Data Mining for Business in Python 2021

9 Data Mining algorithms for Data Science, Machine Learning and Explainable Artificial Intelligence. 18 Case Studies.

What you'll learn
Survival Analysis
Cox Proportional Hazard Regression
CHAID
Cluster Analysis - Gaussian Mixture Model
Association Rule Learning
Random Forest
LIME
SHAP
Data Mining
Principal Component Analyisis
XGBoost
Manifold Learning

Requirements
Statistics - Linear and Logistic Regression
Basic Python
Description
Are you looking to learn how to do Data Mining like a pro? You have come to the right place.

Welcome to the most exciting Data Mining course in Python. I will show you the most impactful algorithms that I have witnessed in my professional career to derive meaningful insights.

In the age of endless spreadsheets, it is easy to feel overwhelmed with so much data. This is where Data Mining techniques come in. To swiftly analyze, find patterns, and deliver an outcome to you. For me, the Data Mining value added is that you stop the number crunching and pivot table creation, leaving time to come with actionable plans based on the insights.

Now, why should you enroll in the course? Let me give you four reasons.

The first is that you will learn the models' intuition without focusing too much on the math. It is crucial that you know why a model makes sense and the underlying assumptions behind it. I will explain to you each model using words, graphs, and metaphors, leaving math and the Greek alphabet to the bare minimum.

The second reason is the thorough course structure of the most impactful Data Mining techniques. Based on my experience, the course curriculum has the algorithms I believe to be most impactful, up-to-date, and sought after. Here is the list of the algorithms we will learn:

Supervised Learning

Survival Analysis

Cox Proportional Hazard Regression

CHAID

Unsupervised Learning

Cluster Analysis - Gaussian Mixture Model

Dimension Reduction – PCA and Manifold Learning

Association Rule Learning

· Explainable Artificial Intelligence

Random Forest and Feature Importance

LIME

XGBoost and SHAP

The third reason is that we code together, line by line. Programming is challenging, especially for beginners. I will guide you through every code snippet. I will also explain all parameters and functions that you need to use, step by step. In the end, you will have code templates ready to use in your problems.

The final reason is that you practice, practice, practice. At the end of each section, there is a challenge. The goal is that you apply immediately what you have learned. I give you a dataset and a list of actions you need to take to solve it. I think it is the best way to really cement all the techniques in you. Hence, there will be 2 case studies per technique.

I hope to have spiked your interest, and I am looking forward to seeing you inside!

Who this course is for:
People looking to learn Data Mining algorithms

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