Postagens do Canal AI Prompts | ChatGPT | Google Gemini | Claude

ChatGPT Prompting Cheatsheet

𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍

1) Generative AI

2) Big data artificial intelligence

3 ) Microsoft Al for beginners

4) Prompt Engineering for Chat GPT

𝐋𝐢𝐧𝐤👇 :- 

https://pdlink.in/40Fbg9d

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5 Useful DeepSeek Al Prompts

1. Personalized learning plan

Prompt: "Create a detailed learning plan for me to become an expert in [Insert Subject or Skill] within [Desired Timeframe]. Include daily study schedules, recommended resources, and milestones."

2. Productivity optimization

Prompt: "Design a customized productivity system for me based on my typical day, including methods to manage my time, prioritize tasks, and reduce distractions while working on [Insert Goal or Specific Project]."

3. Skill development roadmap

Prompt: "Outline a step-by-step roadmap for me to develop proficiency in [Insert skill or subject]. Include beginner to advanced stages and practical exercises."

4. Creative problem-solving techniques

Prompt: "Develop a set of creative problem-solving techniques that I can use to tackle challenges in [Specific field or situation]."

5. Analytical skills enhancement

Prompt: "Suggest a series of activities, puzzles, and exercises that will help me improve my analytical skills,
particularly in [Specific area]."

10 DeepSeek Al Prompts:

1. Improve your writing

Prompt: [Paste your writing] "Please proofread the text above. Correct grammatical and spelling mistakes. and offer ideas that will make my writing more lucid."

2. Learn any new skill

Prompt: "I want to learn [insert desired skill]. I am a complete beginner. Create a 30 day learning plan that will help a beginner like me learn and improve this skill."

3. Solve complex problems

Prompt: "I am having difficulty learning [insert topic]. Help me understand it better by using First Principles Thinking."

4. Boost your memory

Prompt: "I am currently learning about [insert topic]. Convert the key lessons from this topic into engaging stories and metaphors to aid my memorization."

5. Achieve any goal

Prompt: "Use the SMART framework to achieve [insert goal]. Break it down into steps that are Specific, Measurable, Achievable, Relevant, and Time-bound."

6. Connect with others

Prompt: "Connect me with a community of learners and experts in [Insert topic]. How can I join a forum, social media group or other online community to share my knowledge and learn from others?"

7. Learn from mistakes

Prompt: "I made a mistake while practicing Insert skill]. Can you explain what went wrong and how I can avoid making the same mistake in the future?"

8. Train it to learn your writing

Prompt: "Analyze the text below for style, voice, and tone. Create a prompt to write a new paragraph in the same style, voice, and tone: [Insert your text]."

9. Personalized Learning Plan

Prompt: "Help me design a personalized learning plan for mastering [subject]. Break it down into daily learning tasks, recommended resources, and practical exercises I can do to build my skills."

10. 80/20 principle to learn faster

Prompt: "I want to learn about [Insert topic].
Determine and share the 20% of the topic's lessons that are most crucial to understanding the remaining 80%."

