Fundamentals Of Machine Learning For Business Professionals

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

What you'll learn

Developers who want to start their Machine Learning Journey

Managers and Leaders

Product Managers and Project Managers

Entreprenuers

Solution Architects

Requirements

No prior knowledge about Machine Learning, Cloud or Coding is required.

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.

Overview

Section 1: Getting Started with Machine Learning and AI

Lecture 1 Introduction to Machine Learning and AI

Lecture 2 What is Artificial Intelligence?

Lecture 3 The Economic Relefance of AI

Lecture 4 The state of AI in 2022—and a half decade in review

Lecture 5 What is machine learning?

Lecture 6 Requirements for a successful ML product

Lecture 7 Common challenges of machine learning

Lecture 8 ML framework and lifecycle. CRISP-ML(Q)

Lecture 9 Four main steps of Machine Learning process

Section 2: Fundamental Machine Learning Models and Techniques

Lecture 10 Models and Algorithms

Lecture 11 Types of Machine Learnings and Models

Lecture 12 Model Training

Lecture 13 Model Evaluation

Lecture 14 Data Splitting and K-Fold Cross Validation

Lecture 15 Deep Dive into Evaluation of Classification Models

Lecture 16 Deep Dive into Evaluation of Regression Models

Lecture 17 Deep Dive into Bias-Variance Trade-off

Section 3: Classic Machine Learning Models

Lecture 18 Linear Regression

Lecture 19 Logistic Regression

Lecture 20 Decision Trees

Lecture 21 Random Forest

Section 4: Machine Learning Product Lifecycles

Lecture 22 Introduction to Machine Learning Lifecycles

Lecture 23 Machine Learning Product Requirements

Lecture 24 Introduction to ML Project Lifecycle

Lecture 25 CRISP-ML: Business and Data Understanding

Lecture 26 CRISP-ML: Data Engineering

Lecture 27 CRISP-ML: Model Engineering

Lecture 28 CRISP-ML: Model Evaluation

Lecture 29 CRISP-ML: Model Deployment

Lecture 30 CRISP-ML: Model Monitoring and Maintenance

Section 5: Machine Learning Operations

Lecture 31 Introduction to Machine Learning Operations (MLOps)

Lecture 32 Maturity Level 0

Lecture 33 Maturity Level 1

Lecture 34 Maturity Level 2

Section 6: Case Study and Project Work

Lecture 35 Orpi the Real Estate Agency

Lecture 36 The Problem Statement and How Might We Question

Lecture 37 Stakeholder Management

Lecture 38 Justification for the ML-Based Solution

Lecture 39 Requirements for the ML-Based Product

Lecture 40 CRISP-ML(Q) Lifecycle for Orpi's ML-Based Project

Lecture 41 Machine Learning Operations (MLOps)

If you want to learn about machine learning and AI in great detail but want to avoid becoming a full-blown ML engineer, then this course is for you. The course teaches ML and AI in-depth and with a focus on business applications but takes out the complexities of the Python libraries and the learning curve of programming.