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In this course, you will be introduced to the fundamentals of crafting effective prompts. You will gain an understanding of how to refine and optimize prompts for a range of use cases. You will also explore techniques like zero-shot, few-shot, and chain-of-thought prompting. Finally, you will learn to identify potential risks associated with prompt engineering.

In this machine learning course, you will learn about the machine learning lifecycle, and how to use AWS services at every stage. Additionally, you will discover the diverse sources for machine learning models and learn techniques to evaluate their performance. You will also understand the importance of machine learning operations (MLOps) in streamlining the development and deployment of your machine learning projects.

Learning a new language can be exciting, but we don't always have the resources to push ourselves to the next level. In “Learning Languages ​​with AI”, you will learn how to enhance language learning with the help of generative AI tools. Identify your strengths and areas for improvement, then explore how you can responsibly harness AI to enhance your language skills even further. Throughout the course, you'll work to create unique and personalized lessons tailored to your learning style and goals. Learn to use strategic approaches based on your skill level to improve your spoken and written communication in any language. Whether you're new to generative AI tools or are a chatbot expert, this course will meet you where you are to embark on an enjoyable and satisfying language-learning journey.

Program managers are highly valued professionals who can earn up to $245,870 pa (Glassdoor). This course is ideal for anyone interested in starting a career in program management. This includes project managers, product managers, product owners, and scrum masters who want a focused introduction to the role of program manager. Program managers oversee multiple related projects to achieve strategic business objectives. In this course, you'll develop essential program management skills to get you started. You'll discover how to define programs and investigate the phases of the program management lifecycle, addressing challenges and solutions. You'll look at program management offices (PMOs) and learn about stakeholder management using tools like RACI charts. Plus, you'll look at PMI-PgMP certification requirements, consider other certifications, and explore program manager career paths and the essential skills required for success. Throughout this course, you'll get instructional guidance through videos and valuable hands-on practice in online labs to reinforce your learning. Then a final project will enable you to analyze program manager job postings and develop your learning plan to work in this role. Enroll now to kickstart your journey to becoming a prized program manager!

Sequential Decisions builds from math and algorithms that can be understood and used by Coursera Students. This course will start from a consideration of the simplest type of data streams and then gradually advance to more complex types of data and more nuanced decisions being made on that data. You will be able to: (a) program optimal decisions for data arriving from known distribution functions, (b) define error bars and nuanced hedges about ongoing data streams to reflect missing data and/or missing knowledge, (c)understand and use the connections from these models to further understand Markov Chains and Markov Processes and how these ideas connect to Reinforcement Learning and (d) Understand better the nuances between time-independent, time-dependent, one-dimensional and multi-dimensional data. The course is aimed at those working with data, this includes both those charged with analyzing the data and those in charge of making decisions based on that data.

Climate change impacts everyone - from record-breaking heat waves, to physical and mental health impacts, to food shortages. Understanding the science behind climate change provides you with the fundamental knowledge necessary for taking action in your community and enhancing your career. No matter your age, job, goals, or interests, this course will enable you to meaningfully engage with one of the defining challenges facing humanity today. What is the evidence for climate change? What key concepts describe the physical science of climate change? And are there ways to predict how the climate could change in the future? This short course provides everyone an entry-level overview of climate change science, with no science background needed! By the end of the course, you should be able to explain the causes of climate change and its impacts. Along the way, you'll be given opportunities to consider how to apply this knowledge in your own life or career. Learning about climate science is the first step in your journey to becoming a more engaged global citizen as you help determine our planet's future.

In this course, you will learn essential data engineering concepts such as what a data engineer does, how to perform data discovery, and which AWS data services are available for running, automating, and securing your data solutions and projects. The course introduces you to data engineering tools for continuous integration and continuous delivery (CI/CD), infrastructure as code (IaC), serverless applications, cost optimization, and authentication. You will also learn what networking concepts should be taken into consideration for data engineers. The course is divided into different modules. With the learning modules, you will learn new concepts and get introduced to AWS services to build your solution. In this course you will learn to: - Describe the data engineering role and personas they work with. - Identify how to perform data discovery to help define requirements and review considerations. - Identify common data architectures and appropriate AWS services. - Recognize how to orchestrate and automate data pipelines using AWS services. - Identify AWS security and monitoring services. - Identify common CI/CD tools used by data engineers on AWS. - Identify IaC tools used by data engineers on AWS. - Describe AWS Serverless Application Model (AWS SAM). - Identify AWS cost optimization tools. - Identify different AWS security mechanisms.

