How to find a job with Artificial Intelligence skills

1. Artificial Intelligence

Learn essential Artificial Intelligence concepts from AI experts like Peter Norvig and Sebastian Thrun, including search, optimization, planning, pattern recognition, and more...

2. Artificial Intelligence for Trading

Complete real-world projects designed by industry experts, covering topics from asset management to trading signal generation. Master AI algorithms for trading, and build your career-ready portfolio...

3. Artificial Intelligence: an Overview

This Specialization is intended for beginners seeking to enter the artificial intelligence world. Through five courses, you will cover artificial intelligence technical groundings (including machine learning and technologies), ethical and legal issues, which will give you a clear picture of what artificial intelligence is and what opportunities artificial intelligence will provide in the next future...

4. Artificial Intelligence: An Overview

The course will provide a non-technical overview of the artificial intelligence field. Initially, a discussion on the birth of AI is provided, remarking the seminal ideas and preliminary goals. Furthermore, the crucial weaknesses are presented and how these weaknesses have been circumvented. Then, the current state of AI is presented, in terms of goals, importance at national level, and strategies. Moreover, the taxonomy of the AI topics is presented...

5. Ethics of Artificial Intelligence

This course deals with the problems created, aggravated or transformed by AI. It is intended to give students a chance to reflect on the ethical, social, and cultural impact of AI by focusing on the issues faced by and brought about by professionals in AI but also by citizens, institutions and societies. The course addresses these topics by means of case studies and examples analyzed in the light of the main ethical frameworks...

6. CertNexus Certified Artificial Intelligence Practitioner

The Certified Artificial Intelligence Practitioner™ (CAIP) specialization prepares learners to earn an industry validated certification which will differentiate themselves from other job candidates and demnstrate proficiency in the concepts of Artificial intelligence (AI) and machine learning (ML) found in CAIP.\n\nAI and ML have become an essential part 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 specialization shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.\n\nThe specialization is designed for data science practitioners entering the field of artificial intelligence and will prepare learners for the CAIP certification exam.\n\nYour journey to CAIP Certification\n\n1) Complete the Coursera Certified Artificial Intelligence Practitioner Professional Certificate\n\n2) Review the CAIP AIP Exam Blueprint\n\n3) Purchase your CAIP Exam Voucher\n\n4) Register for your CAIP Exam...

7. Artificial Intelligence Privacy and Convenience

In this course, we will explore fundamental concepts involved in security and privacy of machine learning projects. Diving into the ethics behind these decisions, we will explore how to protect users from privacy violations while creating useful predictive models. We will also ask big questions about how businesses implement algorithms and how that affects user privacy and transparency now and in the future...

8. Artificial Intelligence and legal issues

The purpose of the course is to help students understand the legal implications related to the design and use of artificial intelligence systems, providing an overview of the risks and legal protections that can be envisaged and giving an overview of the legislation and legal principles currently applicable on the subject. In particular, the profiles of civil and criminal liability, protection in terms of intellectual property and the impacts of AI on the fundamental rights of the individual - including privacy and the right to non-discrimination – will be examined...

9. Artificial Intelligence on Microsoft Azure

Whether you're just beginning to work with Artificial Intelligence (AI) or you already have AI experience and are new to Microsoft Azure, this course provides you with everything you need to get started. Artificial Intelligence (AI) empowers amazing new solutions and experiences; and Microsoft Azure provides easy to use services to help you build solutions that seemed like science fiction a short time ago; enabling incredible advances in health care, financial management, environmental protection, and other areas to make a better world for everyone. In this course, you will learn the key AI concepts of machine learning, anomaly detection, computer vision, natural language processing, and conversational AI. You’ll see some of the ways that AI can be used and explore the principles of responsible AI that can help you understand some of the challenges facing developers as they try to create ethical AI solutions. This course will help you prepare for Exam AI-900: Microsoft Azure AI Fundamentals. This is the first course in a five-course program that prepares you to take the AI-900 certification exam. This course teaches you the core concepts and skills that are assessed in the AI fundamentals exam domains. This beginner course is suitable for IT personnel who are just beginning to work with Microsoft Azure and want to learn about Microsoft Azure offerings and get hands-on experience with the product. Microsoft Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Microsoft Azure Data Scientist Associate or Microsoft Azure AI Engineer Associate, but it is not a prerequisite for any of them. This course is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial. To be successful in this course, you need to have basic computer literacy and proficiency in the English language. You should be familiar with basic computing concepts and terminology, general technology concepts, including concepts of machine learning and artificial intelligence...

