Stealth Startup

Founder

Data and AI Central

Connecting professionals and enthusiasts, Data and AI Central is your go-to hub for bridging theory to practice, delivering industry insights, actionable trends, and fostering collaboration within the data and AI community.

Horizon AI

Join our forward-thinking community for daily 3-minute briefings on the latest in AI news, tools and tutorials.

Factored

Factored (backed by Andrew Ng's AI Fund and deeplearning.ai) helps leading tech companies select, upskill, and build world-class data science, machine learning and AI engineering teams much faster and more cost effectively. Our engineers have been personally vetted, educated, and mentored by some of the most talented and recognized AI educators and engineers from Silicon Valley, Stanford University and deeplearning.ai.

Data Analyst

Rappi

BI/Data Engineer

Indra

Junior Systems Engineer

Global Business Services Intern

MedaBots SAC

IT Project Intern

Data Analyst | Data Engineer | AI | GenAI | Mentor | Content Creator 👩🏻‍💻 | Google WTM Ambassador | Python | Tableau | Snowflake | Power BI | Business Intelligence

Bachelor in Systems Engineering and Information Technology from ESAN University. I hold a Specialization Program in Business Intelligence Analytics and Big Data from the Universidad Agraria La Molina. I have worked as a Data Engineer and Data Analyst in data-related projects in tech companies such as IBM, Indra, and Rappi. Currently, I work as a Data Analyst at Factored, a company founded by Andrew Ng and specialized in data science, data engineering, machine learning, and data analysis.

I have been recognized as IBM Champion 2022 and Google Women Techmakers Ambassador, I have also been a Mentor and Regional Ambassador at the Hackmakers World Innovation Day Hack 2022 International Hackathon organized by Hackmakers, where I mentored the winning team.

I have published two research papers, one of them in Springer, and I have been a speaker at the WorldCIST'20 International Conference. In addition, I have participated as a speaker in webinars held by IBM Champions and data and tech communities. 

👩🏻‍💻 I enjoy creating content about data-related topics and insights and sharing them on Linkedin and Twitter.

There are only 1.5 weeks left in 2024. If you haven't learned about Generative AI yet, here's a roadmap from MIT to get you started 👇

📚 MIT Introductory Course on Foundation Models & Generative AI
Link to the free course: https://lnkd.in/eZaRw9Vc

It covers:
↳ ChatGPT
↳ Stable-Diffusion & Dall-E
↳ Neural Networks
↳ Supervised Learning
↳ Representation & Unsupervised Learning
↳ Reinforcement Learning
↳ Generative AI
↳ Self-Supervised Learning
↳ Foundation Models
↳ GANs (adversarial)
↳ Contrastive Learning
↳ Auto-encoders
↳ Denoising & Diffusion

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#python #data #ai #machinelearning #programming

Gina Acosta Gutiérrez

Daily Posts and Resources on Data Science, Data Engineering, and AI 📚 | Mentor | Google WTM Ambassador

From Excel demos in '93 to leading a $2.73 trillion tech titan. Satya Nadella's journey at Microsoft shows us anything's possible.

In 22 years, he went from middle manager to the top, growing Microsoft's stock by nearly 1,000%!

Under his strategic vision, he made Google take notice and form pivotal partnerships, especially with OpenAI, propelling Microsoft to the forefront of AI innovation 🤖

Now worth over $930M, Nadella's story is a masterclass in turning dreams into reality.

👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#data #ai #artificialintelligence #technology #innovation #microsoft

FREE AI/Machine Learning eBooks 📚

I've compiled these 4 free AI and machine learning eBooks, covering everything from basic statistics to deep learning, with plenty of hands-on examples.

📌 An Introduction to Statistical Learning
https://lnkd.in/ejuHcuC5

This book provides a broad and less technical treatment of key topics in statistical learning. There are 2 editions available, each edition includes practical labs in R or Python to illustrate concepts. This book is appropriate for anyone who wishes to use contemporary tools for data analysis. 


📌 Python Data Science Handbook
https://lnkd.in/eVX8JPGU

You’ll learn how to use the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. This resource is perfect for tackling day-to-day issues such as cleaning, manipulating, and transforming data — or building machine learning models.