Data Scientist Roadmap
|
|-- 1. Basic Foundations
| |-- a. Mathematics
| | |-- i. Linear Algebra
| | |-- ii. Calculus
| | |-- iii. Probability
| | `-- iv. Statistics
| |
| |-- b. Programming
| | |-- i. Python
| | | |-- 1. Syntax and Basic Concepts
| | | |-- 2. Data Structures
| | | |-- 3. Control Structures
| | | |-- 4. Functions
| | | `-- 5. Object-Oriented Programming
| | |
| | `-- ii. R (optional, based on preference)
| |
| |-- c. Data Manipulation
| | |-- i. Numpy (Python)
| | |-- ii. Pandas (Python)
| | `-- iii. Dplyr (R)
| |
| `-- d. Data Visualization
| |-- i. Matplotlib (Python)
| |-- ii. Seaborn (Python)
| `-- iii. ggplot2 (R)
|
|-- 2. Data Exploration and Preprocessing
| |-- a. Exploratory Data Analysis (EDA)
| |-- b. Feature Engineering
| |-- c. Data Cleaning
| |-- d. Handling Missing Data
| `-- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
| |-- a. Supervised Learning
| | |-- i. Regression
| | | |-- 1. Linear Regression
| | | `-- 2. Polynomial Regression
| | |
| | `-- ii. Classification
| | |-- 1. Logistic Regression
| | |-- 2. k-Nearest Neighbors
| | |-- 3. Support Vector Machines
| | |-- 4. Decision Trees
| | `-- 5. Random Forest
| |
| |-- b. Unsupervised Learning
| | |-- i. Clustering
| | | |-- 1. K-means
| | | |-- 2. DBSCAN
| | | `-- 3. Hierarchical Clustering
| | |
| | `-- ii. Dimensionality Reduction
| | |-- 1. Principal Component Analysis (PCA)
| | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
| | `-- 3. Linear Discriminant Analysis (LDA)
| |
| |-- c. Reinforcement Learning
| |-- d. Model Evaluation and Validation
| | |-- i. Cross-validation
| | |-- ii. Hyperparameter Tuning
| | `-- iii. Model Selection
| |
| `-- e. ML Libraries and Frameworks
| |-- i. Scikit-learn (Python)
| |-- ii. TensorFlow (Python)
| |-- iii. Keras (Python)
| `-- iv. PyTorch (Python)
|
|-- 4. Deep Learning
| |-- a. Neural Networks
| | |-- i. Perceptron
| | `-- ii. Multi-Layer Perceptron
| |
| |-- b. Convolutional Neural Networks (CNNs)
| | |-- i. Image Classification
| | |-- ii. Object Detection
| | `-- iii. Image Segmentation
| |
| |-- c. Recurrent Neural Networks (RNNs)
| | |-- i. Sequence-to-Sequence Models
| | |-- ii. Text Classification
| | `-- iii. Sentiment Analysis
| |
| |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
| | |-- i. Time Series Forecasting
| | `-- ii. Language Modeling
| |
| `-- e. Generative Adversarial Networks (GANs)
| |-- i. Image Synthesis
| |-- ii. Style Transfer
| `-- iii. Data Augmentation
|
|-- 5. Big Data Technologies
| |-- a. Hadoop
| | |-- i. HDFS
| | `-- ii. MapReduce
| |
| |-- b. Spark
| | |-- i. RDDs
| | |-- ii. DataFrames
| | `-- iii. MLlib
| |
| `-- c. NoSQL Databases
| |-- i. MongoDB
| |-- ii. Cassandra
| |-- iii. HBase
| `-- iv. Couchbase
|
|-- 6. Data Visualization and Reporting
| |-- a. Dashboarding Tools
| | |-- i. Tableau
| | |-- ii. Power BI
| | |-- iii. Dash (Python)
| | `-- iv. Shiny (R)
| |
| |-- b. Storytelling with Data
| `-- c. Effective Communication
|
|-- 7. Domain Knowledge and Soft Skills
| |-- a. Industry-specific Knowledge
| |-- b. Problem-solving
| |-- c. Communication Skills
| |-- d. Time Management
| `-- e. Teamwork
|
`-- 8. Staying Updated and Continuous Learning
|-- a. Online Courses
|-- b. Books and Research Papers
|-- c. Blogs and Podcasts
|-- d. Conferences and Workshops
`-- e. Networking and Community Engagement

𝗚𝗲𝘁 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 𝗜𝗻 𝗔𝗺𝗮𝘇𝗼𝗻, 𝗚𝗼𝗼𝗴𝗹𝗲, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗡𝗩𝗜𝗗𝗜𝗔, 𝗮𝗻𝗱 𝗠𝗲𝘁𝗮 (𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸) 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲𝘀𝗲 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀😍

1️⃣ Amazon Interviewing Guide
2️⃣ Google Interview Tips
3️⃣ Microsoft Hiring Tips
4️⃣ NVIDIA Hiring Process
5️⃣ Meta Onsite SWE Prep Guide

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/40OSJJ6

Crack Interview & Get Your Dream Job In Top MNCs

You don't need to spend several $𝟭𝟬𝟬𝟬𝘀 to learn Data Science.❌

Stanford University, Harvard University & Massachusetts Institute of Technology is providing free courses.💥

Here's 8 free Courses that'll teach you better than the paid ones:


1. CS50’s Introduction to Artificial Intelligence with Python (Harvard)

https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python

2. Data Science: Machine Learning (Harvard)

https://pll.harvard.edu/course/data-science-machine-learning

3. Artificial Intelligence (MIT)

https://lnkd.in/dG5BCPen

4. Introduction to Computational Thinking and Data Science (MIT)

https://lnkd.in/ddm5Ckk9

5. Machine Learning (MIT)

https://lnkd.in/dJEjStCw

6. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT)

https://lnkd.in/dkpyt6qr

7. Statistical Learning (Stanford)

https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning

8. Mining Massive Data Sets (Stanford)

📍https://online.stanford.edu/courses/soe-ycs0007-mining-massive-data-sets

ENJOY LEARNING

𝟱 𝗙𝗥𝗘𝗘 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Ready to dive into the world of Machine Learning? Here are 5 powerful resources that will guide you every step of the way—from beginner concepts to advanced techniques.

𝐋𝐢𝐧𝐤 👇:- 

https://pdlink.in/40wyXk8

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How to be a Prompt Engineer 101

The shortest and most comprehensive guide

1. start with an explanation

Make a description and character situation at the beginning of the Prompt

Error example:

Please help me read the following code:

{your input here}

Correct example:

Now let's play the role, you are a senior information security engineer, I will give you a piece of code, please help me read the code and point out where there may be security vulnerable.



Text: """

{your input here}

"""

2. Prompt to describe the situation

In the prompt, it is necessary to describe the context, result, length, format and style as much as possible

Error example:

Write a short story for kids

Correct example:

Write a funny soccer story for kids that teaches the kid that persistence is the key for success in the style of Rowling.

3. gives output in the format

If you are doing data analysis, please give the input template of the format

Error example:

Extract house pricing data from the following text.

Text: """

{your text containing pricing data}

"""

Correct example:

Extract house pricing data from the following text.

Desired format: """

House 1 | $1,000,000 | 100 sqm

House 2 | $500,000 | 90 sqm

... (and so on)

"""

Text: """

{your text containing pricing data}

"""

4. Add some example questions and answers

Sometimes adding some question and answer examples can make GPT more intelligent

Correct example:

Extract brand names from the texts below.

Text 1: Finxter and YouTube are tech companies. Google is too.

Brand names 2: Finxter, YouTube, Google

###

Text 2: If you like tech, you'll love Finxter!

Brand names 2: Finxter

###

Text 3: {your text here}

Brand names 3:

The question and answer example is also a standard template example in fine-tune


5. Simplify the sentence and clarify the purpose


Keep your words as short as possible and don't say useless content


Error example:

ChatGPT, write a sales page for my company selling sand in the desert, please write only a few sentences, nothing long and complex

Correct example:

Write a 5-sentence sales page, sell sand in the desert.

6. Good at using introductory words

Error example:

Write a Python function that plots my net worth over 10 years for different inputs on the initial investment and a given ROI

Correct example:

# Python function that plots net worth over 10

# years for different inputs on the initial

# investment and a given ROI


import matplotlib


def plot_net_worth(initial, roi):

AI as a life saver:
1. ChatGPT - thesis, essay, writing
2. Scite and perplexity - literature review
3. Consesus - latest research paper
4. Gemini - coding and technical
5. Claude AI - Analysis data, comparison data, literature review