Automated Machine learning (AutoML) for Marketing Analytics

Automated Machine learning (AutoML) for Marketing Analytics, available at $64.99, has an average rating of 4.38, with 61 lectures, 6 quizzes, based on 4 reviews, and has 68 subscribers.

You will learn about Understand the fundamentals of PyCaret: Gain a solid understanding of PyCaret, its features, and how it can be used effectively for marketing analytics tasks. Learn how to apply topic modelling techniques using PyCaret to uncover underlying themes and patterns in customer feedback, social media data etc Analyze customer churn and predict churn likelihood: Discover how to leverage PyCaret to analyze customer churn and build predictive models Perform sentiment analysis on customer feedback: Explore sentiment analysis techniques Discover how to leverage PyCaret for clustering analysis to segment customers into distinct groups based on their behavior and preferences Understand how to apply association rule mining techniques in PyCaret to analyze transactional data Learn to conduct RFM analysis, a powerful method for segmenting customers based on their transactional behavior This course is ideal for individuals who are You are a marketing professional excited using data to drive marketing decisions. You may have a background in marketing, but are not necessarily an expert in data analysis or programming. The low code aspect of PyCaret will appeal to you if you are looking for a more user-friendly solution to perform marketing analytics. or You are a data professional interested in using PyCaret for marketing analytics. You may have a background in data analysis or programming and are looking for a low code solution that can streamline your work and make it easier to perform marketing analytics. or You are a business professional who is interested in using data to drive business decisions. You may have a background in business, but are not necessarily an expert in data analysis or programming. or You are a market researcher who is interested in using PyCaret to perform marketing analytics. You may have a background in market research, but are not necessarily an expert in data analysis or programming. It is particularly useful for You are a marketing professional excited using data to drive marketing decisions. You may have a background in marketing, but are not necessarily an expert in data analysis or programming. The low code aspect of PyCaret will appeal to you if you are looking for a more user-friendly solution to perform marketing analytics. or You are a data professional interested in using PyCaret for marketing analytics. You may have a background in data analysis or programming and are looking for a low code solution that can streamline your work and make it easier to perform marketing analytics. or You are a business professional who is interested in using data to drive business decisions. You may have a background in business, but are not necessarily an expert in data analysis or programming. or You are a market researcher who is interested in using PyCaret to perform marketing analytics. You may have a background in market research, but are not necessarily an expert in data analysis or programming.

Enroll now: Automated Machine learning (AutoML) for Marketing Analytics

Summary

Title: Automated Machine learning (AutoML) for Marketing Analytics

Price: $64.99

Average Rating: 4.38

Number of Lectures: 61

Number of Quizzes: 6

Number of Published Lectures: 61

Number of Published Quizzes: 6

Number of Curriculum Items: 67

Number of Published Curriculum Objects: 67

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the fundamentals of PyCaret: Gain a solid understanding of PyCaret, its features, and how it can be used effectively for marketing analytics tasks.

Learn how to apply topic modelling techniques using PyCaret to uncover underlying themes and patterns in customer feedback, social media data etc

Analyze customer churn and predict churn likelihood: Discover how to leverage PyCaret to analyze customer churn and build predictive models

Perform sentiment analysis on customer feedback: Explore sentiment analysis techniques

Discover how to leverage PyCaret for clustering analysis to segment customers into distinct groups based on their behavior and preferences

Understand how to apply association rule mining techniques in PyCaret to analyze transactional data

Learn to conduct RFM analysis, a powerful method for segmenting customers based on their transactional behavior

Who Should Attend

You are a marketing professional excited using data to drive marketing decisions. You may have a background in marketing, but are not necessarily an expert in data analysis or programming. The low code aspect of PyCaret will appeal to you if you are looking for a more user-friendly solution to perform marketing analytics.

You are a data professional interested in using PyCaret for marketing analytics. You may have a background in data analysis or programming and are looking for a low code solution that can streamline your work and make it easier to perform marketing analytics.

You are a business professional who is interested in using data to drive business decisions. You may have a background in business, but are not necessarily an expert in data analysis or programming.

You are a market researcher who is interested in using PyCaret to perform marketing analytics. You may have a background in market research, but are not necessarily an expert in data analysis or programming.

