Applied artificial intelligence (AI) is a branch of computer science that focuses on creating practical solutions using machine learning and other AI techniques. It involves developing and implementing algorithms and models that can learn from and make predictions based on data.

• 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

Python is used extensively in various AI applications such as natural language processing, computer vision, and predictive analytics. Its popularity among data scientists and AI developers has led to the creation of many useful libraries and tools, making Python an essential language for applied AI development.

• 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

Data pre-processing and cleaning are essential steps in the development of applied AI models. It involves preparing and organizing raw data to ensure that it is suitable for analysis and use in machine learning algorithms.

• 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

Regression, classification, and clustering are the three main techniques used in ML. Regression is a method for predicting continuous numerical output based on input variables, while classification is used for predicting categorical output based on input variables.

• 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

Deep learning techniques are widely used in applied AI because they can achieve state-of-the-art performance in tasks such as image and speech recognition, natural language processing, and recommendation systems. Examples of deep learning architectures include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs).

• 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)

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. NLP is widely used in applied AI, particularly in fields such as virtual assistants, chatbots, and sentiment analysis.

• 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

CV is used in applied AI to solve problems such as object recognition, image segmentation, and facial recognition. CV involves several techniques, including image preprocessing, feature extraction, and deep learning-based approaches such as Convolutional Neural Networks (CNNs).

• 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