Applied Machine Learning: Foundations

Intermediate 0(0 Ratings) 0 Students enrolled
Created by Skill Central Last updated Sun, 28-Feb-2021 English
What will i learn?
  • 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

Curriculum for this course
33 Lessons 02:34:16 Hours
Introduction
3 Lessons 00:04:27 Hours
  • Leveraging Machine Learning 00:01:57
  • What You Should Know 00:01:06
  • Using The Exercise Files 00:01:24
Machine Learning Basics
6 Lessons 00:27:44 Hours
  • What is machine learning? 00:04:01
  • What Kind Of Problems Can This Help You Solve? 00:05:02
  • Why Python? 00:05:49
  • Machine Learning Vs. Deep Learning Vs. Artificial Intelligence 00:03:49
  • Demos Of Machine Learning In Real Life 00:02:59
  • Common Challenge 00:06:04
Exploration Data Analysis and Data Cleaning
7 Lessons 00:42:31 Hours
  • Why do we need to explore and clean our data? 00:03:29
  • Exploring Continuous Features 00:08:46
  • Plotting Continuous Features 00:07:35
  • Continuous Data Cleaning 00:05:44
  • Exploring Categorical Features 00:06:04
  • Plotting Categorical Features 00:06:20
  • Categorical data cleaning 00:04:33
Measuring Success
4 Lessons 00:21:37 Hours
  • Why do we split up our data? 00:05:54
  • Split data for train/validation/test set 00:05:07
  • What is cross-validation? 00:06:03
  • Establish an evaluation framework 00:04:33
Optimizing a Model
5 Lessons 00:19:51 Hours
  • Bias/Variance Tradeoff 00:05:00
  • What Is Underfitting? 00:02:26
  • What Is Overfitting? 00:02:47
  • Finding The Optimal Tradeoff 00:03:16
  • Hyperparameter Tuning 00:06:22
End–to-End Pipeline
8 Lessons 00:38:06 Hours
  • Overview Of The Process 00:01:48
  • Clean Continuous Features 00:05:04
  • Clean Categorical Features 00:04:18
  • Split Data Into Train/Validation/Test Set 00:03:48
  • Fit A Basic Model Using Cross-Validation 00:05:21
  • Tune Hyperparameters 00:06:34
  • Evaluate Results On Validation Set 00:06:43
  • Final Model Selection And Evaluation On Test Set 00:04:30
Conclusion
0 Lessons 00:00:00 Hours
Requirements
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Description

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.

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Includes:
  • 02:34:16 Hours On demand videos
  • 33 Lessons
  • Access on mobile and tv
  • Full lifetime access
  • Certificate of completion
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