For more information: www.casugol.com/cmle

Duration: 4 Day / 32 Hours

Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination

Who Should Attend: Data Analyst, Finance Analyst, HR Analyst, System Analyst, CIO, or Anyone who is interested in pursuing a career in the areas of Artificial Intelligence / Machine Learning

Course Objective

Acquire advanced knowledge on how to use Python Programming to apply powerful Artificial Intelligence / Machine Learning techniques to gain real-world insight.

Learn how to apply Artificial Intelligence / Machine Learning techniques to analyze and organize patterns, trends, and data to make accurate real-world predictions.

Pre-Requisite

No pre-requisite. Certified Machine Learning Expert (CMLE) is suitable for everyone.

Examination

Participants are required to attempt an examination upon completion of the course. This exam tests a candidate’s knowledge and skills related to Artificial Intelligence and Machine Learning based on the syllabus covered

Participants are expected to score a minimum of 70% to pass the examination

Course Outline

Module 1 Introduction to AI and Machine Learning

Topics Covered

What is Artificial Intelligence (AI)
Concepts of machine learning
Data and machine learning
Real-world applications of machine learning
How machine learning works

Module 2 Data Structures & Managing Data Using Python

Topics Covered

Data and Data Types
Deep Dive into Python
Data Types
Variable Operators in Python
Data Vectors and Data Frames
Reading and Writing Data Files to Python
Communicating with Database via Python
Executing SQL Using Python
Joining Structured & Semi Structured Data with Python
Big Data Concepts & Application of Python

Module 3 Exploring Data Using Python

Topics Covered

Bar Chart
Pie Chart
Trend Chart
Histogram
Box Plot
Scattered Plot & Correlation
Other Chart

Module 4 Basic Classification Models & Techniques

Topics Covered

Concept of Classification
Supervised and Unsupervised Classification
Decision Tree Classification
Random Forest Classification
Naive Bayes Classification
Support Vector Machine

Module 5 Regression Methods and Forecasting

Topics Covered

Concept of Regression Modelling
Modelling Stages
Simple linear Regression
Multiple Linear Regression
Refining the Model
Model Validation and Prediction
Logistic Regression

Module 6 Finding Data Patterns Using Association Rules

Topics Covered

Concepts of Association Rules
Market Basket Analysis (MBA)
Support, Confidence & Lift
Other Techniques of Association
Application of Association

Module 7 K-Means Clustering

Topics Covered

Cluster Analysis
Hierarchical Clustering
K-Means Clustering

Module 8 Evaluating and Improving Model Performance

Topics Covered

Model Evaluation and Comparison
Parameters to Evaluate the Model Accuracy
Selection of the Right Parameters for a Model

Module 9 Deep Dive Into Deep Learning

Topics Covered

Understanding the Learning Representation of Data
Fundamentals of Deep Learning
How Deep Learning Works
Deep Learning and Its Application
Future of Deep Learning

Certified Machine Learning Expert (CMLE) involves rigorous usage of real-time case studies, hands-on exercises, and group discussion