NoCode Machine Learning Practical Guide to Modern ML Tools
What you’ll learn
Master Machine Learning fundamentals without any intimidating mathematics and without writing a single line of code!
Build, Train and Deploy machine learning models that could predict loan default using customer features such as income and loan term using Microsoft Azure.
Master Google Vertex AI to automate the process of building and training machine learning models at scale.
Understand trained machine learning models by exploring feature importance.
Learn how to create multiple experiments using DataRobot AI and perform hyperparameter optimization.
Learn how to train multiple cutting-edge machine learning models using DataRobot AI and deploy the best model.
Requirements
The course has no prerequisites and is open to anyone with no or basic programming knowledge.
Students who enroll in this course will master Machine Learning fundamentals and directly apply these skills to solve real world challenging problems.
Description
Do you want to leverage the power of Machine Learning without writing any code?Do you want to break into Machine Learning, but you feel overwhelmed and intimidated?Do you want to leverage Machine Learning for your business, but you don’t have data science or mathematics background?If the answer is yes to any of these questions, you came to the right place!This course is the only course available online that empowers anyone with zero coding and mathematics background to build, train, test and deploy machine learning models at scale.Machine Learning is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects.Machine learning is the study of algorithms that teach computers to learn from experience. Through experience (i.e.: more training data), computers can continuously improve their performance. Machine Learning techniques are widely used in several sectors nowadays such as banking, healthcare, transportation, and technology.In this course, we will cover top 3 brand new tools to build, train and deploy cutting edge machine learning models without writing a single line of code! We will cover the new Google Vertex AI, Microsoft Azure Machine Learning Designer, and DataRobot AI.
Overview
Section 1: Course Introduction, Outline and Key Learning Outcomes
Lecture 1 Introduction and Welcome Message
Lecture 2 Best Practices, Success Tips and Certification
Lecture 3 Artificial Intelligence (AI) and Machine Learning (ML) Super Powers
Lecture 4 Machine Learning: The Big Picture
Lecture 5 Machine Learning Key Elements (Recipe!)
Lecture 6 Course Outline and Key Learning Outcomes
Section 2: University Admission Prediction Using Google Vertex AI
Lecture 7 The Rise of Machine Learning in Higher Education
Lecture 8 Reading Material: The Rise of Machine Learning in Higher Education
Lecture 9 Machine Learning Regression 101
Lecture 10 Google Vertex AI Demo Part 1: Setup and Upload
Lecture 11 Google Vertex AI Demo Part 2: Model Training
Lecture 12 Machine Learning Regression Models Metrics
Lecture 13 Google Vertex AI Demo Part 3: Test and Deploy Trained Model
Lecture 14 Recap and Concluding Remarks
Section 3: Bank Loan Risk Prediction Using Data Robot AI
Lecture 15 Introduction to Machine Learning in Finance/Banking
Lecture 16 Reading Material: Machine Learning and AI in the Finance/Banking Industry
Lecture 17 Project Overview: Bank Loan Risk Prediction
Lecture 18 Data Robot Demo Part #1: Sign Up and Dataset Upload
Lecture 19 Data Robot Demo Part #2: Target Selection & Exploratory Data Analysis
Lecture 20 Data Robot Demo Part #3: Model Training & Feature Importance
Lecture 21 Data Robot Demo Part #4: Confusion Matrix, Precision, Recall, ROC, AUC
Lecture 22 Data Robot Demo Part #5: Model Assessment
Lecture 23 Data Robot Demo Part #6: Hyper Parameters Tuning
Lecture 24 Data Robot Demo Part #7: Model Deployment and Making Inference
Lecture 25 Project Recap and Concluding Remarks
Section 4: Price Forecasting Using Microsoft Azure Machine Learning Designer
Lecture 26 Introduction to Machine Learning for Price Prediction
Lecture 27 Reading Material: Machine Learning for Price Forecasting
Lecture 28 Project Overview
Lecture 29 Microsoft Azure ML Demo Part #1: ML Signup and Studio Launch
Lecture 30 Microsoft Azure ML Demo Part #2: Dataset Creation
Lecture 31 Microsoft Azure ML Demo Part #3: Build ML Pipeline with ML Azure Designer
Lecture 32 Microsoft Azure ML Demo Part #4: Execute and Test the Pipeline
Lecture 33 Microsoft Azure ML Demo Part #5: Deploy Model and Make Inference
Lecture 34 Mini Project: Build and Test Multiple Models
Lecture 35 Mini Project Solution: Build and Test Multiple Models
Lecture 36 Project Recap and Concluding Remarks
Section 5: Conclusion
Lecture 37 Thanks for Watching!
Lecture 38 Links to Other Courses
Section 6: Appendix
Lecture 39 Introduction to XG-Boost [Optional Lecture/Additional Material]
Lecture 40 What is boosting
Lecture 41 Decision trees and ensemble learning
Lecture 42 Gradient Boosting Deep Dive #1 [Optional Lecture/Additional Material]
Lecture 43 Gradient Boosting Deep Dive #2 [Optional Lecture/Additional Material]
Lecture 44 AWS SageMaker XGBoost
Anyone who’s interested in building practical real-world Machine Learning applications but doesn’t have any coding skills (or have basic coding skills).,Beginner Machine Learning engineers wanting to advance their careers and build their portfolio without writing any code.,Business owners wanting to transform businesses by leveraging machine learning.,Beginner Data Scientists who are passionate about Machine Learning and want to gain real-world practical experience.
Course Information:
Udemy | English | 6h 30m | 4.15 GB
Created by: Dr. Ryan Ahmed, Ph.D., MBA
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