📌 Dive into Deep Learning
https://d2l.ai/

Interactive deep learning book with code, math, and discussions. Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow. Adopted at 400 universities from 60 countries.


📌 Approaching (Almost) Any Machine Learning Problem
https://lnkd.in/efPC8xdH

This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is for you if you are looking for guidance on approaching machine learning problems.

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#python #data #ai #machinelearning #programming

Fascinating Visualization on How ChatGPT Builds Sentences in Real-Time 👇

Data scientist Santiago Ortiz mapped ChatGPT's thinking by running the prompt "𝘐𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦 𝘪𝘴" hundreds of times with a high temperature setting of 1.6 for varied responses. Then he used Principal Component Analysis (PCA) to compress 1536-dimensional word embeddings into a 3D visualization, tracking how the AI builds responses word by word.

The project displays two linked visualizations: 
↳ A 3D cube showing bifurcating paths 
↳ And a tree diagram revealing word probabilities. 

Each point represents a text sequence, while hovering highlights specific completion paths, making AI decision-making patterns visible and understandable.

Link to the project: https://moebio.com/mind

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#python #data #ai #machinelearning #programming

All the FREE Harvard University Data Science & ML Lectures 👇

Hands-on projects and theory behind algorithms, classification, optimization, reinforcement learning, and other topics in AI and ML.

1. Introduction to Artificial Intelligence with Python - https://lnkd.in/eg2g7aCp

2. Introduction to Programming with Python - https://lnkd.in/eKFAH5iA

3. Introduction to Databases with SQL - https://lnkd.in/eBCu3YQy

4. Introduction to Probability - https://lnkd.in/e3FShyXY

5. Data Science: Wrangling - https://lnkd.in/eFfGKyYB

6. Machine Learning and AI with Python - https://lnkd.in/ec-e8z4Y

7. Data Science: Machine Learning- https://lnkd.in/ea2JJ_XY

8. Data Science: Inference and Modeling - https://lnkd.in/ez_q5ADp

9. Algorithms for Big Data - https://lnkd.in/eV3qKQ8E

10. Advanced Algorithms - https://lnkd.in/eiCQeKYr

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#python #data #ai #machinelearning #programming

A fantastic visual demonstration of CNNs (Convolutional Neural Networks) and how they work! 🧠

This video shows that making an image smaller doesn't always mean losing important details. It's impressive that even though the image is much smaller, we can still clearly see it shows a dog.

CNNs can focus on the key features of an image, reducing its size without losing important information. By using pooling, they make image recognition more efficient. They are great at finding patterns even in smaller images, which makes the process simpler and faster.


👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#python #data #ai #machinelearning #programming

All the FREE MIT Data Science & ML Courses 👇

Learn about Algorithms, Statistics, Machine Learning models, NLP, and more ...

1. Introduction to Algorithms - https://lnkd.in/eb3vzbSQ

2. Design and Analysis of Algorithms - https://lnkd.in/eBQ2PEYQ

3. Mathematics of Machine Learning - https://lnkd.in/eYbyS_T2

4. Mathematics of Big Data and Machine Learning - https://lnkd.in/eNBk4BGg

5. Statistics and Visualization for Data Analysis and Inference - https://lnkd.in/eky3EWMH

6. Statistical Thinking and Data Analysis - https://lnkd.in/eDgrcq4s

7. Statistics for Applications - https://lnkd.in/ePpA7rPb

8. How to Process, Analyze and Visualize Data - https://lnkd.in/e8JNVx8U

9. Data Mining - https://lnkd.in/et58aqgj

10. Data, Models, and Decisions - https://lnkd.in/eKkaXwR4

11. Prediction: Machine Learning and Statistics - https://lnkd.in/ew5_uiUK

12. Machine Learning - https://lnkd.in/e2gZdPqq

13. Machine Learning for Healthcare - https://lnkd.in/eVwAiwAV

14. Natural Language and the Computer Representation of Knowledge - https://lnkd.in/e_PhNTAe

15. Advanced Natural Language Processing - https://lnkd.in/e3FxEXGu

16. Deep Learning - https://lnkd.in/eZ_Sy94U

17. Communicating With Data - https://lnkd.in/ewWKEfWg

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#python #data #ai #machinelearning #programming

FREE Course: Applied Machine Learning - Cornell CS 5787 👇

Starting from the very basics, it covers all of the most important ML algorithms (linear regression, kernel methods, neural networks, etc.), their mathematical foundations and how to apply them in practice.

