Online Course – Certified Professional Internship in Generative AI Engineering with LLMs from Google, IBM

Advance your machine learning career with Gen AI and Large Language Models (LLMs). Learn the fundamentals of Gen AI and Large Language Models engineering in just 3 months.

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קורס אונליין - התמחות מקצועית מוסמכת בהנדסת AI גנרטיבי עם LLMs של Google

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

Building applications using frameworks and models such as BERT, GPT, and LLaMA.
Using the Hugging Face Transformers library.
Working with the PyTorch deep learning library.
Development and deployment of NLP-based applications.
Investigating tokenization, data payloads, language, and assimilation models.
Applying transformer techniques and attention mechanisms.
Command engineering.
Creating a data payload for NLP.
Developing and training a language model with a neural network.
Applying transformers to classify, build, and evaluate a translation model.
Model tuning.
LangChain tools implementation.
Building AI agents and applications with RAG and LangChain.

What you will learn in the course

Courses for which the course is suitable

Gen AI Engineer
Data Scientist
Machine Learning Engineer
Artificial Intelligence Developer
NLP Application Developer
Software Engineer in the Field of Artificial Intelligence
Natural Language Processing Expert
Broad Language Modeler (LLMs)

Internship – Series of 7 courses

The artificial intelligence (Gen AI) market is expected to grow by 46% annually through 2030 (Source: Statista). Gen AI engineers are in high demand. This program provides data scientists, machine learning engineers, and AI developers with the foundational skills in Gen AI, LLMs, and NLP that employers are looking for.

Gen AI engineers design systems that understand human language. They use LLMs and machine learning to build these systems.

Skills you will acquire in the program

Building applications using frameworks and models such as BERT, GPT, and LLaMA.
Using the Hugging Face Transformers library.
Working with the PyTorch deep learning library.
Development and deployment of NLP-based applications.
Investigating tokenization, data payloads, language, and assimilation models.
Applying transformer techniques and attention mechanisms.
Command engineering.

Through this specialization short course series, you will gain practical experience through hands-on labs and a project, which is great for interviews.

Prerequisites

Please note that you need basic knowledge of Python, machine learning, and neural networks. Familiarity with PyTorch would be helpful.

Practical learning project

Through hands-on labs and projects in each course, you will gain practical skills in using LLMs to develop NLP-based applications. The labs and projects include:

Creating a data payload for NLP.
Developing and training a language model with a neural network.
Applying transformers to classify, build, and evaluate a translation model.
Command engineering and contextual learning.
Model tuning.
LangChain tools implementation.
Building AI agents and applications with RAG and LangChain.

In the final course, you will complete a final project, in which you will apply what you have learned to develop a question-answering robot through a series of hands-on labs. You will start by loading your document from various sources, then implement text segmentation strategies to improve the responsiveness of the model, and use watsonx for training. You will also implement RAG to improve retrieval and configure a Gradio interface to build your question-answering robot. Finally, you will test and deploy your robot.

Details of the courses that make up the specialization

Removing HTML Tags to Extract Plain Text

Here are several methods to remove HTML tags and keep only plain text:

1. Using Regular Expressions in Python

import re
html_content = 'This is bold text.'
plain_text = re.sub('<[^', '', html_content)
print(plain_text)  # Output: This is bold text.

2. Using BeautifulSoup in Python

from bs4 import BeautifulSoup
html_content = 'This is bold text.'
soup = BeautifulSoup(html_content, 'html.parser')
plain_text = soup.get_text()
print(plain_text)  # Output: This is bold text.

3. Using JavaScript

function stripHtml(html) {
    var tempDiv = document.createElement('div');
    tempDiv.innerHTML = html;
    return tempDiv.textContent || tempDiv.innerText || '';
}
var htmlContent = 'This is bold text.';
var plainText = stripHtml(htmlContent);
console.log(plainText);  // Output: This is bold text.

4. Using PHP

$html_content = 'This is bold text.';
$plain_text = strip_tags($html_content);
echo $plain_text;  // Output: This is bold text.