a communicable business strategy

Are you eager to dive into the world of Machine Learning and kickstart your career in Data Science? Do you dream of showcasing your expertise in Retail Marketing Analytics with a compelling project portfolio? Imagine unlocking actionable insights from your machine learning endeavors to boost your resume. Look no further – this course is tailored just for you!

Crafted by a seasoned data scientist, this course is your gateway to mastering Machine Learning concepts and their practical applications in Retail Analytics. Here’s what you’ll achieve:

  1. Embark on your Data Science journey with confidence, guided by comprehensive learning modules.

  2. Navigate complex theories effortlessly through clear explanations and hands-on Python implementations, enhancing your understanding.

  3. Explore the realm of Marketing Analytics, unraveling its core concepts and methodologies.

  4. Harness your skills by tackling two real-world Machine Learning projects in the Retail Marketing domain, crafting compelling narratives around insights and recommendations.

Structured across ten modules, the course covers:

  1. Fundamentals of Machine Learning

  2. Data Preprocessing

  3. Feature Selection

  4. Clustering Algorithms

  5. Dimensionality Reduction

  6. Regression Algorithms

  7. Classification Algorithms

  8. Model Optimization

  9. Marketing Analytics Concepts

  10. Retail Marketing Analytics Projects

Plus, enrich your learning experience with interactive quizzes interspersed throughout the course, reinforcing your understanding and boosting confidence. All course materials, including codes and datasets, are readily accessible for download.

Join us now to unlock your potential in Machine Learning and Retail Marketing Analytics!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Machine Learning Express Code Folder

Chapter 2: Setting up the Environment

Lecture 1: Setting up the Jupyter and Code Folder

Chapter 3: Fundamentals of Machine Learning

Lecture 1: Overview of Machine Learning

Lecture 2: Fundamentals of Supervised and UnSupervised Learning

Lecture 3: Steps to build a Model

Lecture 4: Model Fit and Bias Variance Tradeoff

Lecture 5: Model Performance Metrics

Chapter 4: Data Preprocessing

Lecture 1: Set up the Machine Learning Express Code and Data Folder

Lecture 2: Introduction

Lecture 3: Import Libraries and Data

Lecture 4: Check Types of Features

Lecture 5: Independent and Target Split

Lecture 6: Check Percentage of Missing Values

Lecture 7: Imputation of Missing Values

Lecture 8: Outlier Handling

Lecture 9: Encoding Categorical Features

Lecture 10: Feature Scaling

Lecture 11: Feature Discretization

Lecture 12: Joining of Data

Lecture 13: Train Test Split

Lecture 14: Concept of Outlier Handling

Lecture 15: Concept of Imputation of Missing Values

Lecture 16: Concept of Feature Scaling

Chapter 5: Feature Selection

Lecture 1: Introduction

Lecture 2: Feature Selection for Regression Problems

Lecture 3: Feature Selection for Classification Problems

Chapter 6: Clustering Algorithms

Lecture 1: Introduction

Lecture 2: Business Problem and Data Description

Lecture 3: Hierarchical Clustering – Concept

Lecture 4: Hierarchical Clustering – Python Step 1

Lecture 5: Hierarchical Clustering – Python Step 2

Lecture 6: Hierarchical Clustering – Python Step 3

Lecture 7: K Means Clustering – Concept

Lecture 8: K Means Clustering – Python Step 1

Lecture 9: K Means Clustering – Python Step 2

Lecture 10: K Means Clustering – Python Step 3

Chapter 7: Dimensionality Reduction

Lecture 1: Introduction

Lecture 2: Business Problem and Data Description

Lecture 3: PCA – Concept

Lecture 4: PCA – Python Step 1

Lecture 5: PCA – Python Step 2

Lecture 6: Variable Clustering – Concept

Lecture 7: Variable Clustering – Python Step 1

Lecture 8: Variable Clustering – Python Step 2

Chapter 8: Regression Algorithms

Lecture 1: Introduction

Lecture 2: Business Problem 1 and Data Description

Lecture 3: Simple Linear Regression – Concept

Lecture 4: Simple Linear Regression – Python Step 1

Lecture 5: Simple Linear Regression – Python Step 2

Lecture 6: Simple Linear Regression – Python Step 3

Lecture 7: Business Problem 2 and Data Description

Lecture 8: Multiple Linear Regression – Concept

Lecture 9: Multiple Linear Regression -Python Step 1

Lecture 10: Multiple Linear Regression – Python Step 2

Lecture 11: Multiple Linear Regression – Python Step 3

Lecture 12: Decision Tree Regression – Concept

Lecture 13: Decision Tree Regression – Python Step 1

Lecture 14: Decision Tree Regression – Python Step 2

Lecture 15: Decision Tree Regression – Python Step 3

Lecture 16: Random Forest Regression – Concept

Lecture 17: Random Forest Regression – Python Step 1

Lecture 18: Random Forest Regression – Python Step 2

Lecture 19: Random Forest Regression – Python Step 3

Lecture 20: Gradient Boosting Regression – Concept

Lecture 21: Gradient Boosting Regression – Python Step 1

Lecture 22: Gradient Boosting Regression – Python Step 2

Lecture 23: Gradient Boosting Regression – Python Step 3

Chapter 9: Classification Algorithms

Lecture 1: Introduction

Lecture 2: Business Problem and Data Description

Lecture 3: Logistic Regression – Concept

Lecture 4: Logistic Regression – Python Step 1

Lecture 5: Logistic Regression – Python Step 2

Lecture 6: Logistic Regression – Python Step 3

Lecture 7: Logistic Regression – Python Step 4

Lecture 8: Logistic Regression – Python Step 5

Lecture 9: Decision Tree Classification – Concept

Lecture 10: Decision Tree Classification – Python Step 1

Lecture 11: Decision Tree Classification – Python Step 2

Lecture 12: Decision Tree Classification – Python Step 3

Lecture 13: Decision Tree Classification – Python Step 4

Lecture 14: Random Forest Classification – Concept

Lecture 15: Random Forest Classification – Python Step 1

Lecture 16: Random Forest Classification – Python Step 2

Lecture 17: Random Forest Classification – Python Step 3

Lecture 18: Random Forest Classification – Python Step 4

Lecture 19: Gradient Boosting Classification – Concept

Lecture 20: Gradient Boosting Classification – Python Step 1

Instructors

Rating Distribution

  • 1 stars: 0 votes
  • 2 stars: 5 votes
  • 3 stars: 9 votes
  • 4 stars: 20 votes
  • 5 stars: 39 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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