Free Download Unleash The Power Of Pycaret For Marketing Analytics
Published 7/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.84 GB | Duration: 4h 3m
The Complete PyCaret Guide for Marketing Insights
What you'll 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
Requirements
An understanding of Python
We use Google Colab in this course, hence there will be no installations
Description
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 like Advertools. The course will cover:The low-code solution to analyzing millions of customer interactions and unlocking hidden insightsHow to accurately predict customer churn and create targeted retention campaigns in just a few lines of codeRevolutionize customer segmentation with state-of-the-art clustering algorithms and increase sales by understanding buyer personasTransform your marketing strategy by gaining a deeper understanding of customer sentiment with cutting-edge topic modelingLeverage 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. 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. PyCaret, developed by Moez Ali is an AutoML library with a wide range of applications: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 are generated by PyCaret to communicate critical insights to your stakeholders.PyCaret Anomaly Detection module comes in useful to detect spikes in demand for inventory management, for detecting anomalous reactions to Social Media posts, etc.PyCaret's Association Rule Mining course helps you identify patterns within transaction datasets for Ecommerce datasets, or if you plan to service Hypermarkets or Supermarket chains. 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 now no longer need to read these documents when you can instead leverage unsupervised learning to understand what the themes in the document collection are. 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 miningQuickly undertake data preprocessing, feature engineering, model selection, and evaluation using Auto Machine Learning Communicate insights and results to stakeholdersGain hands-on experience with real-world data and use casesYou 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.Overview
Section 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?
Section 2: Recency Frequency Monetary (RFM) Analysis
Lecture 4 What is RFM analysis?
Lecture 5 How you would use RFM Analysis
Lecture 6 RFM Example
Lecture 7 Tips and Tricks
Section 3: Customer Segmentation with PyCaret Clustering
Lecture 8 PyCaret Clustering Module Workflow
Lecture 9 Install PyCaret, then Load the Dataset
Lecture 10 Explore the dataset
Lecture 11 Step 1: PyCaret Setup Function
Lecture 12 Step 2: Create_Model Function
Lecture 13 Overview of Clustering Evaluation Metrics
Lecture 14 Step 3: Assign Model
Lecture 15 Step 4: Plot Model
Lecture 16 Summary: Why is it important to visualize the clusters?
Section 4: Sentiment Analysis for Marketing Analytics
Lecture 17 What is Sentiment Analysis?
Lecture 18 How you would use Sentiment Analysis for marketing analytics
Lecture 19 Sentiment Analysis with Textblob (Financial News Dataset)
Lecture 20 Vader Sentiment Analysis
Lecture 21 Text 2 Emotion
Section 5: PyCaret Anomaly Detection
Lecture 22 Overview of Anomaly Detection
Lecture 23 Types of Anomalies
Lecture 24 Project 1: Social Media Monitoring Example
Section 6: PyCaret Topic Modelling
Lecture 25 Intuition behind Topic Modelling
Lecture 26 How LDA works
Lecture 27 Topic coherence: Evaluating the results of topic modelling
Lecture 28 Load the dataset
Lecture 29 Why the Setup Function is Vital
Lecture 30 Step One: Setup Function
Lecture 31 Step Two: Create Function
Lecture 32 Step Three: Assign Function
Lecture 33 Step Four: Plot Model Function
Lecture 34 Step Five: Evaluate Function
Lecture 35 Save Model
Lecture 36 The type of the data influences the interpretability of results!
Section 7: PyCaret Association Rule Module
Lecture 37 What is Association Rule Mining?
Lecture 38 How you will use Association Rule Mining for your company
Lecture 39 What is Support?
Lecture 40 Part 1: Explore the dataset
Lecture 41 Summary of Association Rule Mining Concepts
Lecture 42 Part 2: Create the Model and Examine the rules
Lecture 43 Part 3: Visualize the results of the Association Rule Mining Exercise
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.
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