Target Audiences

You are a marketing professional excited using data to drive marketing decisions. You may have a background in marketing, but are not necessarily an expert in data analysis or programming. The low code aspect of PyCaret will appeal to you if you are looking for a more user-friendly solution to perform marketing analytics.

You are a data professional interested in using PyCaret for marketing analytics. You may have a background in data analysis or programming and are looking for a low code solution that can streamline your work and make it easier to perform marketing analytics.

You are a business professional who is interested in using data to drive business decisions. You may have a background in business, but are not necessarily an expert in data analysis or programming.

You are a market researcher who is interested in using PyCaret to perform marketing analytics. You may have a background in market research, but are not necessarily an expert in data analysis or programming.

While assembling your portfolio both when you’re looking for a new role (either as a beginner or as an experienced data analyst) or if you’re pitching your services on a freelance basis, the strength of your marketing analytics portfolio depends on:

(1) the diversity of the projects undertaken – marketing analytics projects will frequently showcase clustering, regression, and classification problems. Go beyond to showcase Topic Modelling for new product development.

(2) how well contextualized the projects are – this is your chance to shine and demonstrate your business acumen and your insight into the constraints and domain knowledge the sector grapples with – be it banking, telecommunication, or e-commerce, you’ll find you can not only work with different types of data, but you can stack the insights into the context

(3) Showcase your ability to leverage citizen data insights within the institution – you can position yourself as the go-to resource person on auto-machine learning and specialized e-commerce marketing Python packages.

The course will cover:

The low-code solution to analyzing millions of customer interactions and unlocking hidden insights

How to accurately predict customer churn and create targeted retention campaigns in just a few lines of code

Revolutionize customer segmentation with state-of-the-art clustering algorithms and increase sales by understanding buyer personas

Transform your marketing strategy by gaining a deeper understanding of customer sentiment with cutting-edge topic modeling

Leverage association rule mining to increase sales and enhance customer lifetime value through optimized cross-selling and up-selling campaigns.

If you’re a beginner, worry not, we are working with an Auto Machine Learning Package where you can download the codebook, change the dataset, and run through the different steps to glean similar insights as the exercises we walk through together by yourself when you use your own datasets (however, if one is a complete beginner experimenting with their own datasets for a project at work, it’s best to have contributions reviewed by a Data Scientist – AutoML provides an easy starting point,  and eliminates “points of frustration”, yet precise and usable solutions need experts).

Plus, we are primarily working with inbuilt datasets which means you don’t have to trip yourself up in downloading the datasets and loading them again into your notebook and your environment (the objective here is to eliminate frustrations at the beginning of a learning journey, and to instead stack wins – this insight, derived from habit formation research, is especially useful as a beginner where working professionals may not find the time and energy to invest in learning a skill ).

PyCaret, developed by Moez Ali is an AutoML library with a wide range of applications:

1. If you’re an existing freelance data science analytics provider, you can double the services you provide in analytics by using PyCaret. Leverage the visuals that PyCaret generates to communicate critical insights to your stakeholders.

2. PyCaret Anomaly Detection module is useful to detect spikes in demand for inventory management, detect anomalous reactions to Social Media posts, etc.

3. PyCaret’s Association Rule Mining course helps you identify patterns within transaction datasets for e-commerce datasets, or if you plan to service Hypermarkets or Supermarket chains.

4. PyCaret’s Topic Modeling for new product development or for identifying themes from large amounts of unstructured text. Whether you are combining through 1000s of product reviews to identify new features that need to be adopted, you no longer need to read these documents when you can instead leverage unsupervised learning to glean the themes in the document collection.

This course is designed for marketing analysts, data scientists, and business leaders who want to improve their skills in marketing analytics and gain a competitive advantage.

Whether a beginner or an experienced professional, this course will help you gain new insights and skills to enhance your marketing strategies.

Here are some of the benefits of taking this course:

Apply RFM analysis, customer churn prediction, sentiment analysis, topic modeling, and association rule mining

Quickly undertake data preprocessing, feature engineering, model selection, and evaluation using Auto Machine Learning

Communicate insights and results to stakeholders with compelling visuals that enhance explainability and effectively aid decision-making

Gain hands-on experience with real-world data and use cases

You will learn how to use machine learning and NLP in Python to create predictive models, visualize and communicate results, and apply the concepts to real-world marketing challenges.

