Certified Applied Artificial Intelligence Professional (CAAI)

For more information: https://casugol.com/course/caai

International Acclaimed Certification. 5-Star Reviews

Suitable for everyone. Learn in an Interactive, Supportive, and Encouraging Environment.

• Duration: 16 Hours
• Certification: Participants will receive a Certificate of Competency upon completing the course and passing the examination

Course Objective

• Acquire advanced knowledge and skills on developing Artificial Intelligence (AI) models in Artificial Intelligence (AI) technologies and its application beyond just operating GEN AI tools.
• Learn how to use Python Programming x GEN AI for Machine Learning, Deep Learning, Natural Language Processing (NLP) and basic computer vision.

Examination

NA

• 
  MODULE 1 - Introduction to Applied Artificial Intelligence

  
  

  
  

  What is Applied Artificial Intelligence?
• Understanding the Concepts of Artificial Intelligence
• Real World Applications of Applied Artificial Intelligence
• Relationship Between Data Science and Artificial Intelligence
• Introduction to Machine Learning, Deep Learning, and Neural Networks
• Data Management and Governance for Artificial Intelligence

• Introduction to Python Editors and IDE
• Basic Programming Rules in Python
• Understanding Variables in Python – Integers, Float, and Strings
• Conditional Operators and Control Loops in Python – If, Else if, For, While
• Packages / Libraries in Python for Artificial Intelligence – NumPy, Pandas, SciPy, Scikit-Learn, MatPlotLib

• Understanding the Different Types of Data
• Reading and Writing Data from Various Sources
• Data Preparation for Pre-processing and Cleaning
• Techniques for Data Manipulation using Python Tools
• Data Formatting, Normalization, and Data Encoding

• Introduction to Regression Modelling
• What is a Linear Regression Model, Multiple Linear Regression Model and Logistic Regression Model
• Model Validation, Prediction and Refining of Regression Models
• Key Components of Classification Models in Machine Learning
• Difference Between Supervised vs. Unsupervised Classification
• Classification Techniques – Decision Tree Classification, Random Forest Classification, and Naïve Bayes Classification
• What is Clustering Analysis
• Introduction to K-Means Clustering and Hierarchical Clustering

• Introduction to Deep Learning
• Common Deep Learning Algorithms – MLP, BM, RBM, DBN, Autoencoders
• Neural Networks in Deep Learning
• The main characteristics of Neural Networks
• Introduction to Python TensorFlow and KERAS for Deep Learning
• Developing a Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN)

• What is Natural Language Processing (NLP)?
• Text Pre-processing for Natural Language Processing (NLP)
• Understanding Recurrent Neural Networks
• Using Recurrent Dropout to Fight Overfitting
• Stacking Recurrent Layers
• Using Bidirectional RNNs
• Understanding 1D Convolution for Sequence Data
• Combing CNNs and RNNs to Process Long Sequences

• Introduction to Computer Vision in Applied Artificial Intelligence
• What is Convnets in Computer Vision
• Understanding Convolution Operation and Max Pooling Operation
• Training a Convnet on a Small Dataset
• Understanding the Relevance of Deep Learning for Small-Data Problems
• Downloading the Data and Building the Network
• Data Pre-processing and Data Augmentation
• Visualizing Intermediate Activations
• Visualizing Convnet Filters
• Visualizing Convnet Filters

Be Recognized as a CASUGOL Certified Professional

Add Value to your already prolific portfolio with a Certificate of Competency from CASUGOL.

Awarded upon completing and meeting all requirements of the course.

Host or Publisher CASUGOL