## Top 3 courses (Free, no registration)
Whether you're a student or a working professional, this guide will help you find the perfect course to jumpstart your career in Machine Learning. From the fundamentals to advanced techniques, these courses cover everything you need to know to succeed in this exciting field. So don't wait any longer, start your journey today and become an expert in Machine Learning!

1. "Zero to Hero" by Laurence Moroney
2. "NLP" by Laurence Moroney
3. "Neural Networks" and DL by Geoffrey Hinton 

## 1. "Zero to Hero" by Laurence Moroney
"Machine Learning Zero to Hero" by Laurence Moroney is a course designed for developers who are new to machine learning and want to become experts in the field. 
It designed for individuals with little to no prior experience in Python programming.

In this course, Laurence teaches you about machine learning and computer vision in a quick and easy way. He covers topics that might take a whole month to understand, but he explains them in just 5 minutes. You will learn how machine learning programming is different from traditional programming. Also, you will learn how to make a basic neural network and build an image classifier using TensorFlow.<br>

![TensorFlow](/images/articles/en/technology/best-ml-courses-for-beginners/1-1.jpg)

**Part 1**
<br>
Traditional programming is the traditional way of writing code to perform specific tasks. It involves writing code with a set of rules and instructions that the computer follows to complete a task.

Machine learning, on the other hand, is a type of artificial intelligence that allows the computer to learn from data and make predictions or decisions without being explicitly programmed to do so. Instead of writing code for each individual task, the computer is fed with data and uses algorithms to identify patterns and make decisions.

The fit method is a method used in machine learning to train a model on a given dataset. The method takes in the data, fits the model to the data, and returns a model that is trained and can be used to make predictions. The fit method helps the model learn the underlying relationships in the data and improves its accuracy in making predictions.
<br>

<a href="https://www.youtube.com/watch?v=KNAWp2S3w94" target="_blank" rel="noopener">Part 1 - Intro to Machine Learning</a><br>
<hr>

**Part 2**
<br>
Data is the information used by machine learning algorithms to learn patterns and make predictions. 

The Fashion MNIST dataset is a collection of 70,000 28x28 grayscale images of clothing items, such as shirts, pants, shoes, and accessories. 
The dataset is commonly used for training and testing machine learning models in the field of computer vision.

The keras library provides a convenient way to load the MNIST data and use it in our models.

The neural network design refers to the architecture of the network, including the number of layers, the type of layers, and the number of neurons in each layer. 
<br>

<a href="https://www.youtube.com/watch?v=bemDFpNooA8" target="_blank" rel="noopener">Part 2 - Basic Computer Vision with ML</a><br>

<hr>

**Part 3**
<br>
Convolutional Neural Networks (CNNs) are a type of deep learning neural network widely used for computer vision tasks such as image classification, object detection, and image segmentation. 

They are called "convolutional" because they perform mathematical operations called "convolutions" on the input data (images), which process the data and extract features from it. These features are then fed into multiple layers of the network, where they are processed and combined to make a final prediction.

<a href="https://www.youtube.com/watch?v=x_VrgWTKkiM" target="_blank" rel="noopener">Part 3 - Introducing convolutional neural networks</a><br>

<hr>

**Part 4**
<br>
An overview of building a simple image classifier with TensorFlow:

* Prepare the data: Get a dataset of labeled images and split it into training, validation, and test sets.
* Preprocess the data: Normalize the pixel values and possibly resize the images to a uniform size.
* Compile the model: Choose a loss function, optimizer, and metrics to compile the model.
* Train the model: Train the model on the training data. 
* Evaluate the model: After training, evaluate the model on the test data to measure its performance.
* Make predictions: Use the trained model to make predictions on new, unseen images.

<a href="https://www.youtube.com/watch?v=u2TjZzNuly8" target="_blank" rel="noopener">Part 4 - Build an image classifier</a><br>
<br>


## 2. "NLP" by Laurence Moroney

![NLP](/images/articles/en/technology/best-ml-courses-for-beginners/2-2.jpg)

**Natural Language Processing with Laurence Moroney**<br>

This course will cover the process of converting text into a numerical representation, known as text vectorization, and using this representation to perform sentiment analysis. 

