AI020: AI Implementation Basics

Agenda:

• Module 1: Introduction to Python for Data Science and AI
  • Brief introduction to Python basic elements (variables, functions, loops, etc.) using simple examples
  • Introduction to Python modules for extending functionality (installing modules with pip, introducing modules such as: os, NumPy, Matplotlib using examples)
  • Introduction to data manipulation and visualization with pandas and matplotlib using easily understandable datasets
  • Summary and discussion on how to apply what has been learned to one's own context

• Module 2: Introduction to Data Extraction and Data Preparation
  • Processing text files such as logs, CSV and Excel with Pandas and Regex
  • Reading data from databases, including SQL and NoSQL
  • Extracting data from websites with BeautifulSoup and Requests
  • Data extraction from additional formats such as PDF, Word and from images with OCR techniques
  • Techniques for data preparation and cleansing with Pandas (including data manipulation, data transformation and error handling)
  • Best Practices
  • Summary and discussion on how to apply what has been learned to one's own context

• Module 3: Introduction to Machine Learning
  • Introduction to Machine Learning: terminology, problem descriptions and use cases (classification, regression, prediction, etc.)
  • Python tools for Machine Learning: Introduction to using Python packages such as pandas and scikit-learn for implementing Machine Learning techniques
  • Classification techniques: Introduction to various classification techniques, such as Random Forests, kNN, etc., and their application to use cases
  • Regression techniques: Introduction to various regression techniques, such as Linear Regression, and their application to use cases
  • Summary and discussion on how to apply what has been learned in one's own context

• Module 4: Introduction to Deep Learning and AI
  • Introduction to neural networks and Deep Learning
  • Important concepts: Artificial neurons, weights, training and architecture
  • Python and libraries for Deep Learning: TensorFlow and Keras
  • Implementation of simple neural networks in various application scenarios
  • Usage of pre-trained networks (TensorFlow Hub) and Transfer Learning
  • Practical examples and exercises for designing, adapting and training models
  • Summary and discussion on how to transfer what has been learned to one's own context

• Certificate Examination

Objectives:

• Learn Python programming fundamentals, understand basic constructs and elements of Python, get to know important standard libraries for data analysis.
• Extract data from various sources, prepare and clean data, process text files, read data from databases, scrape websites, extract data from additional formats.
• Classify machine learning concepts and apply basic techniques in real use cases.
• Understand neural network fundamentals, learn important concepts, design and adapt models, work with Python and TensorFlow, utilize pre-trained networks and transfer learning.

Furthermore, the course provides a good foundation for additional advanced courses, e.g.:
AI030 AI Implementation Advanced
AI100 AI Officer
AI135 AI Auditor
AI050 AI Security Specialist

Target audience:

• Developers
• IT Professionals
• AI Officers
• AI Auditors

Prerequisites:

Description:

The certificate course AI020 AI Implementation Basics is your gateway into the world of Data Science and AI at qSkills™.

The course provides you with fundamentals of Python programming, with focus on syntactic basic elements and concepts. Additionally, important standard libraries for data analysis are introduced, such as NumPy, pandas and Matplotlib.

Data sources are not limited to relational databases or Excel files. Often our data exists in various formats: From PDF files via web pages to images, everything can be included. Learn through illustrative examples techniques to extract data from various sources and prepare it for further use. The course includes processing of text files like logs, CSV and Excel, reading data from databases, extracting data from web pages and data extraction from additional formats like PDF and Word. Furthermore, techniques for data preparation and cleansing are presented.

You learn a series of central techniques that find application in every data analysis. Through introduction of important basic concepts and techniques based on meaningful use cases, participants will come into contact with different areas of Machine Learning, such as classification, regression and prediction.

Learn how to uncover hidden patterns in data and make precise predictions. Utilize innovative techniques that enable revolutionary advances in many industries. To deliver these contents efficiently and sustainably, the course particularly focuses on the usage of established Python libraries such as pandas and scikit-learn.

You will be introduced to a world where computers learn and can solve complex tasks and experience fundamentals about neural networks and Deep Learning. You learn important concepts like artificial neurons, model architectures and model training. Through practical examples, it is conveyed how effective models can be designed, adapted and trained with Python and libraries like TensorFlow and Keras.

The course also covers the usage of pre-trained networks from TensorFlow Hub and Transfer Learning to accelerate the development of AI applications. At the end of the course, participants will be able to implement neural networks themselves in existing frameworks and apply them to their problem.

The course is ideal for IT professionals and developers who want to advance their education in the areas of Data Science and Artificial Intelligence.

Exam: