Supervised Learning for AI with Python and Tensorflow 2

• The basics of supervised learning: What are parameters, What is a bias node, Why do we use a learning rate
• Techniques for dealing with data: How to Split Datasets, One-hot Encoding, Handling Missing Values
• Vectors, matrices and creating faster code using Vectorization
• Mathematical concepts such as Optimization, Derivatives and Gradient Descent
• Gain a deep understanding behind the fundamentals of Feedforward, Convolutional and Recurrent Neural Networks
• Build Feedforward, Convolutional and Recurrent Neural Networks using only the fundamentals
• How to use Tensorflow 2.0 and Keras to build models, create TFRecords and save and load models
• Practical project: Style Transfer – Use AI to draw an image in the style of your favorite artist
• Practical project: Object Detection – Use AI to Detect the bounding box locations of objects inside of images
• Practical project: Transfer Learning – Learn to leverage large pretrained AI models to work on new datasets
• Practical project: One-Shot Learning – Learn to build AI models to perform tasks such as Face recognition
• Practical project: Text Generation – Build an AI model to generate text similar to Romeo and Juliet
• Practical project: Sentiment Classification – Build an AI model to determine whether text is overall negative or positive
• Practical project: Attention Model – Build an attention model to build an interpretable AI model

Who this course is for:

• Beginner Python programmers curious about Artificial Intelligence
• People looking for an AI course that teaches both the theoretical and practical aspects of Artificial Intelligence