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Reading list

6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Don’t Miss this Anthropic’s Prompt Engineering Course in 2024

Pankaj Singh
Pankaj Singh 23 Aug, 2024
6 min read

Introduction

Prompt Engineering has been a hot topic in 2024, with the rapid advancement of Generative AI driving learners to skill up in this competitive field. Mastering prompt engineering is like having the keys to a powerful machine that can transform ideas into reality. While many prompt engineering courses are available, they often lack practicality and good use cases. Anthropic’s Prompt Engineering Interactive Tutorial stands out by offering a free, hands-on learning experience. This course teaches you how to shape and refine your interactions with Claude, one of the most sophisticated AI models (for FREE)!

Anthropic's Prompt Engineering Course

Overview

  • Prompt Engineering is now a critical skill for excelling in AI-driven fields.
  • Anthropic’s Prompt Engineering Interactive Tutorial offers hands-on learning to master AI prompts.
  • Effective prompt engineering enhances AI outputs, saving time and improving productivity.
  • The course covers everything from basic to advanced prompt engineering techniques using Claude.
  • This course is tailored to various industries and is available on Google Sheets for interactive learning.

Table of contents

Why you should take a Prompt Engineering Course?

Whether you’re a budding data scientist, an AI developer, or someone interested in leveraging AI tools for business or research, mastering prompt engineering gives you the full potential to explore AI models, particularly Generative ones. If you’re encountering prompt engineering for the first time, you might wonder why you should invest time in practicing this skill. The answer is simple: effective prompts lead to better AI outputs, enabling you to create more accurate, relevant, and impactful results.

Prompt engineering allows you to communicate more effectively with AI models, making them produce outputs that align closely with your goals. By honing this skill, you can save time, reduce errors, and improve productivity, whether you’re automating tasks, generating creative content, or conducting research. As AI continues to evolve, those with strong, prompt engineering skills will be better equipped to harness the power of these technologies and stay ahead in a rapidly changing landscape.

Anthropic Prompt Engineering Course Goals

By the end of this course, you won’t just understand prompt engineering; you’ll excel at it. You’ll learn:

  • Mastering Prompt Structure: Understand the building blocks of a great prompt.
  • Identifying and Overcoming Pitfalls: Learn the 80/20 techniques to tackle common prompt failures.
  • Leveraging Claude’s Strengths: Know when to use Claude for optimal results and avoid its limitations.
  • Crafting Effective Prompts for Various Scenarios: Develop prompts from scratch tailored to your specific needs.

Claude Model: Claude-3-haiku-20240307

This isn’t just a course; it’s a journey into the heart of AI interaction, designed to be as interactive and hands-on as possible.

Anthropic Prompt Engineering Course Structure

Here’s the course link: Anthropic Prompt Engineering Course Structure

The course is divided into nine chapters, each packed with lessons and exercises that will progressively build your skills. Here’s what you can expect:

  1. Beginner Level:
    • Chapter 1: Basic Prompt Structure
    • Chapter 2: Being Clear and Direct
    • Chapter 3: Assigning Roles
  2. Intermediate Level:
    • Chapter 4: Separating Data from Instructions
    • Chapter 5: Formatting Output & Speaking for Claude
    • Chapter 6: Precognition (Thinking Step by Step)
    • Chapter 7: Using Examples
  3. Advanced Level:
    • Chapter 8: Avoiding Hallucinations
    • Chapter 9: Building Complex Prompts (Industry Use Cases)

Each chapter not only teaches you theory but also immerses you in practice. The “Example Playground” at the end of each lesson lets you experiment with prompt variations, helping you see firsthand how different inputs yield different outputs.

Anthropic Prompt Engineering Course: Why it Stands Out?

They have designed this course with your learning journey in mind. Here’s what makes it unique:

  • Interactive Learning: Every lesson comes with exercises that challenge you to think, try, and iterate.
  • Tailored to Your Needs: Whether you’re focused on legal services, financial operations, or coding, our advanced sections provide industry-specific exercises.
  • Advanced Techniques: For those looking to go beyond the basics, our appendix offers advanced methods like chaining prompts, integrating tools, and employing search and retrieval strategies.

