Chatgpt Masterclass: The Guide To Ai & Prompt Engineering by Code Learn Academy

Mastering ChatGPT: A Comprehensive Course on AI and Advanced Prompt Engineering Strategies

What you'll learn

AB Testing in R

Al Ethics

Artificial Intelligence (Al) Strategy

Data Fluency

Data Preparation in Excel

Data Visualization in Excel

Deep Learning for Text with PyTorch

Dimensionality Reduction in R

End-to-End Machine Learning

Requirements

Basic understanding of statistics and hypothesis testing.

Familiarity with experimental design principles.

Knowledge of the domain or industry for effective testing.

Background in ethics or philosophy is beneficial.

Familiarity with ethical considerations in technology.

Understanding of the societal impact of AI.

Basic knowledge of AI concepts and applications.

Understanding of business strategy and objectives.

Familiarity with industry trends in AI adoption.

Basic understanding of data concepts and terminology.

Proficiency in using spreadsheet software like Excel.

Familiarity with basic statistical analysis.

Proficiency in using Excel for data entry and manipulation.

Basic knowledge of data cleaning and formatting.

Understanding of common data quality issues.

Proficiency in Excel for data visualization.

Understanding of principles of effective data visualization.

Knowledge of different chart types and when to use them.

Familiarity with Python programming language.

Basic understanding of machine learning concepts.

Prior experience with neural networks is beneficial.

Proficiency in the R programming language.

Understanding of data preprocessing and feature engineering.

Knowledge of the challenges of high-dimensional data.

Proficiency in a programming language like Python.

Understanding of machine learning algorithms and models.

Knowledge of the complete machine learning pipeline from data preparation to model deployment.