Sentiment analysis is the task of determining the sentiment or emotion expressed in a piece of text, such as positive, negative, or neutral.

<a href="https://www.youtube.com/watch?v=fNxaJsNG3-s&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S" target="_blank" rel="noopener">Natural Language Processing Course - Playlist</a><br>

<hr>
You will learn how to represent words in a way that a computer can process them, with the goal of training a neural network to understand their meaning later on.

<a href="https://www.youtube.com/watch?v=fNxaJsNG3-s&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S&index=1" target="_blank" rel="noopener">Part 1 - Natural Language Processing - Tokenization</a><br>

<hr>

You will convert sentences into sequences of numbers and use preprocessing techniques to prepare them for training a neural network.

<a href="https://www.youtube.com/watch?v=r9QjkdSJZ2g&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S&index=2" target="_blank" rel="noopener">Part 2 - Sequencing - Turning sentences into data</a><br>

<hr>

Training a model using a tokenizer involves converting text data into numerical representations that can be used as input for a machine learning model. 

A tokenizer is a tool that is used to preprocess text data by dividing it into smaller parts, called tokens, which can then be converted into numerical values.

<a href="https://www.youtube.com/watch?v=Y_hzMnRXjhI&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S&index=3" target="_blank" rel="noopener">Part 3 - Training a model to recognize sentiment in text</a><br>

<hr>

Recurrent Neural Networks (RNNs) are a type of deep learning neural network that are specifically designed to process sequential data, such as time series data, text, or speech. 

<a href="https://www.youtube.com/watch?v=OuYtk9Ymut4&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S&index=4" target="_blank" rel="noopener">Part 4 - ML with Recurrent Neural Networks</a><br>

<hr>

Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network that is specifically designed to handle the problem of vanishing gradients in traditional RNNs and is widely used in the field of Natural Language Processing (NLP).

<a href="https://www.youtube.com/watch?v=A9QVYOBjZdY&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S&index=5" target="_blank" rel="noopener">Part 5 - Long Short-Term Memory for NLP</a><br>

<hr>

Training an AI to create poetry involves using machine learning techniques, such as deep learning with recurrent neural networks, to train a model on a dataset of poems, with the goal of generating new, original poetry.

<a href="https://www.youtube.com/watch?v=ZMudJXhsUpY&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S&index=6" target="_blank" rel="noopener">Part 6 - Training an AI to create poetry</a><br>


<hr>


## 3. "Neural Networks and DL" by Geoffrey Hinton

![Neural Networks and Deep Learning](/images/articles/en/technology/best-ml-courses-for-beginners/3-1.jpg)

**Neural Networks and Deep Learning - Geoffrey Hintons - Coursera - 2012 (Full course)**
<br>
An exciting opportunity to learn from one of the pioneers of the deep learning revolution. Geoffrey Hinton, who won a Turing Award. 
This is a unique opportunity to learn from one of the fathers of deep learning and gain insight into the latest advancements in the field.

It's a great course if you're looking to gain a comprehensive understanding of neural networks and deep learning. 

It covers the basics, like feedforward networks, activation functions, and backpropagation, and also dives into more advanced topics like deep belief networks, convolutional neural networks, and recurrent neural networks. The course is well-structured and provides hands-on experience, so you'll be able to apply what you learn immediately. 

I highly recommend it if you're looking to expand your knowledge in the field of deep learning!

Opportunity to learn about machine learning from one of the fathers of the deep learning revolution who won a Turing Award.

<a href="https://www.cs.toronto.edu/~hinton/coursera_lectures.html" target="_blank" rel="noopener">Lecture  Videos</a>

<a href="https://www.cs.toronto.edu/~hinton/coursera_slides.html" target="_blank" rel="noopener">Lecture Slides</a>


<hr>

## Summary
What sets these machine learning courses apart is the expertise of the instructors. 

They are highly knowledgeable about the material and have the ability to explain it in a clear and concise manner, making the learning experience easier and more enjoyable for beginners.