10. Introduction to Artificial Intelligence (AI)

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project. This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not...

11. Artificial Intelligence Ethics Certification

4.4

(2,656)

The AI Ethics Certification course teaches right and wrong, if such a thing exists, in the context of the artificial intelligence industry. This three-section training starts by asking What is ethics? We'll discuss its history, different philosophies, ethics in business, and learn the five most common principles. Second, ethics as it pertains specifically to AI with interviews from founders across the globe. We will examine commonly cited principles from governments and leaders and equate them to the five traditional pillars. Then finally, we establish a framework for your ethics roadmap. The goal is to provide stronger trust between you, your product or service, the people in the industry, and the public. Despite being a nebulous topic, we'll have fun on the path to understanding morality and committing to a better future...

12. Learn Artificial Intelligence Fundamentals

3.8

(81)

Do you want to learn Artificial Intelligence? Do you want to know what does AI actually mean? Well, you are at the right place. In this course we will give you the knowledge of the fundamentals concepts of the field of Artificial Intelligence. This course is designed specifically for beginners where we will take you step by step through our intuitive curriculum. Please have a look through the concepts and work your way through the quizzes. If anyone already has the knowledge of the fundamentals, please check through the curriculum to see if you need this course, after all our time is precious. We do not want you to repeat anything. We would highly encourage you to look at the contents menu first and see if you really need to take this course. What will you learn? - Definition of Artificial Intelligence- Application of Artificial Intelligence- History of Artificial Intelligence- Definition of Machine Learning - Types of Machine Learning - Industry Situation and Opportunities - What are Expert Systems?- What is Computer Vision? - What is Fuzzy Logic System?...

13. Artificial Intelligence Data Fairness and Bias

In this course, we will explore fundamental issues of fairness and bias in machine learning. As predictive models begin making important decisions, from college admission to loan decisions, it becomes paramount to keep models from making unfair predictions. From human bias to dataset awareness, we will explore many aspects of building more ethical models...

14. Artificial Intelligence Projects with Python

4.8

(79)

