ZERO to HERO: AI and ML Starter Course with Hands-On Projects
1: ZERO to HERO: AI and ML Starter Course with Hands-On Projects
Learn Artificial Intelligence and Machine Learning with Hands-On Projects
Udemy Coupon Codes
This course aims to provide you with an overview of Artificial Intelligence (AI) & Machine Learning (ML) using simplified explanations and hands-on projects.
We will be covering the following topics in this course:
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Introduction to Artificial Intelligence (AI) and Neural Networks
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Difference between Artificial Intelligence, Machine Learning and Deep Learning
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Standardized Architecture for AI and ML Systems
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Machine Learning (ML) Algorithms - Supervised vs Unsupervised vs Reinforcement Learning
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Day in a life of an AI and ML Engineer
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Skills you will need for an AI and ML Engineer role
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Methods to evaluate the performance of Machine Learning Models
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Confusion Matrix, Accuracy, Precision, Recall
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Epoch, Learning Rate, Batch Size
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Hands-on AI and ML Projects with Open-Source Tools
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Summary
This is the first version of this course and it will be updated as we continue to witness the evolution of Artificial Intelligence and Machine Learning. My goal is to ensure people from all over the world are able to access this course and are able to learn the fundamentals of AI and Machine Learning and apply the same in their journey.
You will benefit from this course if:
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You want to learn the basics of AI & Machine Learning and you are looking for beginner-friendly hands-on exposure
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You are contemplating switching your career to Artificial Intelligence and Machine Learning
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You have a genuine interest in improving your understanding of AI and Machine Learning
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You want to learn the standardized framework used to build and evaluate AI/ML Models
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You are building a new startup and need to solidify your understanding of AI and ML concepts
Hope you will enjoy this course!
AI and ML Starter Course with Hands-On Projects | Udemy
What you'll learn
Basics of Human Brain and Artificial Neural Network - Biological Neurons and Artificial Neurons
Difference between Artificial Intelligence, Machine Learning and Deep Learning
3 Machine Learning Techniques - Supervised Learning, Unsupervised Learning, Reinforcement Learning with examples
Learn how to train, evaluate and optimize a Machine Learning model
Day in a life of an AI/ML Engineer
ML Model Evaluation Method - Confusion Matrix, Error, Recall, Precision, Accuracy
ML Model Optimization Method - Learning Rate, Epoch, Batch Size
ML Model Training - Using Open-Source Tools
Hands-On Project 1 - Build a Machine Learning model using Healthcare Dataset
Hands-On Project 2 - Build a Machine Learning model using Agriculture Dataset
Requirements
No specific requirements or prerequisites
Who this course is for:
Those who want to learn the basics of Artificial Intelligence (AI) and Machine Learning (ML) and need hands-on exposure.
You can learn artificial intelligence by taking an online course or enrolling in a data science bootcamp.
Many bootcamps provide an introduction to machine learning. Machine learning is a tool used by AI that involves exposing an algorithm to a large amount of data.
It allows the AI to learn faster.
AI and ML Starter Course with Hands-On Projects | Udemy
- Natural Language Processing. DeepLearning.AI. ...
- Mathematics for Machine Learning. Imperial College London. ...
- Machine Learning Engineering for Production (MLOps) ...
- Introduction to Data Science in Python. ...
- IBM Applied AI. ...
- Algorithms, Part I. ...
- Data Engineering, Big Data, and Machine Learning on GCP. ...
- AI For Everyone.
To make an AI, you need to identify the problem you're trying to solve, collect the right data, create algorithms, train the AI model, choose the right platform, pick a programming language, and, finally, deploy and monitor the operation of your AI system.
For those of you who have been wondering whether or not it's possible to learn AI without learning to code, the answer is yes!
With so many different courses and resources available online, there are plenty of ways for someone with a non-coding background to get started on their AI journey.
Mathematics for Data Science: Essential Mathematics for Machine Learning and AI.
Learn the mathematical foundations required to put you on your career path as a machine learning engineer or AI professional.
A solid foundation in mathematical knowledge is vital for the development of artificial intelligence (AI) systems ..
AI and ML Starter Course with Hands-On Projects | Udemy
I would say, AI and ML isnt as difficult to learn, but more difficult to apply for the right process optimization and applications.
You should perhaps first start with Python, learn concepts of Data Sciene and ML, followed by couple of strong Projects and Self Learning.
Can You Learn AI on Your Own? You can learn AI on your own, although it's more complicated than learning a programming language like Python.
There are many resources for teaching yourself AI, including YouTube videos, blogs, and free online courses.
Is AI hard to learn? Yes, it can be, and it's so hard that 93% of automation technologists themselves don't feel sufficiently prepared for upcoming challenges in the world of smart machine technologies.
Companies face many challenges when implementing artificial intelligence.
You'll need a bachelor's degree for these entry-level jobs. Your next step is to earn your master's degree in data science, computer science, software engineering, or similar. You may also want to work on gaining some certifications, building your skills, and creating your portfolio.
INSTRUCTOR
Prathamesh Khedekar