<hr>

### References
* <a href="https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ" target="_blank" rel="noopener">TensorFlow Channel</a>
* <a href="https://www.cs.toronto.edu/~hinton/nntut.html" target="_blank" rel="noopener">Geoffrey E. Hinton - Neural Network Tutorials</a><br>
* <a href="https://awards.acm.org/about/2018-turing" target="_blank" rel="noopener">Fathers of the Deep Learning Revolution Receive Turing Award</a><br>
* <a href="https://en.wikipedia.org/wiki/Geoffrey_Hinton" target="_blank" rel="noopener">Geoffrey Hinton - Wiki</a><br>
* <a href="https://ai.google/education/" target="_blank" rel="noopener">Education – Google AI</a><br>

Discover the world of AI with the best Machine Learning courses for beginners!

Best Machine Learning Courses for Beginners

## Introduction
To label images for object detection projects, we'll use the LabelImg tool. First, I'll let you know where the software is installed and how to open it. Then, we'll dive deep into the labeling work.

The software should be in the following folder structure. This means that the software should be organized in a specific way, with certain files and folders in specific locations within the main software directory.

```shell
TensorFlow/
├─ addons/
│  └─ labelImg/
```

Having a well-organized folder structure is important for keeping track of your files and making it easier to find what you're looking for. It can also make it easier for others to understand your project and collaborate with you, if that's something you're interested in.

<hr>


## Installation
If you have not installed the software yet, I recommend that you read the following article. The article has detailed instructions on how to install the software, so you can get it up and running on your computer in no time.

<a href="https://1step2learn.com/en/article/technology/install-labelimg-on-windows/" target="_blank" rel="noopener">How to Install LabelImg on Windows</a>

Following the instructions in the article will ensure that the software is installed correctly and that you have all of the necessary components and dependencies in place. This will help to minimize any potential issues and ensure that you can start using the software as soon as possible.

<hr>

## Open LabelImg
Once you have installed the software, you can open it by running the labelImg.py file in the command prompt (cmd) or terminal.

```shell
cd C:/TensorFlow/addons/labelImg
```

```shell
`python labelImg.py`
```

> You must keep the command prompt open when using LabelImg.


<hr>

## Open images
I usually put all the images in a folder and then load the folder in LabelImg. This makes it easy to label all the images in a batch, rather than loading each image one at a time.

![LabelImg](/images/articles/en/technology/label-images-od-labelimg/1-1.png)

One of the advantages of loading the images in a folder is that the file names appear in the side panel of LabelImg. This makes it easy to keep track of which images you have labeled and which ones you still need to work on. 

Additionally, you can navigate between the images using half of the keyboard, which can speed up the labeling process and make it even more efficient.


<hr>


## Draw a new box
Creating a new box is an important step in labeling your images for object detection. This is because the boxes are what define the objects you want to detect in your images.

So, let me explain how to create a new box. 
When you start a new project in LabelImg, you'll be prompted to choose a save format. There are several options to choose from, but in this case, we'll choose PascalVOC.Then, you'll select the "Create RectBox" tool, and then you'll use your mouse to click and drag a box around the object you want to label. You can adjust the size and position of the box as needed to ensure that it covers the object completely.

Once you have created the box, you'll need to label the object by entering the name in the field provided. 
You can also adjust the class label if needed, and specify any additional attributes that are relevant for your project.

![LabelImg](/images/articles/en/technology/label-images-od-labelimg/2-1.jpg)

PascalVOC is a well-established and widely used format for object detection annotations, and it's a good choice if you're new to the field. By choosing this format, you'll be able to save your annotations in a way that is compatible with many existing object detection frameworks and models such as TensorFlow.