This tutorial is also available on Google Sheets via Anthropic’s Claude for Sheets extension, offering an even more user-friendly interface. We recommend trying both versions to see which suits your style best.

Tutorial How to Use this on Google Sheets?

Firstly, you need an API key to interact with this tutorial. If you don’t have one, you can sign up for it through the Anthropic Console or use the static tutorial answer key.

Getting Started:

  1. Make a Copy:
    Save a copy of this sheet to your Google Drive.
  2. Install Claude for Sheets Extension:
    a. Visit the Claude for Sheets extension page.
    b. Click the blue install button.
    (If you already have it installed, skip to step 3.)
  3. Enable the Extension for This Document:
    a. In the toolbar, go to Extensions > Add-ons > Manage add-ons.
    b. Click the three-dot menu in the top right corner of the Claude for Sheets extension.
    c. Ensure “Use in this document” is checked.
  4. Add Your API Key:
    a. Obtain your unique Anthropic API key by following the provided instructions.
    b. Enter your API key by going to Extensions > Claude for Sheets™ > Enter your Anthropic API Key.

Here are articles on Prompt Engineering that can help you in your studies:

ArticleSource
Implementing the Tree of Thoughts Method in AILink
What are Delimiters in Prompt Engineering?Link
What is Self-Consistency in Prompt Engineering?Link
What is Temperature in Prompt Engineering?Link
Chain of Verification: Prompt Engineering for Unparalleled AccuracyLink
Mastering the Chain of Dictionary Technique in Prompt EngineeringLink
What is the Chain of Symbol in Prompt Engineering?Link
What is the Chain of Emotion in Prompt Engineering?Link

Important Things to Consider

This course uses Claude 3 Haiku with a temperature setting of 0, resulting in faster and more consistent outputs. These prompt engineering techniques also apply to other Claude models.

To add a new line in a cell:

  • Mac: Cmd + Enter
  • Windows: Alt + Enter

Navigating Pages:

Click on the gray links at each page’s bottom or the top right corner.

Use the blue-linked pop-up for navigation.

When you reach the correct answer, Claude’s response cell will turn green.

Conclusion

As the world of AI continues to evolve at a breakneck pace, the importance of mastering prompt engineering cannot be overstated. This skill is becoming a cornerstone for anyone aiming to excel in AI-driven fields, enabling you to communicate more effectively with AI models and achieve better, more precise outputs. The Anthropic Prompt Engineering Course offers a unique opportunity to gain hands-on experience and deepen your understanding of this critical skill. Whether you’re a novice or an experienced AI user, this course equips you with the knowledge and tools needed to harness AI’s full potential, making you a valuable asset in an increasingly AI-centric world. Don’t just adapt to the future—shape it with the power of prompt engineering.

Also, if you want to excel in prompt engineering, then explore – the GenAI Pinnacle Program today! Here’s the roadmap:

Are you ready to transform the way you interact with AI? Let’s get started!

Frequently Asked Questions

Q1. What is Prompt Engineering?

Ans. Prompt Engineering is designing and refining prompts to guide AI models, like GPT or Claude, in generating the desired output.

Q2. Who should take a Prompt Engineering course?

Ans. This course is ideal for AI enthusiasts, developers, data scientists, and anyone interested in more effectively using AI models.

Q3. What will I learn in a Prompt Engineering course?

Ans. You’ll learn how to craft effective prompts, understand AI model behavior, troubleshoot prompt issues, and optimize outputs for various applications.

Q4. Do I need prior experience in AI to take this course?

Ans. While some courses may require basic AI knowledge, many are designed for beginners and will cover essential concepts from scratch.

Q5. How can Prompt Engineering skills benefit my career?

Ans. Mastering Prompt Engineering can make you more effective in roles involving AI, enhance your problem-solving abilities, and open up new opportunities in the rapidly growing AI field.

Anthropicanthropic courseAnthropic course on Prompt engineeringClaude 3courseHaikuPrompt Engineeringtutorial
Pankaj Singh
Pankaj Singh 23 Aug, 2024

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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