Data Mining

Course Code: DIGTR 136

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Data Mining

Course Sector:

Digital Transformation and Innovation

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Introduction

Training course introducion / brief

If you need to learn how to understand and create Machine Learning models used to solve business problems, this course is for you. You will learn in this course everything you need about Data Mining process, Machine Learning and how to implement Machine Learning algorithms in Data Mining. This course was designed to provide information in a simple and straight forward way so ease learning methods. You will from scratch and keep building your knowledge step by step until you become familiar with the most used Machine Learning algorithms.

Learn everything about Data Mining and its applications
Understand Machine Learning and its connection with Data Mining
Learn all Machine Learning algorithms, their types, and their usage in business
Learn how to implement Machine Learning algorithms in different business scenarios
Learn how to install and use Python programming language to create machine learning algorithms in a simple way
Learn how to import your data sets into Python and make required cleaning before creating the algorithms
Learn how to interpret the results of each algorithms and compare them with each other to choose the optimum one
Learn how to create graphs in Pythons, such as scattered and regression graphs and use them in your analyses

- Business and technology leaders

- Business Unit Managers

- Business Development Consultants

- General Managers / Regional Managers

- Senior and mid-level leaders

- individual leaders of all levels in the organization

- Art Director

- Marketing Consultants

- Marketing Development Manager

Introduction to Data Mining

Introduction to Machine Learning
How Does Machine Learning Work
Machine Learning Algorithms Classification
Setup Programming Environment
Install Anaconda package
Supervised Learning Algorithms

Introduction to Supervised Learning Algorithms

Types of Variables
Introduction to Regression Model
Regression Model Slope
Intercept Value
R-Squared
P-Value
Simple Linear Regression
Concepts used in Machine Learning (Important**)

Overview on the dataset

Create Simple Linear Regression Model in Python-Part 1
Create Simple Linear Regression Model in Python-Part 2
Create Simple Linear Regression Model in Python-Part 3
Create Simple Linear Regression Model in Python-Part 4
Multiple Linear Regression
Dummy Variables
Step-wise Approach
Assumptions of Multiple Linear Regression

Overview on the business problem data

Create Multiple Linear Regression Model in Python-Part 1
Create Multiple Linear Regression Model in Python-Part 2
Create Multiple Linear Regression Model in Python-Part 3
Create Multiple Linear Regression Model in Python-Part 4
Polynomial Regression
Overview on the business problem data
Create Polynomial Regression Model in Python-Part 1
Create Polynomial Regression Model in Python-Part 2
Course Rating

Introduction to Classification

Introduction to Logistic Regression
Confusion Matrix
Standard Scaler
Overview on the business problem data
Create Logistic Regression Model in Python-Part 1
Create Logistic Regression Model in Python-Part 2
KNN Classification Algorithm
Create KNN Model in Python
Support Vector Machine (SVM) Classification Algorithm
Create Support Vector Machine in Python
Naive Bayes Algorithm Part 1
Naive Bayes Algorithm Part 2
Create Naive Bayes Model in Python
Decision Tree Algorithm
Create Decision Tree Model in Python
Random Forest Algorithm
Create Random Forest Model in Python

Unsupervised Learning Algorithms

Review Unsupervised Learning Algorithms
Hierarchical Clustering Algorithm
Dendrogram Diagram Method
Overview on the business problem data
Create Hierarchical Clustering Algorithm in Python-1
Create Hierarchical Clustering Algorithm in Python-2
K-means Clustering Algorithm
Using Elbow Method to Determine Optimal Number of Clusters
Create K-means Clustering Algorithm Model in Python - 1
Create K-means Clustering Algorithm Model in Python - 2
Association Rules (Market Basket Analysis)
Overview on the business problem data
Create Association Rules (Market Basket Analysis) Model in Python - 1
Create Association Rules (Market Basket Analysis) Model in Python - 2
Create Association Rules (Market Basket Analysis) Model in Python - 3

Deep Learning

Introduction to Deep Learning
How Does Deep Learning Work Part 1
How Does Deep Learning Work Part 2
Activation Functions
What is Tensorflow
Create Artificial Neural Network Model in Python Part-1
Create Artificial Neural Network Model in Python Part-2

Boost certificate of completion

BOOST's Professional Attendance Certificate “BPAC” is always given to the delegates after completing the training course, and depends on their attendance of the program at a rate of no less than 80%, besides their active participation and engagement during the program sessions.

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Shareable Certificate

Earn certificate upon completion

COURSE METHODOLOGY

Our Training programs are implemented by combining the participants' academic knowledge and practical practice (30% theoretical / 70% practical activities).

At The end of the training program, Participants are involved in practical workshop to show their skills in applying what they were trained for. A detailed report is submitted to each participant and the training department in the organization on the results of the participant's performance and the return on training. Our programs focus on exercises, case studies, and individual and group presentations.

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TRAINING COURSE: Data Mining