Artificial Intelligence and Deep Learning with Python

U1: Fundamentals of AI and Deep Learning

→ Fundamentals of Machine Learning and Deep Learning
→ Setting up Python and libraries for Deep Learning
→ Introduction to supervised learning
→ Understanding overfitting and underfitting in supervised learning
→ Metrics for evaluating Classification models
→ Metrics for evaluating regression models
→ Introduction to unsupervised learning

U2: Creation of Artificial Neural Networks (ANN)

→ Basic concepts of neurons and perceptrons
→ ​Fundamentals of neural networks
→ Different activation functions
→ Activation functions for multiclass models
→ Cost Functions and Gradient Descent
→ How backpropagation works
→ Strategies for designing effective neural networks
→ Advantages of using Tensorflow and Keras
→ Practical case of regression with Keras
→ How to import libraries and fonts in Keras
→ Data analysis and preprocessing in Keras (I)
→ Data analysis and preprocessing in Keras (II)
→ Data splitting in Train and Test with Keras
→ Data scaling in Keras
→ Creating Regression models in Keras
→ Training Regression models in Keras
→ Evaluation and Prediction in Regression with Keras
→ Practical case of Binary Classification with Keras
→ Importing libraries and fonts for Binary Classification
→ Data analysis and preprocessing for
→ Binary classification
→ Data splitting into Train and Test for Binary Classification
→ Data Scaling for Binary Classification
→ Creation of binary classification models
→ Training of Binary Classification models
→ Evaluation and Prediction in Binary Classification with Keras
→ Practical case of multiclass classification with Keras
→ Importing libraries and fonts for multiclass classification
→ Data analysis and preprocessing for multiclass classification (I)
→ Data analysis and preprocessing for multiclass classification (II)
→ Data splitting into Train and Test for Multiclass Classification
→ Data Scaling for Multiclass Classification
→ Creation of multiclass classification models
→ Training of multiclass classification models
→ Evaluation and Prediction in Multiclass Classification with Keras
→ Model monitoring with Tensorboard

U3: Creation of Convolutional Neural Networks (CNN)

→ Basics of Convolutional Neural Networks (CNN)
→ Image filters and kernels in CNN
→ Convolutional layers in neural networks
→ Pooling layers in neural networks
→ Black and White Image Classification Case Study
→ Importing libraries and fonts for Black and White Image Classification
→ Black and White Image Preprocessing
→ Creating models for Black and White Image Classification
→ Training models for Black and White Image Classification
→ Evaluation and Prediction in Black and White Image Classification
→ Practical case of RGB image classification
→ Importing libraries and fonts for RGB image classification
→ RGB image preprocessing
→ Creating models for RGB image classification
→ Training models for RGB image classification
→ Evaluation and Prediction in RGB Image Classification

U4: Creation of Recurrent Neural Networks (RNN)

→ Introduction to Recurrent Neural Networks (RNN)
→ LSTM neural networks
→ Creation of batches in RNN
→ Forecasting with RNN practical case
→ Importing libraries and sources for Forecasting with RNN
→ Data preprocessing for Forecasting with RNN
→ Data splitting into Train and Test for Forecasting with RNN
→ Data scaling for Forecasting with RNN
→ ​Creating time series generators for Forecasting with RNN
→ Development of Forecasting models with RNN
→ ​Training Forecasting models with RNN
→ Evaluation and Prediction in Forecasting with RNN

U5: Creation of Neural Networks in Unsupervised Learning

→ Basic concepts of unsupervised neural networks
→ Introduction to autoencoders in neural networks
→ Practical case of unsupervised neural networks
→ Importing libraries and sources for unsupervised neural networks
→ Data preprocessing for unsupervised neural networks
→ Scaling in unsupervised neural networks
→ Estimating the number of clusters for unsupervised neural networks
→ Creating the model for unsupervised neural networks
→ Model training for unsupervised neural networks
→ Evaluation and prediction for unsupervised neural networks

U6: Conclusions

→ Conclusions

Course evaluation

→ This course contains a final exam

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