Step 1: select your 'save format'.<br>
Step 2: Click on 'Create RectBox'.<br>
Step 3: draw a box (RectBox).<br>
Step 4: Select class (label) from the list (predefined_classes.txt).<br>


## Predefined classes .txt file
The "predefined_classes.txt" file contains a list of classes that can be used to train your machine learning model. This file is essentially a pre-defined list of object classes, and it allows you to quickly label objects in your images without having to manually enter the class name each time.

![LabelImg](/images/articles/en/technology/label-images-od-labelimg/3-1.png)

To use the "predefined_classes.txt" file, simply edit it to include the classes (or labels) that you want to train your model to detect. For example, if you're working on an object detection project for cars, you might have a list of classes that includes "sedan," "SUV," "truck," and so on.

Just to clarify, you can find the "predefined_classes.txt" file in the "data" folder of your LabelImg installation.

```shell
TensorFlow/
├─ addons/
│  └─ labelImg/
│  │  └─ data/
│  │  │  └─ predefined_classes.txt
```

The "data" folder is where LabelImg stores various data and configuration files, including the "predefined_classes.txt" file. So, if you need to access this file and edit it to include your desired classes, simply navigate to the "data" folder and open the "predefined_classes.txt" file with a text editor.

> Make sure to save a separate "predefined_classes.txt" file for each project you work on.

By having a separate "predefined_classes.txt" file for each project, you'll be able to keep track of the unique set of classes that you used for each project. This can be especially useful if you're working on multiple object detection projects simultaneously, as it will help you to avoid using conflicting or overlapping classes between projects.

![LabelImg](/images/articles/en/technology/label-images-od-labelimg/4-1.png)

It's important to note that if you make changes to the "predefined_classes.txt" file, you will need to reopen LabelImg for the changes to take effect.

This means that if you add, remove, or modify any classes in the "predefined_classes.txt" file, you will need to close LabelImg and then reopen it for the changes to be reflected in the software.

This is because LabelImg loads the classes from the "predefined_classes.txt" file only when the software is first opened, and it does not dynamically update the classes as you make changes to the file.

<hr>

## LabelImg Shortcuts

LabelImg provides several keyboard shortcuts to help you work more efficiently while labeling your images. Here are a few of the most commonly used shortcuts:

* Create RectBox: Press the 'W' key to quickly create a new bounding box.
* Zoom In: Use the 'Ctrl' + '+' keys to zoom in on the image you're labeling.
* Zoom Out: Use the 'Ctrl' + '-' keys to zoom out of the image you're labeling.
* Save (as xml): Use the 'Ctrl' + 'S' keys to save your labeled image and its annotations in xml format.
* Next Image: Press the 'D' key to move to the next image in the list of files (this shortcut is only available when the "File List" is not empty).
* Prev Image: Press the 'A' key to move to the previous image in the list of files.
* Delete RectBox: Use the 'Delete' key to remove a bounding box from the image.
* Edit Label: Use the 'Ctrl' + 'E' keys to edit the label of a bounding box.

By using these keyboard shortcuts, you can save time and make the labeling process more efficient.

<hr>

## Tips and tricks
Some tips to speed up the process:

1. Use 'Open Dir' instead of 'Open': When opening your images, it's faster to select the entire folder using 'Open Dir' instead of selecting each image individually using 'Open'.

2. Use shortcuts: As mentioned earlier, LabelImg has several keyboard shortcuts that can help you work more efficiently. Make sure to familiarize yourself with these shortcuts and use them often.

3. Define your own 'predefined_classes.txt': This file contains a list of classes to train your ML model. By creating your own predefined classes list, you can ensure that you have the correct labels for your project from the start.

4. Avoid adding new labels while labeling: It's best to have all your labels and classes defined in advance, before you start the labeling process. This will save you time and minimize the risk of errors.

5. Start labeling on a few pictures and train your model: Before you start labeling all of your images, it's a good idea to label a few images and train your model. This will help you identify any issues or mistakes early on and avoid wasting time later.

6. Don't be too lazy to zoom: When labeling, it's important to zoom in on the images to ensure that you're labeling the objects accurately.

