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Artificial Intelligence Entrepreneurship

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.

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Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

Description Fundamentals of Artificial Intelligence diploma  Artificial intelligence is widely popular and fascinates most people, especially when compared to technology-related topics. The reason behind it may be that it often goes and wanders beyond what we can imagine. We live in a world where we constantly hold smartphones capable of performing tasks we could never have even thought of just ten years ago. We use technological innovations every day, like voice commands, facial recognition, and other tools that can isolate portraits or take group photos. These are things we never imagined were possible, but we have grown used to them today. We have experienced the wonders of AI, and every new aspect awes us. Although technology has become a part of our daily lives, most do not know how it works. Looking at how amazing the effects of AI are, there is no doubt that the way it works will awe us just as much. Narrow AI is more common, which is AI meant to be used powerfully in one case, like AI for facial recognition or detection of spam. AI systems can be used to sort out vast piles of data like recommendation systems and search engines. They can also provide insights into the data. With this kind of narrow AI, it is expected that it can only perform what it is designed to do. A program meant to detect spam or recognize facial features cannot also be expected to play chess, compose songs, and record shows to watch in the future. The Fundamentals of Artificial Intelligence diploma course aims to remove AI's mystery so that the learners can develop a better in-depth understanding of the technology. The Fundamentals of Artificial Intelligence diploma course consists of an introduction that includes all the basic concepts in AI that will enable you to see what things are possible and what are not within the technology. It will also help you understand the effects of AI on our daily lives. After completing the Fundamentals of Artificial Intelligence diploma course, you will be able to define AI, discuss it, evaluate AI claims, explain the technologies that underpin AI, such as neural networks and machine learning, and understand the main implications of AI. The Fundamentals of Artificial Intelligence diploma course will also discuss AI's concerns and issues like biases, ethics, and jobs. You will also get expert advice regarding learning about AI and beginning a profession in AI-related fields. The fundamentals of Artificial Intelligence diploma does not demand that you have any expertise in computer science or programming. It is simply meant to introduce to you the essentials and basics in the field, regardless of whether you possess a technical background. We aim to encourage as many people as possible from all backgrounds to learn about artificial intelligence, what it is, what it can or cannot do, and how one can start creating methods of AI. The course is flexible and can be studied and completed at whatever pace you are comfortable with. What you will learn   1: An introduction to AI and data  2: Algorithms and Hardware  3: Uses of AI in computer applications and common processes  4: Using AI for medical needs  5: AI helping improve human interactions  6: Data analysis for AI  7: Machine learning and deep learning in AI  8: AI in hardware applications  9: AI as a nonstarter  10: AI in outer space  Course Outcomes After completing the course, you will receive a diploma certificate and an academic transcript from Elearn college. Assessment Each unit concludes with a multiple-choice examination. This exercise will help you recall the major aspects covered in the unit and help you ensure that you have not missed anything important in the unit. The results are readily available, which will help you see your mistakes and look at the topic once again. If the result is satisfactory, it is a green light for you to proceed to the next chapter. Accreditation Elearn College is a registered Ed-tech company under the UK Register of Learning( Ref No:10062668). After completing a course, you will be able to download the certificate and the transcript of the course from the website. For the learners who require a hard copy of the certificate and transcript, we will post it for them for an additional charge.

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Duration 0.5 Days 3 CPD hours This course is intended for This course is primarily designed for business leaders, consultants, product and project managers, and other decision-makers who are interested in growing the business by leveraging the power of AI. Other individuals who wish to explore basic AI concepts are also candidates for this course. This course is also designed to assist students in preparing for the CertNexus AIBIZ™ (Exam AIZ-210) credential. Overview In this course, you will identify ways in which AI can bring significant value to the business. You will: Describe AI fundamentals. Identify the functions of AI in business. Implement business requirements for AI. Artificial intelligence (AI) is not just another technology or process for the business to consider?it is a truly disruptive force, one that delivers an entirely new level of results across business sectors. Even organizations that resist adopting AI will feel its impact. If the organization wants to thrive and survive in this transforming business landscape, it will need to harness the power of AI. This course is designed to help business professionals conquer and move beyond the basics of AI to apply AI concepts for the benefit of the business. It will give you the essential knowledge of AI you'll need to steer the business forward. Lesson 1: AI Fundamentals Topic A: A Brief History of AI Topic B: AI Concepts Lesson 2: Functions of AI in Business Topic A: Improve User Experiences Topic B: Segment Audiences Topic C: Secure Assets Topic D: Optimize Processes Lesson 3: Implementing Business Requirements for AI Topic A: Identify Design Requirements Topic B: Identify Data Requirements Topic C: Identify Risks in Implementing AI Topic D: Develop an AI Strategy

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