Fundamentals of Machine Learning for Business Professionals



Unlocking Success for Managers, Leaders, Solution Architects, Project Managers, and Engineers – No Coding Required

What you will learn

Developers who want to start their Machine Learning Journey

Managers and Leaders

Product Managers and Project Managers

Entreprenuers

Solution Architects

Description

Introduction:

Machine learning has revolutionized numerous industries, including business, architecture, project management, and engineering. To stay competitive in this rapidly evolving landscape, professionals in these fields must grasp the fundamentals of machine learning. The course “Fundamentals of Machine Learning for Business Professionals” is explicitly designed for managers, leaders, entrepreneurs, product managers, business analysts, solution architects, project managers, and engineers, equipping them with the essential knowledge to leverage machine learning effectively.

Course Overview:

The comprehensive course “Fundamentals of Machine Learning for Business Professionals” bridges the gap between business professionals and the complexities of machine learning. It empowers managers, business professionals, solution architects, project managers, and engineers alike with the necessary skills and knowledge to harness the potential of machine learning in their respective roles. The course emphasizes practical applications and real-world case studies, ensuring a holistic understanding of the subject. No coding skills or knowledge of programming (i.e. Python) is required.

Key Learning Objectives:

1. Introduction to Machine Learning: Gain a solid understanding of core concepts, terminologies, and algorithms used in machine learning. Explore supervised and unsupervised learning, classification, regression, clustering, and other essential techniques.

2. Data Preparation and Feature Engineering: Master data preprocessing, cleaning, feature selection, and engineering. Learn how to transform raw data into a suitable format for machine learning algorithms.


3. Model Development and Evaluation: Dive into model development, including algorithm selection, training, and performance evaluation. Explore standard evaluation metrics and techniques to assess model accuracy and reliability.

4. Business Applications of Machine Learning: Highlight specific machine learning applications in the business domain. Explore customer segmentation, demand forecasting, fraud detection, recommendation systems, and predictive analytics using real-world case studies and industry examples.

5. Ethics and Bias in Machine Learning: Understand machine learning algorithms’ ethical implications and potential biases. Explore ethical considerations in data collection, model training, and decision-making processes. Learn strategies to mitigate bias and ensure fairness in machine learning applications.

6. Implementation and Deployment: Gain insights into implementing and deploying machine learning models in real-world scenarios. Topics include scalability, model deployment options, integration with existing systems, and performance monitoring and updates.

The course “Fundamentals of Machine Learning for Business Professionals” unlocks the potential of machine learning for anyone who wants to learn machine learning but does not want to become a professional ML engineer. Anyone who works in a business-focused role, such as C-suit managers, product managers, project managers, solution architects, entrepreneurs and even developers, can bring themselves up to speed with machine learning and AI.

Enrol now to unlock your success in machine learning for business professionals.

English
language

Content

Getting Started with Machine Learning and AI

Introduction to Machine Learning and AI
What is Artificial Intelligence?
The Economic Relefance of AI
The state of AI in 2022—and a half decade in review
What is machine learning?
Requirements for a successful ML product
Common challenges of machine learning
ML framework and lifecycle. CRISP-ML(Q)
Four main steps of Machine Learning process
Introduction to machine learning

Fundamental Machine Learning Models and Techniques

Models and Algorithms
Types of Machine Learnings and Models
Model Training
Model Evaluation
Data Splitting and K-Fold Cross Validation
Deep Dive into Evaluation of Classification Models
Deep Dive into Evaluation of Regression Models
Deep Dive into Bias-Variance Trade-off

Classic Machine Learning Models

Linear Regression
Logistic Regression
Decision Trees
Random Forest
Quiz : Fundamental ML Models and Techniques

Machine Learning Product Lifecycles

Introduction to Machine Learning Lifecycles
Machine Learning Product Requirements
Introduction to ML Project Lifecycle
CRISP-ML: Business and Data Understanding
CRISP-ML: Data Engineering
CRISP-ML: Model Engineering
CRISP-ML: Model Evaluation
CRISP-ML: Model Deployment
CRISP-ML: Model Monitoring and Maintenance

Machine Learning Operations

Introduction to Machine Learning Operations (MLOps)
Maturity Level 0
Maturity Level 1
Maturity Level 2
Machine Learning Product Lifecycle and MLOps

Case Study and Project Work

Orpi the Real Estate Agency
The Problem Statement and How Might We Question
Stakeholder Management
Justification for the ML-Based Solution
Requirements for the ML-Based Product
CRISP-ML(Q) Lifecycle for Orpi’s ML-Based Project
Machine Learning Operations (MLOps)

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