This course on financial analysis with ChatGPT and pairs trading integrates AI with financial expertise for insightful investment decisions, risk assessments, and financial planning. - Participants will build a trading bot using pairs trading, starting with an introduction to ChatGPT, project scope, and necessary tools. - The course covers pairs trading intuition, initial prompts, trading signal correction, and z-score computation, along with returns analysis and strategy testing. - Emphasis is placed on leveraging ChatGPT for efficient financial decision-making and trading. - Key learnings include algorithmic trading strategies and applying data science to finance. Designed for those with a fundamental understanding of Python, finance, and statistics, the course aims to enhance skills in using ChatGPT for coding, pairs trading, and trading strategies across various markets like stocks, forex, and cryptocurrencies.

This course is a complete guide to supervised and unsupervised learning using R, practical covering data science comprehensively. Companies globally use R to analyze vast data, and mastering it can enhance your career. Unlike other courses, this one provides in-depth knowledge of R's machine learning features, from data reading and cleaning to implementing and evaluating algorithms. -You'll explore topics such as R framework, data structures, pre-processing, machine learning, model building, and selection. -Emphasizing real data, you'll use packages like Caret and understand unsupervised learning, dimension reduction, and supervised learning. -You'll read data, pre-process in R Studio, implement K-means clustering, PCA, Random Forests, and evaluate models. Ideal for students starting with R Studio data science, those wanting to apply unsupervised learning to real data, and anyone with R experience aiming to enhance practical skills. Prior exposure to common machine learning terms would be needed.

This course delves into regression analysis using R, covering key concepts, software tools, and differences between statistical analysis and machine learning. - You'll learn data reading, cleaning, exploratory data analysis, and ordinary least squares (OLS) regression modeling, including theory, implementation, and result interpretation. - You'll tackle multicollinearity with techniques like principal component regression and LASSO regression, and cover variable and model selection for performance evaluation. - You'll handle OLS violations through data transformations and robust regression, and explore generalized linear models (GLMs) for logistic regression and count data analysis. - Advanced sections include non-linear and non-parametric techniques such as polynomial regression, GAMs, regression trees, and random forests. Ideal for statisticians, data analysts, and machine learning practitioners with basic R knowledge, this course blends theory with hands-on practice to enhance your regression analysis skills.

Dive into advanced data analytics with our Alteryx course: - Begin by mastering data cleansing, handling multiple files, sampling, and regex for parsing XML. - Enhance your skills with in-database processing, blending, and selecting data efficiently. - Advance to app and macro customization, creating batch macros, and handling errors with conditional logic. - Learn Alteryx's machine learning capabilities, from building models to deploying them for fraud detection and customer segmentation. Designed for data professionals with basic Alteryx knowledge, the course offers hands-on exercises and real-world examples. The end goal is to transform complex data challenges into actionable insights, enabling you to drive informed business decisions.

This course delves into deep learning and artificial neural networks using TensorFlow. - It begins with fundamental machine learning concepts, covering linear classification and regression, before exploring neurons, model learning, and predictions. - Core modules focus on forward propagation, activation functions, and multiclass classification, with practical examples like the MNIST dataset for image classification and regression tasks. - It also covers model saving, Keras usage, and hyperparameter selection. - The final sections provide an in-depth look at loss functions and gradient descent optimization techniques, including Adam. - Key outcomes include understanding machine learning concepts, implementing ANN models, and optimizing deep learning models using TensorFlow. This course suits those interested in deep learning, TensorFlow 2, and fundamental concepts for advanced neural networks like CNNs, RNNs, LSTMs, and transformers. Proficiency in Python and familiarity with NumPy and Matplotlib are required.