7. Mark the whole object and give some space in the margins: When drawing the bounding boxes, make sure to include the entire object and give it a little space in the margins. This will help ensure that your model is able to identify the object more accurately.

8. Double-click on class name to select it: Instead of manually typing in the class name for each bounding box, you can double-click on the class name in the list to quickly select it.

![LabelImg](/images/articles/en/technology/label-images-od-labelimg/5.jpg)

9. Label the objects in an orderly manner, so that you don't have to constantly switch between different labels.

10. Take breaks when needed to avoid fatigue and ensure the labeling remains accurate.

11. Consider labeling with a partner to divide the work and increase efficiency.

12. Label a random sample of images first, to fine-tune the labels and make sure they meet the requirements of your project before labeling the entire dataset.

<hr>

## Save as xml
When you press the 'Ctrl' + 'S' shortcut or select "Save" from the "File" menu, LabelImg will generate an XML file that contains the annotations for the image you are labeling. This file will be saved in the same folder as the original image, with the same name as the image, but with a '.xml' extension.

![LabelImg](/images/articles/en/technology/label-images-od-labelimg/6.png)

For example, if your image is named 'building.jpg', the annotation file will be named 'building.xml'. It's important to save your annotations regularly so that you don't lose any work in case of a power outage or unexpected interruption.

![LabelImg](/images/articles/en/technology/label-images-od-labelimg/7.jpg)

<hr>

## Result
This screenshot depicts the LabelImg software window with a sample image of a building.

![](/images/articles/en/technology/label-images-od-labelimg/feature_image.jpg)

The building features several windows, chairs, and window-boxes. The image has been labeled using the LabelImg tool, with bounding boxes drawn around each object and the object class, such as "window", "chair", or "window box", labeled within each bounding box. 

The labels and bounding boxes are displayed in the software's main window, along with the image, and are also saved in an XML file for use in object detection projects.

The screenshot demonstrates how LabelImg can be used to efficiently label images for object detection tasks, with the labeled objects clearly visible in the software window.

<hr>

### References
1. <a href="https://github.com/tzutalin/labelImg" target="_blank" rel="noopener">GitHub - tzutalin/labelImg</a>

Streamline your manual image labeling process for object detection with LabelImg. Learn how to use the tool effectively and save time with tips and tricks.

How to Label Images for Object Detection Using LabelImg

## Why?
I know installations can be a real pain when it comes to AI and machine learning. 
That's why I decided to write a tutorial to simplify the installation process of LabelImg. 
I want to help people who may not be technical but still need to work with an image annotation tool. 
Hopefully, my tutorial will make their lives a little easier.

## Folders structure
```shell
TensorFlow/
├─ addons/
│  └─ labelImg/
```
Installing LabelImg inside the TensorFlow folder structure can be a bit tricky, but it can be done. The best place to install it is inside the addons folder. This way, it will be easier to keep track of your dependencies and make sure everything is organized properly.

<hr>

To install LabelImg inside the TensorFlow addons folder, you'll need to follow a few steps. First, you'll need to download the LabelImg repository from GitHub. Then, you'll need to extract the files into the TensorFlow addons folder. After that, you'll need to install the required dependencies, such as PyQt5, using pip. Finally, you'll need to run the labelImg.py script to start the application.

It's important to make sure that you've installed all the required dependencies before trying to run the LabelImg application. If you don't, you may encounter errors that can be difficult to debug.

By following these steps, you'll be able to install LabelImg inside the TensorFlow folder structure and take advantage of all the benefits it offers for working with image annotation.

<hr>

## Requirements
Python is a required dependency for LabelImg because the software is written in Python, a high-level programming language. 
In order to run LabelImg, you must have a version of Python installed on your computer.

By having Python installed on your computer, you will be able to run LabelImg and any other Python-based software or scripts you need for your AI and machine learning projects. This makes it an important tool to have in your toolkit, especially if you're working in the field of AI and machine learning.