Welcome, In this course, we aim to specialize in artificial intelligence by working on 7 Machine Learning Projects and 7 Deep Learning Projects (14 AI Projects in total) at various levels (easy - medium - hard). Before starting the course, you should have basic Python knowledge. Our aim in this course is to turn real-life problems into projects and then solve them using latest versions of artificial intelligence algorithms (machine learning algortihms and deep learning algorithms) and Python. We will carry out some of our projects using machine learning and some using deep learning algorithms. In this way, you will have a general perspective on artificial intelligence. When you complete the projects in our course, you will get a clear understanding of the basic working principles of Machine Learning software and Deep Learning algorithms and the difference between them. In our course, we will use well known datasets that are widely used by high level education about Machine Learning as well as custom datasets. By doing our projects, you will master artificial intelligence concepts as well as learn these famous datasets. After completing the course, you will be able to easily produce solutions to the problems that you may encounter in real life. In our Machine Learning Projects we will use Scikit-Learn Python library. In our Deep Learning Projects we will use Tensorflow and Keras libraries. The course is composed of 14 Artificial Intelligence Projects  - Machine Learning Projects and Deep Learning Projects:- Project #1: House Price Prediction using Machine LearningIn this project we will build a artificial intelligence model that predicts house prices using sklearn multiple linear regression algortihm.- Project #2: Salary Calculation using Machine LearningIt is a tedious work to calculate each employee's salary according to employee's experience level. In this project we are going to build a machine learning model for exact calculation of employee salaries. Since most of salary values are non-linear, a simple linear function can not be used for this calculation process. Generally most of the companies have polynomial salary values for their employees. Therefore we will use polynomial linear regression algorithm for solution here.- Project #3: Handwritten Digit Recognition using Multiple Machine Learning ModelsIn this Project, we will implement a software that recognizes and makes sense of the objects in the photograph by using multiple Machine Learning Models together. Thanks to this project, you will see how you can combine machine learning models and combine several models to solve complex problems. You will have solved a problem that can be used in daily life (recognition of a handwritten text by a computer) using Artificial intelligence (AI).- Project #4: Advanced Customer Segmentation using Machine LearningIn this project, we will use a new and advanced segmentation library developed by the Massachusetts Institute of Technology (MIT). The customer data in our Customer Segmentation project, which is included in the entry and intermediate level projects, was simple and the K-Means clustering algorithm was sufficient for segmentation. But life is not that simple! When you have complex customer data, if you do clustering with K-Means, you may get erroneous results! Since the customer data in this project is complex data (both numeric and categorical) just like in real life, here we will use a special unsupervised learning algorithm instead of a standard model and divide our 2000 customers into groups with the latest artificial intelligence algorithms.- Project #5: IMDB Sentiment Analysis Using NLP (Natural Language Processing)With this Project, we will develop sentiment analysis software using the NLP concept. In this study, we will use the data set obtained from the Kaggle platform, a platform belonging to Google. Thanks to our artificial intelligence software that we will develop in this project, we will be able to automatically extract positive or negative comments from the English IMDB movie reviews that come with this data set. With this project, you will learn the concept of NLP in a very short time without drowning in theory.- Project #6: Building a Movie Recommendation SystemUsing the IMDB movie dataset, we will make a software that recommends 5 different movies that are most similar to that movie for any user watching a particular movie. You know, when you watch a movie on NETFLIX, it says the following may also interest you, just like that. While doing this, we will establish a Recommendation System by analyzing the likes of all users in the database who watched and liked the movie.- Project #7: Predicting Diabetes using Artificial Neural NetworksIn this project we are goint to predict whether or not a patient has diabets. We are going to use a well known dataset from Kaggle: Pima Indians Diabetes Database. In this dataset we have some medical test results and statistical information of 768 patients. We will have two different Artificial Neural Network solutions for this project: We will build the simplest ANN model using only 1 neuronWe will build another model using 2 hidden layers and a total of 25 neurons- Project #8: Image Classification using Convolutional Neural Network and Artificial Neural Network Algorithms (Deep Learning)We will make a project that automatically recognizes and classifies thousands of different image files using deep learning and artificial neural network algorithms. We will use Tensorflow and Keras libraries to achieve this.- Project #9: Airline Passenger(Time Series) Prediction using Keras LSTM (Deep Learning)We will use the Airline Passenger dataset for this project. This dataset provides the monthly total passenger numbers of a US airline from 1949 to 1960. We will produce a solution for this project by using the LSTM model available in Keras, and you will see a good example of how to solve Time Series problems with Deep Learning in general.- Project #10: San Francisco Crime Geographical Clustering using Machine LearningIn this project, we will perform geographic clustering using Geolocation information (Latitude & Longitude) using a data set created by the SFPD (San Francisco Police Department), which includes crimes committed in the city of San Francisco between 2003-2015. We will also learn to determine the optimal number of clusters (hyperparameter K-value) for this data set using the Elbow method. Then, we will display the geographic coordinates in our clustering results on a Python-based geographic map system. Finally, we will learn how to export this map we created to an HTML file.- Project #11: Image Classification (ImageNet Library) using Transfer Learning - Keras InceptionResNetV2 (Deep Learning)Transfer learning uses knowledge gained in solving a problem and applies it to a different but related problem. In Transfer Learning, we use a model that has been previously trained on a dataset and includes weights and biases that represent the properties of the dataset it was trained on. In this project, we will use the InceptionResNetV2 model, which has a pre-trained 164-layer advanced architecture and is pre-trained with an ImageNet dataset containing more than 1 million images.- Project #12: Military Aircraft (Satellite) Imagery Classification using Deep Learning (Custom Datasets)In this project, we will classify military aircraft images obtained from satellites (F-22 Raptor, Boeing B-52, A-10 Thunderbolt,.. etc.) using Deep Learning algorithms. In this project you will learn to create your own dataset and you will learn to use these customized datasets on pre-trained models.- Project #13: Sound Signal Processing for Deep Learning using Python (Custom Datasets) (Part - 1/2) In order to perform Sound Recognition and Classification with Python, the audio files must be in a format that can be used in Deep Learning algorithms. This project is essentially a pre-request project of our next project in our course, "Project#14 - Sound Classification using Deep Learning" Project. In this project we will process sound signals using Mel-Frequency Cepstral Coefficients (MFCC) algorithms and prepare audio for deep learning use. In this project you will learn how to prepare and process your own custom audio dataset for Deep Learning Training and Test operations.- Project #14: Sound Classification using Deep Learning (Part - 2/2)We will build a CNN (Convolutional Neural Network) Architecture with three Hidden Layers and 500 neurons in total (125-250-125) using Tensorflow and Keras libraries. We will use the pre-processed sound signals from previous project (Project #13) which has a dataset with a total size of 5.8 GB audio. Each project will be implemented by Python using Jupyter Notebook. Python source code of each project is included in relevant Udemy course section. You can download source codes for all projects.. This course will cover the following topics: Machine Learning (ML)Deep Learning (DL)Linear RegressionCustomer SegmentationNatural Language Processing (NLP)Artificial Neural Network (ANN)Convolutional Neural Network (CNN) Time Series PredictionSentiment AnalysisImage Classification Geographical ClusteringSound Signal Processing for Deep Learning ModelsAudio Classification with Deep LearningHere in this course you will find Artificial intelligence projects for beginners as well as Intermediate/Advanced Level Artificial Intelligence Projects. at the end of the course you will have a clear artificial intelligence definition in your mind and you will get the answer of the question what is AI ? or What is Machine Learning / Deep Learning?------------------------------        When I go visit different companies even at the top Silicon Valley companies, very often I see people trying to apply machine learning algorithms to some problem and sometimes they have been going at for six months. But sometimes when I look at what their doing, I say, I could have told you six months ago that you should be taking a learning algorithm and applying it in like the slightly modified way and your chance of success will have been much higher.                                               Andrew NG (Professor at Stanford University Department of Computer Science and Department of Electrical Engineering)...

