1. Machine Learning & Data Science A-Z: Hands-on Python 2021
Publisher : Navid Shirzadi
Course Language: English
Description
Learn NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Scipy and develop Machine Learning Models in Python
2. Diploma in Python with Data Science and Machine Learning
Publisher : Global Education Foundation
Course Language: English
Description
Python Bootcamp with Data Science & Machine Learning
3. 50 Must-Know Concepts, Algorithms in Machine Learning
Publisher: TheMachineLearning.Org.
Course Language: English
Description
This course is designed to give you an introduction to the syllabus of machine learning. If you want to get started with machine learning then this course will help you. It helps you to get ready for an interview with 50 concepts covering a varied range of topics. The course is intended not only for candidates with a full understanding of Machine Learning but also for recalling knowledge in data science.
4. Simplified: All About Neural Networks
Publisher: Jayanth Peetla
Course Language: English
Description
In this sequel series, we will learn the basics of what a neural network is, how they're used in the real world and two in-depth projects that allow us to use our skills in an applicable program. This course is dedicated to teaching students with an understanding of basic computer science concepts and little to no pre-existing knowledge of machine learning. Specifically, "Machine Learning Simplified" targets individuals who can't afford an expensive machine learning course and do not have the extensive pre-requisites the majority of courses require. In fact, the only major pre-requisite for taking this course is taking the first course in this series which is also available on Udemy for free. This course is divided into four major sections. The first one covers a brief introduction to neural networks and how they're used in the real world. The second section goes in-depth about the structure and mechanisms of neural networks. In the third section, we program a fully functional neural network using Spyder from Anaconda Navigator. Lastly, we conclude the course by discussing two types of special neural networks. Machine learning is a critical concept that is becoming very relevant in the status quo. So, before it's too late, join Simplified: All About Neural Networks and learn this topic as simple as possible!
5. Artificial Intelligence and Machine Learning Made Simple
Publisher : Sertac Ozker
Course Language: English
Description
Are you ready for the coming AI revolution? It already started to affect us. In this non-technical course, I will try to show you how to navigate the rise of Artificial Intelligence, Machine Learning and Deep Learning.
"Artificial Intelligence and Machine Learning Made Simple" is carefully created to match the needs of business leaders, managers and CXOs. This program was built to be broadly applicable across industries and roles. So regardless if you're coming from IT or marketing, work as an engineer or manager, this program may well be suited for you. Despite its broad applicability, this program will be most useful for those who are looking to understand and make better decisions surrounding machine learning projects in a business environment. My focus will be on explaining concepts in a way that is easily understandable regardless of your technical background.
When you finish the course, you will be comfortable with the buzzwords around Artificial Intelligence, Machine Learning and Deep Learning. You will have a certain understanding of AI applications and how to apply them to your business.
6. Basic Python/Machine Learning in Bioinformatics
Publisher: William Kang
Course Language: English
Description
This is a course intended for beginners interested in applying Python in Bioinformatics. We will go over basic Python concepts, useful Python libraries for bioinformatics/ML, and going through several mini-projects that will use these Python/ML concepts. These mini-projects include a sequence analysis (with no libraries) Python example, a Python sequence analysis example using libraries, and a basic Sklearn Machine Learning example.
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