Machine Learning with Hugging Face Bootcamp: Zero to Mastery

18h 27m 9s
English
Paid

Learn to apply machine learning using the Hugging Face ecosystem — from scratch to professional level!

This practice-oriented course will guide you through the entire journey — from training models to deploying them. We will start with the basics, gradually proceed to real machine learning engineering skills, and most importantly, enjoy the process!

Why is this Machine Learning course with Hugging Face so awesome?

Because it is the most comprehensive and engaging online course, where you will master modern machine learning tools through real projects. There is minimal theory and maximum practice — you will not only understand how everything works but also be able to apply the acquired knowledge to real-world tasks.

Hugging Face is a sort of "homepage" for artificial intelligence: here companies like OpenAI, Google, and Apple share their open models, and engineers and researchers create their own projects and portfolios.

What you will learn:

  • Hugging Face Transformers — a powerful library for working with machine learning models in text, images, audio, video, and multimodal tasks.
  • Hugging Face Datasets — a convenient tool for accessing datasets in NLP, Computer Vision, and Audio.
  • Hugging Face Hub — an online platform for collaboration, model sharing, and project publication.
  • And much more!

The entire training is based on real projects — you will write code, train, and fine-tune real ML models: from text classification and object detection to working with large language and multimodal models.

Ready to dive into the world of machine learning with Hugging Face and become an AI master? Then welcome to the course!