15. Artificial Intelligence for Simple Games

4.5

(218)

Ever wish you could harness the power of Deep Learning and Machine Learning to craft intelligent bots built for gaming?If you're looking for a creative way to dive into Artificial Intelligence, then 'Artificial Intelligence for Simple Games' is your key to building lasting knowledge. Learn and test your AI knowledge of fundamental DL and ML algorithms using the fun and flexible environment of simple games such as Snake, the Travelling Salesman problem, mazes and more.1. Whether you're an absolute beginner or seasoned Machine Learning expert, this course provides a solid foundation of the basic and advanced concepts you need to build AI within a gaming environment and beyond.2. Key algorithms and concepts covered in this course include: Genetic Algorithms, Q-Learning, Deep Q-Learning with both Artificial Neural Networks and Convolutional Neural Networks.3. Dive into SuperDataScience's much-loved, interactive learning environment designed to build knowledge and intuition gradually with practical, yet challenging case studies.4. Code flexibility means that students will be able to experiment with different game scenarios and easily apply their learning to business problems outside of the gaming industry.'AI for Simple Games' CurriculumSection #1 - Dive into Genetic Algorithms by applying the famous Travelling Salesman Problem to an intergalactic game. The challenge will be to build a spaceship that travels across all planets in the shortest time possible! Section #2 - Learn the foundations of the model-free reinforcement learning algorithm, Q-Learning. Develop intuition and visualization skills, and try your hand at building a custom maze and design an AI able to find its way out. Section #3 - Go deep with Deep Q-Learning. Explore the fantastic world of Neural Networks using the OpenAI Gym development environment and learn how to build AIs for many other simple games! Section #4 - Finish off the course by building your very own version of the classic game, Snake! Here you'll utilize Convolutional Neural Networks by building an AI that mimics the same behavior we see when playing Snake...

16. Artificial Intelligence and Predictive Analysis

4

(181)

