Module 01 – Intro to comprehensive ChatGPT
Learn chatgpt large language model (LLM) developed by OpenAI

Module 02 – Learn about machine learning and deep learning
-machine learning vs deep learning -supervised & unsupervised machine learning -computer vision

Module 03 – Prompt engineering and Using ChatGPT
-how to use ChatGPT -Basic and advanced prompts -Quiz and solutions

Module 04 – Creation of AI Images and Videos
-Using Dall-E application -Using Midjourney -Using Invideo AI

Module 05 – Application of research and development in AI developments
Websites, articles, journals and online papers on the topic of artificial intelligence (AI) and its application in the real life world

Explanation on machine learning
About Lesson

Machine learning is the powerhouse behind the technological transformations reshaping our world. Whether you’re a career professional seeking a competitive edge or a student curious about the future, understanding machine learning is a game-changer.

What is Machine Learning? At its core, machine learning is a subset of artificial intelligence (AI) that empowers computers to learn and make decisions without explicit programming. Instead of following rigid rules, machines adapt and improve their performance based on data.

How Does it Work? Imagine teaching a computer to recognize handwritten digits. In traditional programming, you’d outline specific rules for identifying each number. In machine learning, you provide the computer with a dataset of handwritten digits and let it figure out the patterns on its own. The computer learns from examples, refines its approach, and becomes adept at recognizing digits.

Types of Machine Learning:

  1. Supervised Learning: The computer is trained on a labeled dataset, making predictions or classifications based on that training.

  2. Unsupervised Learning: The computer explores data without explicit instructions, finding patterns or relationships on its own.

  3. Reinforcement Learning: The computer learns through trial and error, receiving feedback to improve its decision-making over time.

Applications in the Real World:

  1. Healthcare: Predicting diseases, personalizing treatment plans, and analyzing medical images.

  2. Finance: Detecting fraud, optimizing investment portfolios, and predicting market trends.

  3. Marketing: Targeting advertisements, personalizing user experiences, and predicting customer behavior.

  4. Autonomous Vehicles: Enabling cars to perceive their environment, make decisions, and navigate safely.

  5. Education: Personalizing learning experiences, assessing student performance, and automating administrative tasks.

Why Learn Machine Learning?

  1. Innovation: Machine learning drives technological breakthroughs, fostering innovation across industries.

  2. Career Opportunities: Professionals skilled in machine learning are in high demand, with diverse roles in data science, AI engineering, and more.

  3. Problem-Solving: Machine learning equips you with powerful tools to solve complex problems and extract insights from vast datasets.

Getting Started:

  1. Learn the Basics: Understand key concepts like algorithms, data preprocessing, and model evaluation.

  2. Coding Skills: Acquire proficiency in programming languages like Python and tools like TensorFlow or PyTorch.

  3. Explore Online Resources: Platforms like Coursera, edX, and Khan Academy offer courses in machine learning.

  4. Practice Projects: Apply your knowledge through hands-on projects, building a portfolio to showcase your skills.

Whether you’re a seasoned professional or a student on the brink of your career, machine learning opens doors to a future where technology is not just a tool but a dynamic partner in solving real-world challenges. Embrace the journey, and let the algorithms of machine learning guide you toward new horizons (ChatGPT, 2023). 

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