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Supervised Learning for AI with Python and Tensorflow 2

Uncover the Concepts and Techniques to Build and Train your own Artificial Intelligence Models
(21 reviews)
3,424 students
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Platform: Udemy
Video: 21h 12m
Language: English
Next start: On Demand

Table of contents


Gain a deep understanding of Supervised Learning techniques by studying the fundamentals and implementing them in NumPy.
Gain hands-on experience using popular Deep Learning frameworks such as Tensorflow 2 and Keras.

Section 1 – The Basics:
– Learn what Supervised Learning is, in the context of AI
– Learn the difference between Parametric and non-Parametric models
– Learn the fundamentals: Weights and biases, threshold functions and learning rates
– An introduction to the Vectorization technique to help speed up our self implemented code
– Learn to process real data: Feature Scaling, Splitting Data, One-hot Encoding and Handling missing data
– Classification vs Regression

Section 2 – Feedforward Networks:
– Learn about the Gradient Descent optimization algorithm.
– Implement the Logistic Regression model using NumPy
– Implement a Feedforward Network using NumPy
– Learn the difference between Multi-task and Multi-class Classification
– Understand the Vanishing Gradient Problem
– Overfitting
– Batching and various Optimizers (Momentum, RMSprop, Adam)

Section 3 – Convolutional Neural Networks:
– Fundamentals such as filters, padding, strides and reshaping
– Implement a Convolutional Neural Network using NumPy
– Introduction to Tensorfow 2 and Keras
– Data Augmentation to reduce overfitting
– Understand and implement Transfer Learning to require less data
– Analyse Object Classification models using Occlusion Sensitivity
– Generate Art using Style Transfer
– One-Shot Learning for Face Verification and Face Recognition
– Perform Object Detection for Blood Stream images

Section 4 – Sequential Data
– Understand Sequential Data and when data should be modeled as Sequential Data
– Implement a Recurrent Neural Network using NumPy
– Implement LSTM and GRUs in Tensorflow 2/Keras
– Sentiment Classification from the basics to the more advanced techniques
– Understand Word Embeddings
– Generate text similar to Romeo and Juliet
– Implement an Attention Model using Tensorflow 2/Keras

You will learn

✓ 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


• Secondary Level (High School) Mathematics
• Some basic programming experience in Python

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
Data Scientist and Software Engineer
Jeremy Richard Lai Hong was born and raised in South Africa and apologises in advance for his accent.

He has experience as both a Software Engineer and as a Data Scientist. He has practical working experience, having worked at a bank and for a brand management company.

He’s worked on many projects including Optical Character Recognition, Topic Modelling, ATM cash forecasting, Object Tracking and Sentiment Analysis.

Platform: Udemy
Video: 21h 12m
Language: English
Next start: On Demand

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