International Acclaimed Certification. 5-Star Reviews
Suitable for everyone. Learn in an Interactive, Supportive, and Encouraging Environment.
Duration: 3 Day (Onsite) / 24 Hours (Online via Zoom)
Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
Who Should Attend: IT, Data, Data Managers, Data Analytics, Statistic, software developers, and anyone seeking to acquire advanced knowledge on Applied Artificial Intelligence.
- Acquire advanced knowledge and skills in artificial intelligence and its application.
- Learn how to use Python Programming language for Machine Learning, Deep Learning, Natural Language Processing (NLP), and Basic Computer Vision.
No pre-requisite. Certified Applied AI Professional (CAAI) is suitable for anyone who is interested in Applied Artificial Intelligence and does not have any prior technological experience
Participants are required to attempt an examination upon completion of the course. This exam tests a candidate’s knowledge and skills related to Applied Artificial Intelligence based on the syllabus covered
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
Module 2 - Deep Dive into Python Programming for Applied 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
- Introduction to List (Array) and Dictionary Comprehension in Python
- Packages / Libraries in Python for Artificial Intelligence – NumPy, Pandas, SciPy, Scikit-Learn, MatPlotLib
Module 3 - Data Pre-processing and Cleaning for Applied Artificial Intelligence
- Understanding the Different Types of Data
- Reading and Writing of Data from Various Sources
- Data Preparation for Pre-processing and Cleaning
- Techniques to Data Manipulation using Python Tools
- Data Formatting, Normalization and Data Encoding
- Cleaning Techniques to Remove Extraneous Information
Module 4 - Machine Learning Regression, Classification and Clustering Techniques
- 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
Module 5 - Deep Learning Techniques in Applied Artificial Intelligence
- 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)
Module 6 - Natural Language Processing (NLP) in Applied Artificial Intelligence
- What is Natural Language Processing (NLP)
- Text Pre-processing for Natural Language Processing (NLP)
- Understanding Recurrent Neural Networks
- What is a First Recurrent Baseline?
- 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
Module 7 - Computer Vision (CV) in Applied Artificial Intelligence
- 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 Heatmaps of Class Activation
Certified Applied AI Professional (CAAI) involves extensive practical / hands-on exercises, rigorous usage of real-time case studies, role-playing and group discussion
|09:30 AM — 05:30 PM
|No. of Days:
“The duty of helping one’s self in the highest sense involves helping of one’s neighbours” – Samuel Smiles
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