English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 50m | 264 MB
Machine learning is the most exciting branch of artificial intelligence. It allows systems to learn from data by identifying patterns and making decisions with little to no human intervention. In this course, you’ll navigate the machine learning lifecycle by getting hands-on practice training your first machine learning model. Join instructor Kesha Williams as she explores widely adopted machine learning methods: supervised, unsupervised, and reinforcement. There’s a focus on sourcing and preparing data and selecting the best learning algorithm for your project. After training a model, learn to evaluate model performance using standard metrics. Finally, Kesha shows you how to streamline the process by building a machine learning pipeline. If you’re looking to understand the machine learning lifecycle and the steps required to build systems, check out this course.
Table of Contents
Introduction
1 Introduction to AI foundations Machine learning course
2 Reviewing the course scenarios
Understanding Machine Learning
3 Exploring machine learning
4 Examining how machines learn
Implementing a Machine Learning Solution
5 Breaking down the machine learning lifecycle
6 Framing machine learning problems
7 Identifying a pre-built model
8 Understanding tools used to train a model
Preparing Data for Machine Learning
9 Obtaining data
10 Visualizing and understanding data
11 Understanding feature engineering
12 Demo Performing feature engineering
Training a Machine Learning Model
13 Understanding learning algorithms and model training
14 Exploring learning algorithms for classification
15 Reviewing learning algorithms for regression
16 Examining additional learning algorithms
17 Training a custom machine learning model
18 Demo Training a custom machine learning model
Evaluating Model Performance
19 Exploring common classification metrics
20 Understanding the confusion matrix
21 Exploring common regression metrics
22 Determining feature importance
23 Combating bias
Operationalizing a Machine Learning Pipeline
24 Structuring a machine learning pipeline
25 Demo Designing and building a pipeline
Conclusion
26 Your machine learning journey
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