Description

A/B Testing in R is a course offered by Code Learn Academy , focusing on the exploration of A/B testing using the R programming language. A/B testing is a common experimental design employed in both industry and academia to investigate human behavior. These tests compare two variables to determine if there is a significant difference in performance measurements and whether the measurements vary significantly by a meaningful method. By mastering A/B testing and interpreting results, you can make data-driven decisions and predictions.In this course, you will learn what questions A/B tests answer, essential considerations for A/B testing, how to respond to existing questions, and how to visualize data. You will also discover how to determine the required sample size for an experiment, perform appropriate analyses for data and existing hypotheses, ensure that results can be confidently considered, and present results to an audience without statistical background. The course covers both parametric and non-parametric A/B tests, such as the t-test, Mann-Whitney U test, Chi-Square independence test, Fisher's exact test, and Pearson and Spearman correlation. Additionally, power analysis will be examined for each test.The AI Ethics course has been released by the Code Learn Academy. This introductory course on artificial intelligence ethics provides a comprehensive overview of ethical considerations in the rapidly evolving field of artificial intelligence. It encompasses industry, policy-making, academia, and society in general, covering the principles of AI ethics, strategies for fostering fair and just artificial intelligence systems, methods for minimizing biases, and approaches to addressing key issues and building user trust. Throughout this course, you will learn the principles of ethical artificial intelligence and expand your understanding of common challenges and opportunities in the field of AI ethics. Through practical exercises, you will develop the skills to create ethical artificial intelligence.The Artificial Intelligence (AI) Strategy course has been released by the Code Learn Academy. You've likely heard about various strategies such as business, data, and artificial intelligence, and have been amazed by how they interconnect. To understand how to integrate these intertwined strategies to create a robust strategic framework for organizations active in today's data-centric world, take this course. Additionally, you will explore the role of an AI strategist in successfully transforming artificial intelligence that aligns well with business strategic objectives.When formulating an effective artificial intelligence strategy, you will begin by understanding the differences between artificial intelligence and traditional software. Such distinctions aid in developing the skill of appropriately discerning the suitability of artificial intelligence. You will also learn to set realistic business goals and define appropriate criteria for project success. As you progress, you'll gather information about evaluating the return on investment for projects that lead to the creation of such complex technology.Data Fluency, a course on data literacy, is published by the Code Learn Academy. Data is ubiquitous, and in today's data-centric world, being data-fluent is not just a necessity for individuals but also for entire organizations. Data fluency is not only about understanding data but also about the ability to work with and effectively use data for data-driven decision-making.This course introduces you to the exciting concept of data fluency, covering the best practices and essential skills required to master data fluency. You will start by learning the meaning of data fluency and its distinction from data literacy. Additionally, you will become familiar with the significance of data fluency in today's world.The course provides a framework for achieving data fluency at both individual and organizational levels. Subsequently, you will explore the data-centric behaviors of individuals along with the skills they use, from identifying business problems with data from the initial stage to conveying information effectively for decision-making.The "Data Preparation in Excel" course has been released by the Code Learn Academy. In this course, you will become familiar with the process of preparing and cleaning raw data in Excel spreadsheets. The lesson guides you on utilizing the various features available in Excel, enabling you to import data from different sources. Through filtering, sorting, and organizing your columns and rows, you'll learn to prepare your data for subsequent analyses in the most effective manner possible.In addition to the internal features provided by Excel, you will learn to use various functions for managing and manipulating dates and text strings. Familiarity with logical functions will empower you to create new flags and classifications in your raw data. Furthermore, you'll understand how to combine different logical functions in nested formulas. The course also covers the usage of search functions in Excel to import data from various sheets and identify specific results in large datasets.Lastly, the course provides an overview of PivotTables, a powerful Excel feature that allows you to summarize and analyze large volumes of data using dynamic tables."Data Visualization in Excel" is a course offered by the Code Learn Academy. In this course, you will delve into the fundamentals of Excel charts, equipping yourself with the skills to create impactful visualizations and customize various chart types. With a comprehensive understanding of series and categories, you will gain the expertise needed to transform data into engaging narratives that captivate your audience. Explore working with dual-axis series and create more advanced charts such as bullet charts, waterfall charts, or scatter plots. Additionally, we will examine various chart editing options. Data visualization, like any other field, has its best practices, and it's time to take a closer look at do's and don'ts. We will enhance our skills in selecting chart elements, using colors, legends, and labels, learning how to troubleshoot and customize visual weak points for the benefit of end users.The course "Deep Learning for Text with PyTorch" is published by Code Learn Academy. Embark on an exciting journey of deep learning for text with PyTorch. This course introduces you to the skills of dealing with various challenges related to text. You will become familiar with the principles of text processing, including encoding and embedding. Various models such as CNNs, RNNs, GANs, and pre-trained models will be applied using textual data. Finally, you will delve into advanced topics such as transfer learning techniques, attention mechanisms, and how to safeguard your models against adversarial attacks.By the end of this course, you will have the skills to build powerful deep learning models for text. Explore text classification and its role in Natural Language Processing (NLP). Apply your skills to implement word embeddings and develop Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for text classification using PyTorch. Understand how to evaluate your models using appropriate metrics.The course "Dimensionality Reduction in R" has been released by Code Learn Academy. Have you ever worked with datasets containing numerous features? Do you really need all these features? Which ones are more important? In this course, you will learn dimensionality reduction techniques that help simplify your data and models, allowing you to retain the essential information in your original data and achieve good predictive performance with the models you build. We live in the age of information, where the skill of extracting meaningful insights from data is lucrative. Models trained on reduced data learn faster. In production, smaller models mean quicker response times. Perhaps most importantly, understanding your data and building smaller models is key. Dimensionality reduction is your winning edge in the field of data science. You'll learn the difference between feature selection and feature extraction using R, identifying and removing features with little or redundant information while retaining features with the most information. This is feature selection. You'll also learn how to extract combinations of features as compact components that contain maximum information.The "End-to-End Machine Learning" course, published by the Code Learn Academy, guides learners through the intricacies of designing, training, and deploying machine learning models. In this comprehensive course, you will delve into the world of machine learning, discovering how to design, train, and deploy final models. Through engaging examples and practical exercises, you will learn to tackle complex data challenges and build powerful ML models. By the end of this course, you will be equipped with the skills needed to create, monitor, and maintain high-performance models, along with practical insights.You will start by learning the principles of Exploratory Data Analysis (EDA) and data preparation. You'll clean and preprocess your data, ensuring it's ready for model training. Then, you'll master the art of feature engineering and selection to optimize your models for real-world challenges.The course covers using the Boruta library for feature selection, recording experiments with MLFlow, and fine-tuning models using k-fold cross-validation. You'll uncover the secrets of effective error metrics and explore the importance of feature stores and model registries in the context of end-to-end machine learning frameworks. You'll also learn how to monitor and evaluate your model's performance over time using Docker and AWS. The concept of data drift and how to detect it using statistical tests will be comprehensively understood.Feedback loops, retraining, and labeling strategies will be implemented to maintain the performance of your models in the face of ever-changing data.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: AB Testing in R

