100% Discount || Supervised Learning for AI with Python and Tensorflow 2

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    Supervised Learning for AI with Python and Tensorflow 2


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


    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

    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

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