The course includes:
• 23 lectures with detailed course notes
• 30+ hours of lecture videos
• 20+ implementations of ML algorithms in Python

All the materials for this course are executable: the slides are Jupyter notebooks with programmatically generated figures. Readers can tweak parameters and regenerate the figures themselves.

Course Website & Materials: https://lnkd.in/eq5iVTuZ 

Lecture Notes: https://lnkd.in/ehqFWzAn 

It also includes three full lectures on how to apply ML in practice in a principled way. This includes topics such as how to prioritize model improvements, diagnose overfitting, perform error analysis, visualize loss curves, etc.

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#python #data #ai #machinelearning #programming

When you join a startup ...


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#python #softwareengineering #machinelearning #programming

When you are new to a legacy project ...


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#python #softwareengineering #machinelearning #programming

When you are having API integration issues... 😂


👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#data #programming #coding #tech

All the FREE IBM Data Science Courses + Certificates 👇

Learn about Data Analysis, Python, Data Visualization, Deep Learning, and more ...

1. Data Fundamentals - https://lnkd.in/esvN7_5G

2. Data Science Fundamentals - https://lnkd.in/eVXjXYts

3. Applied Data Science with Python - https://lnkd.in/ewPsUzzT

4. Data Analysis with Python - https://lnkd.in/edAPtcdE

5. Data Visualization with Python - https://lnkd.in/eXJAr3Nu

6. Applied Data Science with R - https://lnkd.in/e-kamgZV

7. Data Visualization with R - https://lnkd.in/ebFVM-gU

8. Big Data Foundations - https://lnkd.in/eveT4vMz

9. Scala Programming for Data Science - https://lnkd.in/ewCZemdA

10. Data Science for Business - https://lnkd.in/e97RQg-i

11. Deep Learning Fundamentals - https://lnkd.in/epZsXtnw

12. Deep Learning with TensorFlow - https://lnkd.in/eqEqUn-k

👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#python #data #ai #machinelearning #programming

This sweater acts as an invisibility cloak against AI 🤯

Developed by a team at the University of Maryland, it features "adversarial patterns" that confuse most common object detectors. This means AI systems can't spot the person wearing it.

The researchers, collaborating with Facebook AI, initially set out to explore weaknesses in machine learning models. The outcome is a vibrant pattern on clothing that AI cameras fail to detect, essentially making the wearer invisible to machines.


👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#innovation #learning #ai #artificialintelligence #tech #technology

90% of coding is ...


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#python #softwareengineering #machinelearning #programming

Machine learning is a big part of how we use data today. 

Here's an infographic on key algorithms used in predictive modeling. Let's break them down into simple terms:

▪ 𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻
 - Finds a straight line that best fits the data points.
 - Easy to explain and works well when data can be separated by a line.
 - Assumes a straight relationship between variables, which isn’t always the case.

▪ 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻
 - Used for binary classification, helping decide between two options.
 - Simple to understand and works well with data that can be split into two clear groups.
 - Struggles with complex, non-linear problems.

▪ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲𝘀
 - Like flowcharts, they split data into branches to make decisions.
 - Handle both numbers and categories well and are easy to visualize.
 - Can overfit, meaning they might work too well on training data but not on new data.

▪ 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁
 - A group of decision trees working together.
 - Handles large datasets and reduces overfitting by averaging results.
 - Can be biased towards certain features and requires more resources than simpler models.

▪ 𝗚𝗿𝗮𝗱𝗶𝗲𝗻𝘁 𝗕𝗼𝗼𝘀𝘁𝗶𝗻𝗴
 - Builds trees one after another, correcting errors along the way.
 - Accurate and works well with both numbers and categories.
 - Can overfit if not tuned properly and is slow to train.

▪ 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀
 - Inspired by the human brain, using layers of nodes to learn from data.
 - Great for large datasets and complex data types like images and text.
 - Need a lot of data and computing power, and can overfit if not managed well.

Each algorithm has its strengths and weaknesses. Choosing the right one depends on the problem you’re trying to solve and the data you have.

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#python #data #ai #machinelearning #programming

When you are offered to work on the legacy project ...


