AI Career Development Learning Path

A free online learning program that covers what AI is, its different types and uses, ethical considerations, and its applications in business and environmental sustainability. The 10-hour course allows participants to learn at their own pace and receive a co-branded certificate upon completion.

A free online certificate course for professionals looking to transition to AI-related opportunities with a business focus, covering AI's impact on various industries and career empowerment strategies.

Data Science Career Path

Welcome to 'AI in Coding & Data Science: Master ChatGPT, Git Hub Copilot', a comprehensive course designed to revolutionize your coding and data science journey. This course is meticulously crafted to help you harness the power of AI in coding and data science, thereby boosting your productivity and making you future-ready.With Udemy's 30-day money-back guarantee, you have nothing to lose. So why wait? Start learning today and supercharge your coding efficiency with AI In this course, you will learn how to leverage AI tools like ChatGPT, Git Hub Copilot, and Noteable to enhance your coding efficiency and data science capabilities. These tools are designed to assist you in code generation, debugging, testing, data analysis, visualization, and machine learning. They can significantly speed up your development process and make it easier to get started with new technologies.The course is structured into several modules, each focusing on a different aspect of AI-assisted coding and data science. You will learn how to set up and use these AI tools, understand their features and benefits, and see them in action through hands-on exercises and real-world examples. The course also includes sections on how to use these tools for job search and interview preparation, making it a comprehensive guide for anyone looking to boost their career in development or data science.One of the highlights of this course is the section on ChatGPT Plugins for Data Analytics, Visualizations, and Machine Learning. Here, you will get hands-on experience with the Code Interpreter plugin, which allows you to generate Python code, perform data analysis, and even build machine learning models using natural language commands. You will work on several real-world datasets, including the Titanic, Iris, and MNIST datasets, and build predictive models to solve complex problems.By the end of this course, you will:Understand the role of AI in coding and data science and

No Prior Experience Needed – Learn with Real Projects!Are you curious about Data Science & Machine Learning but don’t know where to start? This beginner-friendly bootcamp is your perfect first step! We’ll guide you from absolute zero to building real-world projects—no math or coding background required!What You’ll Learn:Python for Beginners – Learn from scratch with easy-to-follow examples Data Science Essentials – Pandas, Num Py, and data visualization (Matplotlib & Seaborn) Machine Learning Made Simple – Predict trends, classify data & uncover patterns Hands-On Projects – Work with real datasets (sales predictions, customer behavior, and more!)AI & ChatGPT Basics – Get introduced to cutting-edge tools like LL Ms (Large Language Models) Why This Course?Perfect for Beginners – Starts slow, explains every step, and builds confidence Learn by Doing – No boring theory—just fun, practical projects you can showcase No Experience Needed – We teach Python & math basics along the way Supportive Community – Get help whenever you’re stuck Certificate of Completion – Boost your resume with a valuable skill Who Is This For? Total beginners who want to explore Data Science & AI Students & professionals looking for a high-income skill Career changers curious about tech jobs Anyone who wants to future-proof their skills in 2025! Tools You’ll Use (No Setup Hassle!): Python (easy-to-learn) Jupyter Notebooks (user-friendly coding) Scikit-Learn (simple ML models) ChatGPT & AI tools (see how they work!) Bonus: Downloadable exercises & solutions Cheat sheets & study guides Lifetime access & updates Start Your Data Science Journey Today – No Experience Needed!

Are you looking to ace your next data scientist or data analyst interview? Look no further! This comprehensive Udemy course, "Machine Learning & Data Science Interview Guide: 2025" is designed to equip you with the knowledge and skills necessary to excel in your data science job interviews.600+ Most Asked Interview Questions around Wide topics:Curated selection covering essential topics frequently tested during interviews.Dives deep into various domains, including Python, SQL, Statistics and Mathematics, Machine Learning and Deep Learning, Power BI, Advanced Excel, and Behavioral and Scenario-based questions.Python Section (100 Questions):Tests proficiency in coding with Python.Ensures a strong understanding of this popular programming language.SQL Section (100 Questions):Sharpens SQL querying skills.Tests knowledge of database querying and manipulation.Statistics and Mathematics Section (100 Questions):Solidifies understanding of foundational concepts.Covers essential statistical and mathematical principles.Machine Learning and Deep Learning Section (135 Questions):Explores theoretical knowledge and practical application.Prepares for ML and DL-related interview questions.Power BI and Advanced Excel Sections (105 Questions):Demonstrates expertise in data visualization and analysis tools.Covers a range of topics in Power BI and Advanced Excel functionalities.To round off your interview preparation, the course includes 60 questions that focus on behavioral and scenario-based aspects,

Let me tell you my story. I graduated with my Ph. D. in computational nano-electronics but I have been working as a data scientist in most of my career. My undergrad and graduate major was in electrical engineering (EE) and minor in Physics. After first year of my job in Intel as a "yield analysis engineer" (now they changed the title to Data Scientist), I literally broke into data science by taking plenty of online classes. I took numerous interviews, completed tons of projects and finally I broke into data science. I consider this as one of very important achievement in my life. Without having a degree in computer science (CS) or a statistics I got my second job as a Data Scientist. Since then I have been working as a Data Scientist. If I can break into data science without a CS or Stat degree I think you can do it too! In this class allow me sharing my journey towards data science and let me help you breaking into data science. Of course it is not fair to say that after taking one course you will be a data scientist. However we need to start some where. A good start and a good companion can take us further.We will definitely discuss Python, Pandas, Num Py, Sk-learn and all other most popular libraries out there. In this course we will also try to de-mystify important complex concepts of machine learning. Most of the lectures will be accompanied by code and practical examples. I will also use “white board” to explain the concepts which cannot be explained otherwise. A good data scientist should use white board for ideation, problem solving. I also want to mention that this course is not designed towards explaining all the math needed to “practice” machine learning. Also, I will be continuously upgrading the contents of this course to make sure that all the latest tools and libraries are taught here. Stay tuned!