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#1: Machine Learning with Hugging Face Bootcamp: Zero to Mastery
All Course Lessons (106)
#Lesson TitleDurationAccess
1
Machine Learning with Hugging Face Bootcamp: Zero to Mastery Demo
01:49
2
Overview
05:03
3
Introduction to Text Classification
05:44
4
What We're Going To Build!
07:22
5
Getting Setup: Adding Hugging Face Tokens to Google Colab
05:53
6
Getting Setup: Importing Necessary Libraries to Google Colab
09:36
7
Downloading a Text Classification Dataset from Hugging Face Datasets
16:01
8
Preparing Text Data for Use with a Model - Part 1: Turning Our Labels into Numbers
12:49
9
Preparing Text Data for Use with a Model - Part 2: Creating Train and Test Sets
06:19
10
Preparing Text Data for Use with a Model - Part 3: Getting a Tokenizer
12:54
11
Preparing Text Data for Use with a Model - Part 4: Exploring Our Tokenizer
10:27
12
Preparing Text Data for Use with a Model - Part 5: Creating a Function to Tokenize Our Data
17:58
13
Setting Up an Evaluation Metric (to measure how well our model performs)
08:54
14
Introduction to Transfer Learning (a powerful technique to get good results quickly)
07:11
15
Model Training - Part 1: Setting Up a Pretrained Model from the Hugging Face Hub
12:20
16
Model Training - Part 2: Counting the Parameters in Our Model
12:27
17
Model Training - Part 3: Creating a Folder to Save Our Model
03:54
18
Model Training - Part 4: Setting Up Our Training Arguments with TrainingArguments
15:00
19
Model Training - Part 5: Setting Up an Instance of Trainer with Hugging Face Transformers
05:06
20
Model Training - Part 6: Training Our Model and Fixing Errors Along the Way
13:35
21
Model Training - Part 7: Inspecting Our Models Loss Curves
14:40
22
Model Training - Part 8: Uploading Our Model to the Hugging Face Hub
08:02
23
Making Predictions on the Test Data with Our Trained Model
05:59
24
Turning Our Predictions into Prediction Probabilities with PyTorch
12:49
25
Sorting Our Model's Predictions by Their Probability
05:11
26
Performing Inference - Part 1: Discussing Our Options
09:41
27
Performing Inference - Part 2: Using a Transformers Pipeline (one sample at a time)
10:02
28
Performing Inference - Part 3: Using a Transformers Pipeline on Multiple Samples at a Time (Batching)
06:39
29
Performing Inference - Part 4: Running Speed Tests to Compare One at a Time vs. Batched Predictions
10:34
30
Performing Inference - Part 5: Performing Inference with PyTorch
12:07
31
OPTIONAL - Putting It All Together: from Data Loading, to Model Training, to making Predictions on Custom Data
34:29
32
Turning Our Model into a Demo - Part 1: Gradio Overview
03:48
33
Turning Our Model into a Demo - Part 2: Building a Function to Map Inputs to Outputs
07:08
34
Turning Our Model into a Demo - Part 3: Getting Our Gradio Demo Running Locally
06:47
35
Making Our Demo Publicly Accessible - Part 1: Introduction to Hugging Face Spaces and Creating a Demos Directory
08:02
36
Making Our Demo Publicly Accessible - Part 2: Creating an App File
12:15
37
Making Our Demo Publicly Accessible - Part 3: Creating a README File
07:08
38
Making Our Demo Publicly Accessible - Part 4: Making a Requirements File
03:34
39
Making Our Demo Publicly Accessible - Part 5: Uploading Our Demo to Hugging Face Spaces and Making it Publicly Available
18:44
40
Summary Exercises and Extensions
05:56
41
Introduction
10:04
42
Setting Up Google Colab with Hugging Face Tokens
05:52
43
Installing Necessary Dependencies
03:44
44
Getting an Object Detection Dataset
07:38
45
Inspecting the Features of Our Dataset
06:24
46
Creating a Colour Palette to Visualize Our Classes
09:36
47
Creating a Helper Function to Halve Our Image Sizes
04:25
48
Creating a Helper Function to Halve Our Box Sizes
06:02
49
Testing our Helper Functions
04:33
50
Outlining the Steps to Draw Boxes on an Image
06:27
51
Plotting Bounding Boxes on a Single Image Step by Step
19:05
52
Different Bounding Box Formats
08:18
53
Getting an Object Detection Model
06:16
54
Transfer Learning Overview
06:09
55
Downloading our Model from the Hugging Face Hub and Trying it Out
09:27
56
Inspecting the Layers of Our Model
06:54
57
Counting the Number of Parameters in Our Model
10:55
58
Creating a Function to Build Our Custom Model
13:16
59
Passing a Single Image Sample Through Our Model - Part 1
15:47
60
OPTIONAL: Data Preprocessor Model Workflow
08:46
61
Loading Our Models Image Preprocessor and Customizing it for Our Use Case
20:11
62
Exercise: Imposter Syndrome
02:57
63
Discussing the Format Our Model Expects Our Annotations In (COCO)
06:18
64
Creating Dataclasses to Hold the COCO Format
09:55
65
Creating a Function to Turn Our Annotations into COCO Format
12:06
66
Preprocessing a Single Image Sample and COCO Formatted Annotations
07:27
67
Post Processing a Single Output
12:03
68
Plotting a Single Post Processed Sample onto an Image
12:45
69
OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 1: Overview
10:45
70
OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 2: Replicating Scores by Hand
28:33
71
OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 3: Replicating Labels by Hand
12:33
72
OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 4: Replicating Boxes by Hand Overview
10:24
73
OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 5: Replicating Boxes by Hand Implementation
17:41
74
OPTIONAL: Reproducing Our Models Post Processed Outputs by Hand - Part 6: Plotting Our Manual Post Processed Outputs on an Image
06:44
75
Preparing Our Data at Scale - Part 1: Concept Overview
09:22
76
Preparing Our Data at Scale - Part 2: Creating Train Validation and Test Splits
12:14
77
Preparing Our Data at Scale - Part 3: Preprocessing Multiple Samples at a Time Overview
08:17
78
Preparing our Data at Scale - Part 4: Making a Function to Preprocess Multiple Samples at a Time
21:38
79
Preparing our Data at Scale - Part 5: Applying Our Preprocessing Function to Our Datasets
09:38
80
Preparing Our Data at Scale - Part 6: Creating a Data Collation Function
12:20
81
Training a Custom Model - Part 1: Overview
07:43
82
Training a Custom Model - Part 2: Creating a Model and Folder to Save Our Model to
04:12
83
Training a Custom Model - Part 3: Creating TrainingArguments for Our Model Overview
12:54
84
Training a Custom Model - Part 4: Creating our First TrainingArguments
11:12
85
Training a Custom Model - Part 5: Finishing Off the TrainingArguments
12:40
86
Training a Custom Model - Part 6: OPTIONAL - Creating a Custom Optimizer for Different Learning Rates
16:06
87
Training a Custom Model - Part 7: Creating an Evaluation Function for Our Model Overview
13:09
88
Training a Custom Model - Part 8: Creating an Evaluation Function for Our Model Targets Processing
22:50
89
Training a Custom Model - Part 9: Creating an Evaluation Function for Our Model Predictions Processing
13:53
90
Training a Custom Model - Part 10: Training Our Model with Trainer
12:54
91
Training a Custom Model - Part 11: Plotting Our Models Loss Curves
08:36
92
Evaluating Our Model on the Test Dataset
11:14
93
Making Predictions on Test Data and Visualizing Them
24:21
94
Plotting Our Models Predictions vs. the Ground Truth Images
12:01
95
Trying Our Model on Images from the Wild
09:50
96
Uploading Our Trained Model to the Hugging Face Hub
10:47
97
Turning Our Model into a Demo - Part 1: Gradio and Hugging Face Spaces Overview
10:11
98
Turning Our Model into a Demo - Part 2: Creating an App File Overview
07:10
99
Turning Our Model into a Demo - Part 3: Building the Main Function of Our App File
27:33
100
Turning Our Model into a Demo - Part 4: Finishing Off Our App File and Testing Our Demo
09:57
101
Turning Our Model into a Demo - Part 5: Creating a Readme and Requirements File
03:32
102
Turning Our Model into a Demo - Part 6: Getting Example Images for Our Demo
08:20
103
Turning Our Model into a Demo - Part 7: Uploading Our Demo to the Hugging Face Hub
17:19
104
Turning Our Model into a Demo - Part 8: Embedding Our Demo into Our Notebook
03:45
105
Summary, Extensions and Extra-Curriculum
06:16
106
Thank You!
01:18
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