<hr>

## LabelImg Download for Windows

By downloading LabelImg from GitHub, you'll have access to the latest version of the software, as well as the source code, which you can modify and customize as needed.

To download LabelImg from GitHub and install it on your system, follow these steps:

1. Visit the LabelImg GitHub page at <a href="https://github.com/tzutalin/labelImg" target="_blank" rel="noopener">github.com/tzutalin/labelImg</a>
2. Click the "Code" button and select "Download ZIP" from the drop-down menu.
3. Wait for the ZIP file to download to your computer.
4. Extract the contents of the ZIP file to a directory on your computer (TensorFlow/addons/labelImg).
5. Open a command prompt or terminal window and navigate to the directory where you extracted the LabelImg files (TensorFlow/addons/labelImg).


![labelImg](/images/articles/en/technology/install-labelimg-on-windows/1-1.png)

<hr>

## Install dependencies

**lxml**

```shell
pip install lxml
```
Annotation tools are often used to create XML files, which contain the annotations for the images. The XML files contain information such as the location and size of the objects in the image, as well as their labels.

![](/images/articles/en/technology/install-labelimg-on-windows/2-1.png)

That's why libraries like lxml are so important. They allow you to process and manipulate these XML files in a convenient and efficient way. With lxml, you can extract information from XML files, modify the data, and create new XML files with ease.

For example, if you're working on an image annotation project, you may need to extract information from the XML files, such as the location and size of the objects in the images. With lxml, you can use XPath to easily find the information you need in the XML files, without having to write a lot of code.


**PyQt5**

```shell
pip install PyQt5
```

```shell
Collecting PyQt5
  Downloading PyQt5-5.15.4-cp36.cp37.cp38.cp39-none-win_amd64.whl (6.8 MB)
     |█████████████████| 6.8 MB 939 kB/s
Collecting PyQt5-Qt5>=5.15
  Downloading PyQt5_Qt5-5.15.2-py3-none-win_amd64.whl (50.1 MB)
     |█████████████████| 50.1 MB 6.4 MB/s
Collecting PyQt5-sip<13,>=12.8
  Downloading PyQt5_sip-12.9.0-cp39-cp39-win_amd64.whl (63 kB)
     |█████████████████| 63 kB 4.8 MB/s
Installing collected packages: PyQt5-Qt5, PyQt5-sip, PyQt5
Successfully installed PyQt5-5.15.4 PyQt5-Qt5-5.15.2 PyQt5-sip-12.9.0
```

PyQt5 is a set of Python bindings for the Qt libraries, which are a set of C++ libraries for creating graphical user interfaces (GUIs). It's a popular choice for building cross-platform desktop applications in Python because of its ease of use and the large number of widgets and tools it provides.

LabelImg is an image annotation tool that has a graphical user interface, and PyQt5 is its dependency because it provides the framework for building the UI. In other words, PyQt5 is used to create the windows, buttons, and other elements of the LabelImg application.

![](/images/articles/en/technology/install-labelimg-on-windows/3.png)

Without PyQt5, LabelImg wouldn't be able to provide a user-friendly interface for annotating images. Instead, you would need to use the command line to interact with the tool, which can be more difficult and time-consuming for some users.

<hr>

## Install
Navigate to folder
```shell
C:\>cd TensorFlow
C:\TensorFlow>cd addons
C:\TensorFlow\addons>cd labelImg
```
![](/images/articles/en/technology/install-labelimg-on-windows/4.png)
<br>
```shell
pyrcc5 -o libs/resources.py resources.qrc
```
![](/images/articles/en/technology/install-labelimg-on-windows/5.png)


<hr>

## Open LabelImage

To run LabelImg, you'll need to open a command prompt or terminal window. This is because LabelImg is a command-line application and requires you to run the labelImg.py script from the command line.

