Machine Learning in Python

Introduction:

Machine learning is a widely used statistical analysis technique across many industries. This training gives participants a foundational intuition of neural networks. You will also put your theory to practice with a programming example.

Participants should be familiar with basic linear algebra.

Course Objectives:

  • Understand how machine learning is used for prediction
  • Grasp the basics of optimization
  • Understand how optimization is represented in neural networks
  • Apply knowledge to a Python program

Duration

7 hours, 1 Day Course

Mode of Delivery

Classroom-based, Instructor-led Training

Course Outlines

  1. Overview
    1. What is Machine Learning?
    2. Applications of Machine Learning in Industry
  2. Foundational Knowledge
    1. Vectors and Matrices
    2. Cost Functions
    3. Regression
    4. Multiclass Logistic Regression as Iterated Binary Logistic Regression
    5. Gradient Descent as Iterated Linear Regression
  3. The Analogy to Neurons
    1. Inputs: dendrites
    2. Outputs: axons
  4. Parts of a Neural Network
    1. High-level Representation of a Network
    2. Input Vector
    3. Backpropagation
    4. Activation Functions
    5. Hidden Layers
    6. Bias-Variance Trade-off
  5. Machine Learning in Python
    1. Tensorflow Constructs
    2. MNIST