About this Event
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
Event Venue & Nearby Stays
One Fullerton, 1 Fullerton Road, Singapore, Singapore
SGD 485.00