Скачать с ютуб How to Create a Robust Machine Learning Project Portfolio for Beginners | Module 1 Intro Video в хорошем качестве

How to Create a Robust Machine Learning Project Portfolio for Beginners | Module 1 Intro Video

Connect with me on Twitter here:   / theoyinbooke   Link to the Twitter Thread: https://twitter.com/TheOyinbooke/stat... How to BUILD a Robust Machine Learning Portfolio for Beginners. This will be a practical guide. To get the best, you should practice alongside. OBJECTIVE: Show you how to come up with a portfolio project to demonstrate what you know. Your CV is not sufficient these days, in fact, your online Portfolio should be your CV today. Why This? I recently put up a Machine Learning Internship role and realized the skills I need as the minimum is not available. I am not asking too much and these are skills that you don't need an official job to learn. I want to address this knowledge gap with this resource LEARNING STRUCTURE To put things in order, I will need to structure the learning into modules as follows: MODULE 1: 1. Introduction 2. The objective 3. Why This THREAD 4. Learning Structure 5. Learning Outcome MODULE 2: 1. Where Do I find the Datasets? 2. How to Identify What Use Case fits a Sample Data 3. Creating A Fictitious Business Use Case This is a very important module. this will satisfy your curiosity and power your learning quest. MODULE 3: 1. How to Solve the Created Business Use Case (steps and thought that should go into it). 2. Solving the Business Use Case. 3. Wrapping Up Your Documentation and Sharing Your Work. MODULE 4: What Next? This is what we will discuss in this module. how to scale the knowledge and skill to either previous portfolios or new ones. I can imagine how impactful this whole learning journey will be for you. Let's talk about the Learning Outcome. At the end of this learning journey, you will 1. Know where to get open data for machine learning projects 2. be able to identify what type of machine learning projects can be coined from a given dataset 3. demonstrate knowledge of business use case crafting 4. demonstrate understanding of the thought process for properly implementing a machine learning project 5. be able to build a machine learning project 6. demonstrate knowledge of project documentation 7. many more