What you'll learn
Build machine learning models to use on real problems without a single line of code and no math skills
Use the main tools applied in classification, regression and time series forecasting problems
Implement machine learning in the following tools: Google Vertex AI, Data Robot AI, Obviously AI, Big ML, Microsoft Azure and Orange
Learn how to deploy machine learning models
Requirements
There are no prerequisites, however, you will enjoy it better if you know the basics of machine learning
No programming or math experience required
Description
If you want to learn machine learning but you feel intimidated by programming or math fundamentals, this course is for you!You are going to learn how to build projects using six tools that do not require any prior knowledge of computer programming or math! This course was designed for you to create hands-on projects quickly and easily, without a single line of code. It is suitable for beginners and also for students with intermediate or advanced knowledge, who need to increase productivity but at the same time do not have the time to implement code from scratch. You can perform exploratory data analysis, build, train, test and put machine learning models into production with a few clicks!We are going to cover 6 tools that are widely used for commercial projects: Google Vertex AI, Data Robot AI, Obviously AI, Big ML, Microsoft Azure Machine Learning, and Orange! All projects will be developed calmly and step by step, so that you can make the most of the content. There is an exercise along with the solution at the end of each section, so you can practice the steps for each tool! There are more than 30 lectures and 5 hours of videos!
Overview
Section 1: Introduction
Lecture 1 Course content
Lecture 2 Machine Learning - intuition
Lecture 3 Course materials
Section 2: Google Vertex AI
Lecture 4 Overview
Lecture 5 Datasets
Lecture 6 Training
Lecture 7 Regression metrics
Lecture 8 Deploy and predictions
Lecture 9 HOMEWORK
Lecture 10 Homework solution
Section 3: Data Robot AI
Lecture 11 Overview
Lecture 12 Datasets
Lecture 13 Classification metrics
Lecture 14 Training
Lecture 15 Deploy and predictions
Lecture 16 HOMEWORK
Lecture 17 Homework solution
Section 4: Obviously AI
Lecture 18 Overview
Lecture 19 Dataset, training, and predictions
Lecture 20 HOMEWORK
Lecture 21 Homework solution
Section 5: Big ML
Lecture 22 Overview
Lecture 23 Dataset
Lecture 24 Training and evaluating
Lecture 25 Predictions
Lecture 26 HOMEWORK
Lecture 27 Homework solution
Section 6: Microsoft Azure
Lecture 28 Overview
Lecture 29 Workspace and dataset
Lecture 30 Machine learning pipeline
Lecture 31 Evaluation and deploy
Lecture 32 Auto ML
Lecture 33 HOMEWORK
Lecture 34 Homework solution
Section 7: Orange
Lecture 35 Overview
Lecture 36 Classification
Lecture 37 Time series
Lecture 38 HOMEWORK
Lecture 39 Homework solution
Section 8: Final remarks
Lecture 40 Final remarks
People interested in building real machine learning models without a single line of code,People interested in starting their studies in Machine Learning and Data Science,Students who have little programming or math experience,Undergraduate and graduate students who are studying subjects related to the area of Artificial Intelligence

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