![Open LabelImage](/images/articles/en/technology/install-labelimg-on-windows/6.png)

Once you've opened the command prompt or terminal window, you'll need to navigate to the folder where you've installed LabelImg. Then, you can run the labelImg.py script by typing the following command:

```shell
cd C:/TensorFlow/addons/labelImg <br>
python labelImg.py
```
This will start the LabelImg application and you'll be able to use the UI to annotate your images.
> Make sure you've installed all the required dependencies before trying to run LabelImg.

<hr>

When you run LabelImg, the application is executed in the command prompt or terminal window. As long as the window is open, LabelImg will continue to run. If you close the window, LabelImg will be terminated and you'll need to start it again.

The reason you need to keep the command prompt or terminal window open while LabelImg is running is because the application is being run in the context of the terminal. The terminal provides the environment for the application to run, and as long as the terminal is open, the application will continue to run.

Additionally, the terminal also serves as a way for you to interact with LabelImg. If you need to close the application, you can simply close the terminal window. And if you encounter any issues or errors while using LabelImg, you can examine the output in the terminal to try to troubleshoot the problem.

> You must keep the cmd or terminal open while LabelImg is running.

<hr>

## Result
![](/images/articles/en/technology/install-labelimg-on-windows/7.png)
<div class="imgDescription">LabelImg running on Windows 10 Pro.</div>

<hr>

You can learn how to use LabelImage at: 
<a href="https://1step2learn.com/wp/en/machine-learning/label-images-od-labelimg/" target="_blank" rel="noopener">How to label images for object detection</a>

<hr>

### References
1. <a href="https://github.com/tzutalin/labelImg" target="_blank" rel="noopener">GitHub - tzutalin/labelImg</a>
2. <a href="https://lxml.de/installation.html" target="_blank" rel="noopener">Installing lxml</a>
3. <a href="https://www.riverbankcomputing.com/software/pyqt/download" target="_blank" rel="noopener">PyQt Download</a>

LabelImg is a graphical image annotation tool and label object bounding boxes in images.

How to Install LabelImg Annotation Tool on Windows

## Build Models with Zero Coding Knowledge
Wow, I was beyond excited when a colleague told me about Microsoft's latest release - a tool (Lobe.ai) that lets you build a machine learning model without any programming knowledge! 

At first, I was a bit skeptical and didn't want to believe it. I mean, who wouldn't be a little worried about their profession becoming obsolete? But I decided to give it a chance and I am so glad I did! I installed it and was blown away by the ease and simplicity of it all. This is truly a game-changer in the field of machine learning!

## Train with your webcam
A must-watch video has just been made available that showcases how you can train a model using just a laptop. Trust me, this 10-minute video is worth your time.

In this video, you'll see a fascinating demonstration of creating a machine learning model with the help of a webcam, which detects whether you're drinking a glass of water. This is a great opportunity for you to learn about the power and potential of machine learning, so don't miss out!

<div class="videoContainer">
	<iframe src="https://www.youtube.com/embed/Mdcw3Sb98DA" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
</div>
<div class="imgDescription">Webcam demonstration.</div>

<hr>

## Mask or no mask
As an entrepreneur without any programming experience, you may have a brilliant idea for using machine learning to improve safety in stores. Specifically, you envision a system that alerts store employees with a beep as soon as a person enters the store without a mask.

Although the idea is innovative and relevant, you may be hesitant to move forward with it due to a lack of programming knowledge. However, you'll be happy to know that there is a solution that can help you bring your idea to life.

Enter Lobe.ai - a powerful tool that allows you to create machine learning models without any programming experience. With Lobe, you can focus on marketing your idea and demonstrating its potential to stores. If there is interest, you can then easily recruit experienced developers to help you bring your vision to life.

This solution not only showcases the potential of machine learning, but also helps stores ensure the safety of their customers. By using Lobe to create a simple and effective system for detecting mask usage, you could be at the forefront of a new era of store safety and technology.

<hr>

## Just drag and drop
Imagine having a powerful machine learning tool at your fingertips that allows you to train models with just a few clicks of a button. That's exactly what Lobe offers - an intuitive and user-friendly platform that simplifies the process of creating machine learning models.

