Certified Machine Learning Expert (CMLE) Using Python - Onsite @ Singapore

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