Download and Learn AI for Business Leaders Udacity Nanodegree Course 2023 for free with google drive download link.

Master the foundations of artificial intelligence so you can strategically implement AI in your company. Leverage machine learning technologies to power corporate growth, increase efficiency, and enhance customer experiences.

Built in partnership with

BMW

“Creating an AI course for business leaders so they can speak the same language as their engineers is challenging, but working with a variety of companies to understand the practical, technical, and commercial hurdles was incredibly insightful. Now, these learnings are available to all leaders who want to understand more about how to leverage Artificial Intelligence to advance their business.Erik BrynjolfssonProfessor of Management at MIT Sloan & Director of MIT’s Initiative on Digital Economy

What you will learn in AI for Business Leaders

You’ll develop foundational technical knowledge of machine learning and the business applications of artificial intelligence across industries. Through practical case studies, you’ll learn what strategic questions to ask, and how to formulate proposals when evaluating opportunities to embed machine learning processes and artificial intelligence technology into a corporate strategy. Finally, in the capstone project, you will build an AI-backed strategy that can be integrated into your own business.

4 – 8 Weeks to complete

AI for Business Leaders Nanodegree Intro Video:

Prerequisite knowledge

To optimize your chances of success in this Executive Program, we recommend prior exposure to statistics and probability, as well as experience in business decision-making in an IT or technical environment.

This program is intended for students who have spent time in a business setting, had exposure to business decision making, and have potentially worked on technical or IT projects.
In addition, a well-prepared learner will have:

  • Basic knowledge of mathematics (Algebra, Geometry, etc.)
  • Basic Statistics (Able to calculate the mean, median, and mode from a data set)
  • Prior exposure to statistics and probability in an academic or professional setting

The Paradigm Shift

Understand how to apply probabilistic reasoning to machine learning, and gain a working knowledge of the key terms and components involved in machine learning approaches, such as: algorithm, model, training, feature, test set, training set, and ground truth dataset. Then, develop ideas for machine learning and AI use cases for a business, and evaluate them for feasibility and impact.

The Math Behind the Magic

Understand how critical data attributes can affect a machine learning model, and distinguish the differences between classification, regression, optimization, and simulation in ML/AI applications. Become familiar with the applications of deep learning and how it can be applied to predictive modeling, reinforcement learning models, and optimization.

Architectures of AI Systems

Understand the importance, applications, and components of machine learning model architecture including classifiers, regressors, optimizers, simulators, policy learners,

and segmenters. Differentiate between the capabilities of natural language processing, voice/speech processing, and computer vision. Finally, build machine learning model architectures for a digital channel chatbot, negotiation engine, and visual classifier.

Working with Data

Learn how to label data for supervised learning. Understand the fundamental requirements of AI infrastructure, and how to overcome common implementation hurdles. Assess the feasibility of AI use cases in a range of business scenarios by evaluating data readiness.

Accuracy, Bias, and Ethics

Define the parameters for designing machine learning models including accuracy, underfitting and overfitting of data, and ethical frameworks.

Gathering Feedback

Learn how to build surveys and conduct interviews to solicit feedback on model prototypes. Identify key stakeholders inside and outside an organization to provide feedback in an iterative design process. Analyze the results of feedback from stakeholders to inform evaluation and prioritization of business use cases.

Thinking Bigger

Learn how to begin implementing AI use cases with small learning experiments, and build a roadmap deploying machine learning applications that strategically complement one another. Finally, create a proposal that integrates key use cases into a transformational business story.

CAPSTONE PROJECT – Deliver a Machine Learning / AI Strategy

Draw on all of the skills learned throughout the lessons to create an ML/AI strategy that is technically achievable and highly impactful on your business based on the evaluation of various AI-enabled use cases.

Project – Project Details

In this project, you will formulate a cohesive AI strategy for either your own company or a predefined business scenario for an automotive manufacturer or news and telecommunications conglomerate. You will: – Confirm the need for machine learning/artificial technologies and analyze the proposed use cases’ potential for success. – Create mock model architectures and evaluate the cost, timing, and accuracy constraints. Determine the success metrics of your top use cases. – Share your top use cases with stakeholders to solicit feedback for reprioritization of top use cases using the impact versus feasibility matrix. – Create a final proposal that is technically achievable and highly impactful based on the synthesis of conclusions from the process above.

The global Artificial Intelligence market size is expected to reach $554.3 billion by 2024, expanding at an annual growth rate of 16.4% YoY

AI for Business Leaders Download Link: