Course Information

Duration: 5 Day / 40 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 are interested in pursuing a career in the areas of Artificial Intelligence / Machine Learning

Course Objective

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

Learn how to use Python for Data Mining, Data Engineering, and apply Machine Learning algorithms to Analyze and Predict with Structured and Unstructured Data

Pre-Requisite

It is preferred that participants successfully completed and pass Data Analytics Essentials (DAE) or Python Programming Essentials (PPE)

Examination

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

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 Right Parameters for a Model

Module 9 Deep Dive Into Deep Learning

Topics Covered

• Understanding 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