Ask for more dates, other languages or a different delivery mode as needed and we will do our best to meet your needs.
| Language | Mode | Start Date |
|---|---|---|
| German | Virtual Class or Classroom | On request |
| French | Virtual Class or Classroom | On request |
| Italian | Virtual Class or Classroom | On request |
| English | Virtual Class or Classroom | On request |
What you will get
- Attend one day training with instructor in presence classroom or virtual class
- Student guide
- Mock lab exam
- Exam voucher
Course Overview
Certification Exam
Prerequisites
- A foundational understanding of high-level programming languages such as Python and R is required.
- (Reccomended) basic familiarity with concepts including cloud services, relational databases, algebra and statistics, algorithms, data structures, data visualization, and proficiency in a high-level programming language.
Course Audience
What Skills Will You Learn?
- Concepts, Terminologies, Evolution, and Business Drivers of AI:
- Overview of AI: Definition, scope, and applications.
- Evolution of AI: Historical background and advancements.
- Terminologies: Understanding key terms such as machine learning, neural networks, natural language processing, etc.
- Business Drivers: Identifying reasons organizations adopt AI, including efficiency improvement, decision-making support, and competitive advantage.
- Fundamentals of Machine Learning:
- Introduction to Machine Learning: Understanding the concept of learning from data without being explicitly programmed.
- Types of Machine Learning: Supervised learning, unsupervised learning, and reinforcement learning.
- Algorithms and Models: Overview of popular algorithms such as linear regression, decision trees, support vector machines, etc.
- Fundamentals of Relational Databases and SQL:
- Relational Databases: Understanding the structure and principles of relational databases.
- SQL Language: Introduction to SQL for querying and managing relational databases, covering basic commands like SELECT, INSERT, UPDATE, DELETE, etc.
- Fundamentals of Statistics and Data Visualization:
- Statistics Basics: Overview of key statistical concepts such as mean, median, mode, variance, standard deviation, probability distributions, hypothesis testing, etc.
- Data Visualization: Importance and techniques for presenting data visually using charts, graphs, and other visualization tools.
- Fundamentals of the Python Programming Language:
- Python Basics: Introduction to Python syntax, data types, control structures (if statements, loops), functions, and modules.
- Libraries for Data Science: Overview of popular Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn for data manipulation, analysis, and visualization.
- Concepts of Algorithms and Data Structures:
- Algorithms: Understanding algorithms as step-by-step procedures for solving problems, including searching, sorting, and optimization algorithms.
- Data Structures: Overview of fundamental data structures such as arrays, linked lists, stacks, queues, trees, and graphs.
- Different Implementation Strategies for Data Structures:
- Implementation Choices: Understanding the trade-offs between different data structure implementations in terms of time complexity, space complexity, and ease of use.
- Examples and Applications: Exploring real-world scenarios where different data structures are employed and understanding the selection criteria for each.
Course Layout
Module 0: Course Introduction
- Course Highlights
- Course Learning Outcomes
- Module Structure
- Exam Overview
Module 1: AI Definition, Evolution, and Concepts
- Introduction to Artificial Intelligence
- Basics of Artificial Intelligence
- Fundamentals of Machine Learning
Module 2: Fundamentals of Databases
- Basics of Databases
- Concepts of Relational Databases
- Database Languages
- Data Types and Constraints
- SQL Commands
- SQL Keywords
- SQL Operators
- SQL Functions and Additional Objects
- SQL Joins
Module 3: Fundamentals of Statistics
- Basics of Statistics
- Fundamentals of Data Visualization
- Visual Data Visualization Types
- Data Visualization Popular Tools
Module 4: Python Programming Fundamentals
- Introduction and Evolution of Python
- Python Programming Concepts
- Python Object Types
- Python Programming Concepts
- Python Debugging Concepts
Module 5: Foundation and Implementation of Data Structures and Algorithms
- Fundamentals of Data Structures
- Fundamentals of Algorithms
Module 6: Mock Exam
