• 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
• Introduction to List (Array) and Dictionary Comprehension in Python
• Packages / Libraries in Python for Artificial Intelligence – NumPy, Pandas, SciPy, Scikit-Learn, MatPlotLib

• 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

• 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
• 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

• 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

Certificate Verification

All certificates issued by CASUGOL are to individuals who have successfully completed CASUGOL Certification Programs / Executive Workshops and have fulfilled all requirements by demonstrating proficiency in applying the knowledge and skills acquired.

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