Lecture 2 chapter-1-introduction-to-a

Lecture 3 chapter-1-introduction-to-b

Lecture 4 chapter-1-introduction-to-c

Lecture 5 chapter-2-comparing-groups-a

Lecture 6 chapter-2-comparing-groups-b

Lecture 7 chapter-2-comparing-groups-c

Lecture 8 chapter-2-comparing-groups-d

Lecture 9 chapter-3-associations-of-variables-a

Lecture 10 chapter-3-associations-of-variables-b

Lecture 11 chapter-3-associations-of-variables-c

Lecture 12 chapter-3-associations-of-variables-d

Lecture 13 chapter-4-regression-and-prediction-a

Lecture 14 chapter-4-regression-and-prediction-b

Lecture 15 chapter-4-regression-and-prediction-c

Lecture 16 chapter-4-regression-and-prediction-d

Lecture 17 chapter-4-regression-and-prediction-e

Section 3: AI Ethics

Lecture 18 chapter-1-approaching-ai-ethics-a

Lecture 19 chapter-1-approaching-ai-ethics-b

Lecture 20 chapter-1-approaching-ai-ethics-c

Lecture 21 chapter-2-below-the-surface-ai-ethics-a

Lecture 22 chapter-2-below-the-surface-ai-ethics-b

Lecture 23 chapter-2-below-the-surface-ai-ethics-c

Lecture 24 chapter-2-below-the-surface-ai-ethics-d

Lecture 25 chapter-3-the-way-forward-ai-ethics-a

Lecture 26 chapter-3-the-way-forward-ai-ethics-b

Lecture 27 chapter-3-the-way-forward-ai-ethics-c

Lecture 28 chapter-3-the-way-forward-ai-ethics-d

Section 4: Artificial Intelligence (AI) Strategy

Lecture 29 chapter-1-fundamentals-of-ai-strategy-a

Lecture 30 chapter-1-fundamentals-of-ai-strategy-b

Lecture 31 chapter-1-fundamentals-of-ai-strategy-c

Lecture 32 chapter-2-designing-a-winning-ai-strategy-a

Lecture 33 chapter-2-designing-a-winning-ai-strategy-b

Lecture 34 chapter-2-designing-a-winning-ai-strategy-c

Lecture 35 chapter-2-designing-a-winning-ai-strategy-d

Lecture 36 chapter-3–components-of-ai-strategy-a

Lecture 37 chapter-3–components-of-ai-strategy-b

Lecture 38 chapter-3–components-of-ai-strategy-c

Lecture 39 chapter-3–components-of-ai-strategy-d

Lecture 40 chapter-4-time-for-action-a

Lecture 41 chapter-4-time-for-action-b

Lecture 42 chapter-4-time-for-action-c

Lecture 43 chapter-4-time-for-action-d

Lecture 44 chapter-4-time-for-action-e

Section 5: Data Fluency

Lecture 45 chapter-1-the-journey-to-data-fluency-a

Lecture 46 chapter-1-the-journey-to-data-fluency-b

Lecture 47 chapter-1-the-journey-to-data-fluency-c

Lecture 48 chapter-2-data-fluent-individuals-a

Lecture 49 chapter-2-data-fluent-individuals-b

Lecture 50 chapter-2-data-fluent-individuals-c

Lecture 51 chapter-2-data-fluent-individuals-d

Lecture 52 chapter-3–data-fluent-organization-a

Lecture 53 chapter-3–data-fluent-organization-b

Lecture 54 chapter-3–data-fluent-organization-c

Lecture 55 chapter-3–data-fluent-organization-d

Lecture 56 chapter-3–data-fluent-organization-e

Section 6: Data Preparation in Excel

Lecture 57 chapter-1-starting-data-preparation-in-excel-a

Lecture 58 chapter-1-starting-data-preparation-in-excel-b

Lecture 59 chapter-2-functions-for-data-preparation-a

Lecture 60 chapter-2-functions-for-data-preparation-b

Lecture 61 chapter-3-conditional-formulas-a

Lecture 62 chapter-3-conditional-formulas-b

Lecture 63 chapter-4-lookups-and-data-transformation-a

Lecture 64 chapter-4-lookups-and-data-transformation-b

Lecture 65 chapter-4-lookups-and-data-transformation-c

Section 7: Data Visualization in Excel

Lecture 66 chapter-1–building-basic-charts-a

Lecture 67 chapter-1–building-basic-charts-b

Lecture 68 chapter-2-advancing-to-more-complex-charts-a

Lecture 69 chapter-2-advancing-to-more-complex-charts-b

Lecture 70 chapter-3-data-visualization-best-practices-a

