Applied Machine Learning: Foundations

Applied Machine Learning: Foundations
Applied Machine Learning: Foundations
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 38m | 381 MB

Anyone who can write basic Python is capable of fitting a simple machine learning model on a clean dataset. The competitive edge comes in the ability to customize and optimize those models for specific problems. The workflow used to build effective machine learning models and the methods used to optimize those models are typically not algorithm or problem specific. In this course, the first installment in the two-part Applied Machine Learning series, instructor Derek Jedamski digs into the foundations of machine learning, from exploratory data analysis to evaluating a model to ensure it generalizes to unseen examples. Instead of zeroing in on any specific machine learning algorithm, Derek focuses on giving you the tools to efficiently solve nearly any kind of machine learning problem.

Topics include:

  • What is machine learning (ML)?
  • ML vs. deep learning vs. AI
  • Handling common challenges in ML
  • Plotting continuous features
  • Continuous and categorical data cleaning
  • Measuring success
  • Overfitting and underfitting
  • Tuning hyperparameters
  • Evaluating a model
Table of Contents

Introduction
1 Leveraging machine learning
2 What you should know
3 What tools you need
4 Using the exercise files

Machine Learning Basics
5 What is machine learning
6 What kind of problems can this help you solve
7 Why Python
8 Machine learning vs. Deep learning vs. Artificial intelligence
9 Demos of machine learning in real life
10 Common challenges

Exploratory Data Analysis and Data Cleaning
11 Why do we need to explore and clean our data
12 Exploring continuous features
13 Plotting continuous features
14 Continuous data cleaning
15 Exploring categorical features
16 Plotting categorical features
17 Categorical data cleaning

Measuring Success
18 Why do we split up our data
19 Split data for train validation test set
20 What is cross-validation
21 Establish an evaluation framework

Optimizing a Model
22 Bias Variance tradeoff
23 What is underfitting
24 What is overfitting
25 Finding the optimal tradeoff
26 Hyperparameter tuning
27 Regularization

End-to-End Pipeline
28 Overview of the process
29 Clean continuous features
30 Clean categorical features
31 Split data into train validation test set
32 Fit a basic model using cross-validation
33 Tune hyperparameters
34 Evaluate results on validation set
35 Final model selection and evaluation on test set

Conclusion
36 Next steps