Practical Deep Learning with Keras and Python

This course is for you if you are new to Machine Learning but want to learn it without all the math. This course is also for you if you have had a machine learning course but could never figure out ho...

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Course Description

This course is for you if you are new to Machine Learning but want to learn it without all the math. This course is also for you if you have had a machine learning course but could never figure out how to use it to solve your own problems. In this course, we will start from the very scratch. This is a very applied course, so we will immediately start coding even without installation! You will s...

This course is for you if you are new to Machine Learning but want to learn it without all the math. This course is also for you if you have had a machine learning course but could never figure out how to use it to solve your own problems. In this course, we will start from the very scratch. This is a very applied course, so we will immediately start coding even without installation! You will see a brief bit of absolutely essential theory and then we will get into the environment setup and explain almost all concepts through code. You will be using Keras - one of the easiest and most powerful machine learning tools out there. All with only a few lines of code. All the examples used in the course comes with starter code which will get you started and remove the grunt effort. The course also includes finished codes for the examples run in the videos so that you can see the end product should you ever get stuck. There is also a real-time chat system in place for students who enroll in this course. With a free signup, you get access to real-time chat with myself and fellow students who are working to complete this course (or have completed the course before you). We plan on creating this network of like-minded machine learning experts who can help each other out and collaborate on exciting ideas together.

What you’ll learn
  • Convolutional Neural Networks
  • Residual Connections
  • Inception Module

Covering Topics

1
Section 1 : Introduction

2
Section 2 : A Bit of Theory

3
Section 3 : Installation and Setup

4
Section 4 : Say Hi to Keras

5
Section 5 : Real World Case Study: Predicting Protein Functions

6
Section 6 : Convolutional Neural Networks (CNN)

7
Section 7 : Graph-based Models

8
Section 8 : Finishing Touches

Curriculum

      Section 1 : Introduction
    1
    Dive into Machine Learning Preview
    2
    Making Predictions
      Section 2 : A Bit of Theory
    3
    Machine Learning Pipeline
    4
    Regression
    5
    Binary and Multi-class Classification Preview
    6
    Recap and a Link to More Theory
      Section 3 : Installation and Setup
    7
    Environment setup for Windows (and some issues with it)
    8
    Environment setup for Mac and Linux
      Section 4 : Say Hi to Keras
    9
    Data Preparation Preview
    10
    Training and Testing
      Section 5 : Real World Case Study: Predicting Protein Functions
    11
    Problem Description and Data View
    12
    Pre-processing the Data
    13
    Loading Data and Getting the Shapes Right
    14
    Train, Test Split Preview
    15
    Shapes in Depth (or how not to have headaches for days)
    16
    Sequential Model
    17
    Functional API
      Section 6 : Convolutional Neural Networks (CNN)
    18
    Basics and Rationale
    19
    CNN in Keras (or why Keras is better than your ML tool)
    20
    Pooling (and why it's not that important)
    21
    Dropout (and why you should always consider it)
      Section 7 : Graph-based Models
    22
    Functional API for CNN
    23
    Inception Module
    24
    Residual Connections
      Section 8 : Finishing Touches
    25
    Saving and Loading Model Weights
    26
    Parting Words

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