Lecture 71 chapter-3-data-visualization-best-practices-b

Lecture 72 chapter-4-visualizing-disaggregated-data-with-pivotcharts-a

Lecture 73 chapter-4-visualizing-disaggregated-data-with-pivotcharts-b

Lecture 74 chapter-4-visualizing-disaggregated-data-with-pivotcharts-c

Section 8: Deep Learning for Text with PyTorch

Lecture 75 chapter-1-introduction-to-deep-learning-for-text-with-pytorch-a

Lecture 76 chapter-1-introduction-to-deep-learning-for-text-with-pytorch-b

Lecture 77 chapter-1-introduction-to-deep-learning-for-text-with-pytorch-c

Lecture 78 chapter-2-text-classification-with-pytorch-a

Lecture 79 chapter-2-text-classification-with-pytorch-b

Lecture 80 chapter-2-text-classification-with-pytorch-c

Lecture 81 chapter-2-text-classification-with-pytorch-d

Lecture 82 chapter-3-text-generation-with-pytorch-a

Lecture 83 chapter-3-text-generation-with-pytorch-b

Lecture 84 chapter-3-text-generation-with-pytorch-c

Lecture 85 chapter-3-text-generation-with-pytorch-d

Lecture 86 chapter-4-advanced-topics-in-deep-learning-for-text-with-pytorch-a

Lecture 87 chapter-4-advanced-topics-in-deep-learning-for-text-with-pytorch-b

Lecture 88 chapter-4-advanced-topics-in-deep-learning-for-text-with-pytorch-c

Lecture 89 chapter-4-advanced-topics-in-deep-learning-for-text-with-pytorch-d

Lecture 90 chapter-4-advanced-topics-in-deep-learning-for-text-with-pytorch-e

Section 9: Dimensionality Reduction in R

Lecture 91 chapter-1-foundations-of-dimensionality-reduction-a

Lecture 92 chapter-1-foundations-of-dimensionality-reduction-b

Lecture 93 chapter-1-foundations-of-dimensionality-reduction-c

Lecture 94 chapter-2-feature-selection-for-feature-importance-a

Lecture 95 chapter-2-feature-selection-for-feature-importance-b

Lecture 96 chapter-2-feature-selection-for-feature-importance-c

Lecture 97 chapter-2-feature-selection-for-feature-importance-d

Lecture 98 chapter-3-feature-selection-for-model-performance-a

Lecture 99 chapter-3-feature-selection-for-model-performance-b

Lecture 100 chapter-3-feature-selection-for-model-performance-c

Lecture 101 chapter-3-feature-selection-for-model-performance-d

Lecture 102 chapter-4-feature-extraction-and-model-performance-a

Lecture 103 chapter-4-feature-extraction-and-model-performance-b

Lecture 104 chapter-4-feature-extraction-and-model-performance-c

Lecture 105 chapter-4-feature-extraction-and-model-performance-d

Lecture 106 chapter-4-feature-extraction-and-model-performance-e

Section 10: End-to-End Machine Learning

Lecture 107 chapter-1-design-and-exploration-a

Lecture 108 chapter-1-design-and-exploration-b

Lecture 109 chapter-1-design-and-exploration-c

Lecture 110 chapter-2-model-training-and-evaluation-a

Lecture 111 chapter-2-model-training-and-evaluation-b

Lecture 112 chapter-2-model-training-and-evaluation-c

Lecture 113 chapter-2-model-training-and-evaluation-d

Lecture 114 chapter-3-model-deployment-a

Lecture 115 chapter-3-model-deployment-b

Lecture 116 chapter-3-model-deployment-c

Lecture 117 chapter-3-model-deployment-d

Lecture 118 chapter-4-model-monitoring-a

Lecture 119 chapter-4-model-monitoring-b

Lecture 120 chapter-4-model-monitoring-c

Lecture 121 chapter-4-model-monitoring-d

Lecture 122 chapter-4-model-monitoring-e

Marketing Professionals,Product Managers,Data Scientists and AI Developers,Business Leaders,Business Executives,Technology Consultants,Business Analysts,Entry-level Data Scientists,Business Analysts,Financial Analysts,Data Analysts,Researchers,Natural Language Processing (NLP) Engineers,AI Researchers,Data Scientists,Statisticians,Data Engineers,Data Scientists