Artificial intelligence is the simulation of human intelligence through machines and mostly through computer systems. Artificial intelligence is a sub field of computer. It enables computers to do things which are normally done by human beings. This course is a comprehensive understanding of AI concepts and its application using Python and iPython. The training will include the following;What is Artificial Intelligence?IntelligenceApplications of AIProblem solvingAI search algorithmsInformed (Heuristic) Search StrategiesLocal Search AlgorithmsLearning SystemCommon SenseGenetic algorithmsExpert SystemsScikit-learn moduleWhat is Artificial Intelligence?The first idea of artificial intelligence was given by scientist Mr. Alan Turing around the time of the second world war. He suggested building a machine that can mimic the understanding of human intelligence and act like a human. Artificial Intelligence today is used in all fields of work specifically banking, insurance, manufacturing, retail, logistics and so on. Its application in medical diagnosis, robots, remote sensing, etc. is a high state of the art. AI as a subject includes the use of computer science, mathematics, statistics and domain expertise. AI has great advantages and so of them are mentioned below: It provides greater precision and accuracy on detection and predictionRobots trained on AI can be used to do the works which are difficult for usAI has created newer technological breakthroughs in our lifeFraudulent activities such as credit card transactions have become easier with AI technologiesAI can be used in time-consuming tasks and it can save a lot of time by becoming more efficient. You will be able to build the following as a practical project: -Classifiers of various typesLogic Programming based optimizersHeuristic Search performed on NP-complete problemsNatural Language Processing on text dataMachine Learning in general for several kinds of dataLogic and reasoning for model evaluation and interpretationRule-based Programming for business use casesDecision Making based on AI and MLStochastic methods such as time series and HMM...

17. Artificial Intelligence In Digital Marketing

4

(718)

Being smart in business means knowing what's just around the corner. It means thinking ahead and preparing for inevitable changes that will impact the way business is conducted. This is what allows a business to be resilient and to thrive in a changing environment. This video course will help you to prepare, and explain a number of concepts: AI vs Machine LearningHow to conduct SEO now that Google is an "AI first" companyChat botsProgrammatic advertisingBig dataRank BrainDigital assistantsData scienceSQLLatent Semantic IndexingThe future of internet marketingIn this course, you will gain a crystal ball with which to gaze into the future of internet marketing, and to ensure that you are ready for all those changes when they come. Topics covered: What Is AI And Machine Learning?Google As An AI-First CompanyPreparing For Semantic SearchBig DataComputer VersionAdvertisingEmail MarketingChat botsDeveloping Your AI Skills - Using SQLHow To Future Proof Your Marketing...

18. Demystifying Artificial Intelligence: Understanding Machine Learning

Curious about Artificial Intelligence? Start here with Machine Learning — what it is what it isnt and how we all interact with it every day. Join product developer and keynote speaker Christian Heilmann for a fascinating class all about Machine Learning. From how we all use it to where its headed in the future youll learn the ins and outs of how machines are processing our data finding patterns and making our lives easier every day. With a focus on how machine learning can power human interfaces and ease our interactions with technology lessons are packed with tools and tips for developers designers and the curious-minded. Key lessons include: Machine Learning myths capabilities and limitations...

19. Modern Artificial Intelligence with Zero Coding

4.6

(1,255)

Do you want to build super-powerful applications in Artificial intelligence (AI) but you don't know how to code?Are you intimidated by AI and don't know where to start?Or maybe you don't have a computer science degree and want to break into AI?Are you an aspiring entrepreneur who wants to maximize business revenue and reduce costs with AI but don't know how to get there quickly and efficiently?If the answer is yes to any of these questions, then this course is for you! Artificial intelligence is one of the top tech fields to be in right now! AI will change our lives in the same way electricity did 100 years ago. AI is widely adopted in Finance, banking, healthcare, transportation, and technology. The field is exploding with opportunities and career prospects. This course solves a key problem which is making AI available to anyone with no coding background or computer science degree. The purpose of this course is to provide you with knowledge of key aspects of modern AI without any intimidating mathematics and in a practical, easy, and fun way. The course provides students with practical hands-on experience using real-world datasets. In this course, we will assume that you have been recently hired as a consultant at a start-up in San Francisco. The CEO has tasked you to apply cutting-edge AI techniques to 5 projects. There is only one caveat, your key data scientist quit on you and do not know how to code, and you need to generate results fast. In fact, you only have one week to solve these key company problems. You will be provided with datasets from all these departments and you will be asked to achieve the following tasks: Project #1: Develop an AI model to detect people's emotions using Google Teachable Machines (Technology). Project #2: Develop an AI model to detect and classify chest disease using X-Ray chest data using Google Teachable Machines (HealthCare). Project #3: Predict Insurance Premium using Customer Features such as age, smoking habit, and geo-location using AWS AI AutoPilot (Business). Project #4: Detect Cardiovascular Disease using DataRobot AI (HealthCare). Project #5: Recognize food types and explore AI explainability using DataRobot AI (Technology)...