In this scenario, you collected 60 images of people, 30 with masks and 30 without. You created two separate folders labeled "Mask" and "No Mask" and then simply dragged and dropped the images into Lobe. It did the rest - automatically labeling each image and beginning the training process.

![Lobe drag and drop](/images/articles/en/technology/lobe-ai-review/1.png)
<div class="imgDescription">Drag and drop.</div>

It was amazing to see how quickly and easily Lobe transformed your images into a powerful machine learning model. And the best part is that you didn't have to write a single line of code or have any programming knowledge. With Lobe, all you have to do is collect the data and let the platform do the work.

So if you're looking for an effortless way to get started with machine learning, look no further than Lobe. Just drag and drop your data, sit back, and watch as Lobe creates a model that can change the game for you and your business.

<hr>

## Offline capabilities
In today's fast-paced world, time is a valuable commodity. That's why Lobe offers a unique solution that enables you to train machine learning models directly on your computer, without having to upload your data to the cloud. This not only protects your privacy, but also ensures that your machine learning models are created and trained much faster.

![Lobe model example](/images/articles/en/technology/lobe-ai-review/3.png)
<div class="imgDescription">A model I created in a few minutes.</div>

Gone are the days of waiting half the time for your images or data to upload to a cloud-based platform. With Lobe, you can train your models locally, on your own computer, without sacrificing speed. This makes it ideal for individuals or businesses who need to process large amounts of data quickly and efficiently.`

In addition, Lobe's local capabilities also allow you to demonstrate your machine learning models to others, even if you don't have an internet connection. This is especially useful for businesses who may need to present their models in areas where internet connectivity is limited.

<hr>

## Training with Lobe
Training machine learning models can be a daunting task, especially for those who don't have a background in programming. But with Lobe, you'll find that training is easy, fun, and intuitive.

![Lobe train](/images/articles/en/technology/lobe-ai-review/2.png)
<div class="imgDescription">Model training.</div>

The Lobe interface is designed with simplicity in mind, making it easy to use even for those with no prior experience in machine learning. You'll find that the drag and drop feature, combined with the clear and straightforward labels, makes the training process a breeze.

Of course, no machine learning model is perfect, and there may be instances where some images require a bit of help. For example, you may come across an image of a man without a mask but with his hands covering his face. In such cases, all you have to do is either fix the label or delete the image, and Lobe will adjust the model accordingly.

<hr>

## Model types
Lobe supports the development of image classification models.

* Image Classification - Accuracy or Speed.
* Object Detection - Coming soon.
* Data Classification - Coming soon.


## Beta
This is a beta version, currently free.
I always recommend checking the <a href="https://lobe.ai/license/" target="_blank" rel="noopener">license</a>.

<hr>

## Download
Before downloading, they ask for details but without email validation.<br>
<a href="https://lobe.ai" target="_blank" rel="noopener">Download from Lobe.ai</a><br>


<hr>

## Conclusion
In conclusion, Lobe is a fantastic tool for anyone looking to start their journey in building machine learning models. With its intuitive and user-friendly interface, you don't need to have a large set of technical skills or programming knowledge to get started.

All you need is a collection of images, and with a simple drag and drop, and a few taps of a button, you'll be able to have a working machine learning model in no time. Whether you're an entrepreneur looking to demonstrate the technology, or an aspiring data scientist, Lobe is an excellent tool to have in your arsenal.

So why wait? Start your journey in machine learning today with Lobe and discover how easy and fun building models can be.

<hr>

### References 
1. <a href="https://lobe.ai/" target="_blank" rel="noopener">Lobe.ai</a>
2. <a href="https://aischool.microsoft.com/en-us/machine-learning/learning-paths/ml-crash-course" target="_blank" rel="noopener">Microsoft ML Crash Course</a>
2. <a href="https://docs.lobe.ai/docs/tips/tips/" target="_blank" rel="noopener">Lobe Help</a>

Build your own machine learning models with ease using microsoft's lobe.ai - no programming knowledge required, just a webcam or photos.

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