We will be using Google Colab in this course, so let us get started.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to the Course!

Lecture 2: Overview of Sector-specific Use Cases

Lecture 3: How can you get the most out of this course?

Lecture 4: Setting up Google Colab

Lecture 5: For beginners: Inbuilt Datasets to start your learning journey

Chapter 2: Recency Frequency Monetary (RFM) Analysis

Lecture 1: What is RFM analysis?

Lecture 2: How you would use RFM Analysis

Lecture 3: RFM Example

Lecture 4: Tips and Tricks

Chapter 3: Customer Churn Prediction with PyCaret Classification

Lecture 1: What is Customer Churn?

Lecture 2: PyCaret Workflow for the Classification Module

Lecture 3: Upload and Explore the Dataset

Lecture 4: Explore the dataset

Lecture 5: Preprocessing with PyCaret's Setup Function

Lecture 6: Compare, then Create Models

Lecture 7: Examine Confusion Matrix with PyCaret Plot_Model

Lecture 8: Examine ROC with PyCaret Plot_Model

Lecture 9: Feature Importance Plot with PyCaret Plot_Model

Lecture 10: Examine Precision Recall Curve with PyCaret Plot_Model

Lecture 11: Class Reports provide insight into imbalanced classes using Evaluate_Model

Lecture 12: Make predictions with Predict_Model

Chapter 4: Customer Segmentation with PyCaret Clustering

Lecture 1: PyCaret Clustering Module Workflow

Lecture 2: Install PyCaret, then Load the Dataset

Lecture 3: Explore the dataset

Lecture 4: Step 1: PyCaret Setup Function

Lecture 5: Step 2: Create_Model Function

Lecture 6: Overview of Clustering Evaluation Metrics

Lecture 7: Step 3: Assign Model

Lecture 8: Step 4: Plot Model

Lecture 9: Summary: Why is it important to visualize the clusters?

Chapter 5: Customer Lifetime Value

Lecture 1: Calculate Customer Lifetime Value based on Customer Segmentation

Lecture 2: Steps to Calculate CLV (RFM approach)

Lecture 3: CLV using Linear Regression on the basis of RFM

Chapter 6: Sentiment Analysis for Marketing Analytics

Lecture 1: What is Sentiment Analysis?

Lecture 2: How you would use Sentiment Analysis for marketing analytics

Lecture 3: Sentiment Analysis with Textblob (Financial News Dataset)

Lecture 4: Vader Sentiment Analysis

Lecture 5: Text 2 Emotion

Chapter 7: PyCaret Anomaly Detection

Lecture 1: Overview of Anomaly Detection

Lecture 2: Types of Anomalies

Lecture 3: Project 1: Social Media Monitoring Example

Chapter 8: PyCaret Topic Modelling

Lecture 1: Intuition behind Topic Modelling

Lecture 2: How LDA works

Lecture 3: Topic coherence: Evaluating the results of topic modelling

Lecture 4: Load the dataset

Lecture 5: Why the Setup Function is Vital

Lecture 6: Step One: Setup Function

Lecture 7: Step Two: Create Function

Lecture 8: Step Three: Assign Function

Lecture 9: Step Four: Plot Model Function

Lecture 10: Step Five: Evaluate Function

Lecture 11: Save Model

Lecture 12: The type of the data influences the interpretability of results!

Chapter 9: PyCaret Association Rule Mining

Lecture 1: What is Association Rule Mining?

Lecture 2: Summary of Association Rule Mining Concepts

Lecture 3: What is Support?

Lecture 4: Part 1: Explore the dataset

Lecture 5: How you will use Association Rule Mining for your company

Lecture 6: Part 2: Create the Model and Examine the rules

Lecture 7: Part 3: Visualize the results of the Association Rule Mining Exercise

Lecture 8: Efficient Apriori – An alternative to Association Rule Mining with PyCaret

Instructors

DatOlympia Learning Solutions
DatOlympia: A Data Literacy Company

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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!