Are you preparing for a career in Data Science or Machine Learning? Mastering the technical skills is crucial, but excelling in interviews requires more than just technical knowledge. Our course, "Data Science and Machine Learning: Top Interview Questions," equips you with the essential insights and strategies to ace your interviews with confidence.In this comprehensive course, we delve into the core concepts and practical techniques that are frequently tested in interviews for data science and machine learning roles. From feature engineering and model evaluation to unsupervised learning and ensemble methods, we cover a wide range of topics essential for success in interviews.Through a series of curated hands-on exercises, you will gain proficiency in:Crafting effective feature engineering and selection strategies to optimize model performance.Understanding various performance metrics and validation techniques to assess model accuracy and generalization.Exploring unsupervised learning algorithms and ensemble methods for tackling complex data problems.Leveraging cross-validation strategies to ensure robustness and reliability of your machine learning models.Moreover, our course goes beyond technical skills to offer invaluable interview insights, tips, and best practices. You'll learn how to articulate your thought process, communicate your solutions effectively, and tackle interview questions with clarity and confidence.Whether you're a seasoned professional or a beginner in the field, "Data Science and Machine Learning: Top Interview Questions" provides you with the knowledge and skills needed to excel in your next interview and kickstart your career in data science and machine learning. Enroll now and take the next step towards your dream job!

Hello!Welcome, and thanks for choosing How to Start & Grow Your Career in Machine Learning/Data Science!With companies in almost every industry finding ways to adopt machine learning, the demand for machine learning engineers and developers is higher than ever. Now is the best time to start considering a career in machine learning, and this course is here to guide you.This course is designed to provide you with resources and tips for getting that job and growing the career you desire.We provide tips from personal interview experiences and advice on how to pass different types of interviews with some of the hottest tech companies, such as Google, Qualcomm, Facebook, Etsy, Tesla, Apple, Samsung, Intel, and more.We hope you will come away from this course with the knowledge and confidence to navigate the job hunt, interviews, and industry jobs.NOTE This course reflects the instructor's personal experiences with US-based companies. However, she has also worked overseas, and if there is a high interest in international opportunities, we will consider adding additional FREE updates to this course about international experiences.We will cover the following topics:Examples of Machine Learning positions Relevant skills to have and courses to take How to gain the experience you need How to apply for jobs How to navigate the interview process How to approach internships and full-time positions Helpful resources Personal advice Why Learn From Class Creatives?Janice Pan is a full-time Senior Engineer in Artificial Intelligence at Shield AI. She has published papers in the fields of computer vision and video processing and has interned at some

A warm welcome to the Machine Learning and Data Science Interview Guide course by Cloud Excellence Academy.We provides this unique list of Data Science Interview Questions and Answers to help you prepare for the Data Scientist and Machine Learning Engineer interviews. This exhaustive list of important data science interview questions and answers might play a significant role in your interview preparation career and helping you get your next dream job. The course contains real questions with fully detailed explanations and solutions. Not only is the course designed for candidates to achieve a full understanding of possible interview questions, but also for recruiters to learn about what to look for in each question response. Why Data Science Job ?According to Glassdoor, a career as a Data Scientist is the best job in America! With an average base salary of over $120,000, not only do Data Scientists earn fantastic compensation, but they also get to work on some of the world's most interesting problems! Data Scientist positions are also rated as having some of the best work-life balances by Glassdoor. Companies are in dire need of filling out this unique role, and you can use this course to help you rock your Data Scientist Interview!Let's get started!Unlike others, We offer details explanation to each and every questions that will help you to understand the question100% money back guarantee (Unconditional, we assure that you will be satisfied with our services and be ready to face the data science interview).The Course highlights100 Questions on Machine Learning Algorithms , Use Cases ,Scenarios, Regularizations etc.75 Questions on Deep Learning ( ANNs , CNNs , RNNs , LSTMs , Transformer)100 Questions on Statistics and Probability 50 Question on Pyth

This course features 600+ Real and Most Asked Interview Questions for Machine Learning and Data Science that leading tech companies have asked.  Are you ready to master machine learning and data science? This comprehensive course, Master Machine Learning and Data Science: 600+ Real Interview Questions is designed to equip you with the knowledge and confidence needed to excel in your data science career. With over 600 real interview questions and detailed explanations, you'll gain a deep understanding of core concepts, practical skills, and advanced techniques.What You’ll Learn:The essential maths behind machine learning, including algebra, calculus, statistics, and probability.Data collection, wrangling, and preprocessing techniques using powerful tools like Pandas and Num Py.Key machine learning algorithms such as regression, classification, decision trees, and model evaluation.Deep learning fundamentals, including neural networks, computer vision, and natural language processing.Whether you’re a beginner or a professional looking to sharpen your skills, this course offers practical knowledge, real-world examples, and interview preparation strategies to help you stand out in the competitive field of data science. Join us and take the next step toward mastering machine learning and data science!Sample Questions:Question 1:You are building a predictive model for customer churn using a dataset that is highly imbalanced, with a much larger number of non-churning customers than churning ones. What technique would you apply to improve model evaluation and ensure that the model is not biased by the imbalanced classes?A) Use k-fold cross-validation to assess model performance across all data splits.  B) Use stratified sampling in your cross-validation to maintain the class distribution in each fold.