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#python #data #ai #machinelearning #programming

Data is everywhere, and businesses need skilled analysts to make sense of it all. 

Here's a guide to becoming a Data Analyst, save it for future reference

1. Mathematics and Statistics
Descriptive & Inferential Statistics
Probability Theory & Hypothesis Testing
Linear Algebra & Calculus

2. Python
Basics: Syntax, Data Types, Control Structures
Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Plotly, TensorFlow, PyTorch
Data Visualization & Analysis

3. SQL
Basics: SELECT, INSERT, UPDATE, DELETE
Joins, Subqueries, Window Functions
Indexing, Optimization, Database Management

4. Data Wrangling
Data Cleaning & Transformation
Handling Missing Values & Normalization
Data Merging & Joining

5. Data Visualization
Python Libraries: Matplotlib, Seaborn, Plotly, Bokeh
Dashboard Tools: Tableau, Power BI, Looker

6. Machine Learning
Supervised Learning: Linear & Logistic Regression, Decision Trees, Random Forests
Unsupervised Learning: K-Means, Hierarchical Clustering
Model Evaluation: Cross-Validation, Confusion Matrix, ROC Curve

7. Soft Skills
Communication & Problem-Solving
Critical Thinking & Teamwork
Storytelling with Data & Presentation Skills
Adaptability


👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#python #analytics #machinelearning #programming

Public Datasets for Data Processing Projects 👇

You can use these datasets to perform Data Cleaning, Exploratory Data Analysis (EDA), Forecasting, create Visualizations/Dashboard, identify insights, etc, and add these projects to your portfolio 🤩

• 𝗔𝗶𝗿𝗯𝗻𝗯 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮 🏠
https://lnkd.in/dHuUizPx
This dataset describes the listing activity of homestays in New York City

• 𝗖𝗿𝗲𝗱𝗶𝘁 𝗖𝗮𝗿𝗱 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 💳
https://lnkd.in/eFTsZDCW
Anonymized credit card transactions labeled as fraudulent or genuine.

• 𝗧𝗼𝗽 𝗦𝗽𝗼𝘁𝗶𝗳𝘆 𝘀𝗼𝗻𝗴𝘀 𝗳𝗿𝗼𝗺 𝟮𝟬𝟭𝟬-𝟮𝟬𝟭𝟵 🎵
https://lnkd.in/deXZZtmz

• 𝗪𝗮𝗹𝗺𝗮𝗿𝘁 𝗦𝘁𝗼𝗿𝗲 𝗦𝗮𝗹𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 📈
https://lnkd.in/eVT6h-CT
Use historical markdown data to predict store sales

• 𝗡𝗲𝘁𝗳𝗹𝗶𝘅 𝗠𝗼𝘃𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗩 𝗦𝗵𝗼𝘄𝘀 📺
https://lnkd.in/eZ3cduwK
Listings of movies and tv shows on Netflix - Regularly Updated

• 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗷𝗼𝗯𝘀 𝗹𝗶𝘀𝘁𝗶𝗻𝗴𝘀 💼
https://lnkd.in/ezqxcmrE
More than 8400 rows of data analyst jobs from USA, Canada and Africa.

• 𝗧𝗼𝗽 𝟱𝟬 𝗙𝗮𝘀𝘁-𝗙𝗼𝗼𝗱 𝗖𝗵𝗮𝗶𝗻𝘀 𝗶𝗻 𝗨𝗦𝗔 🍗
https://lnkd.in/esBjf5u4
The key features of this Dataset are - Fast-Food Chains, U.S. Systemwide Sales (Millions - U.S Dollars), Average Sales per Unit (Thousands - U.S Dollars), Franchised Stores, Company Stores, 2021 Total Units, Total Change in Units from 2020.

• 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗕𝗼𝗼𝗸 𝗦𝗮𝗹𝗲𝘀 📚
https://lnkd.in/eXHN2XsQ
Forecast sales of 8 book titles in 2418 locations

• 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 📊
https://lnkd.in/dCUxP55N
A Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments.

• 𝗥𝗲𝗮𝗹 / 𝗙𝗮𝗸𝗲 𝗝𝗼𝗯 𝗣𝗼𝘀𝘁𝗶𝗻𝗴 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 🔎
https://lnkd.in/e5SDDW9G
This dataset contains 18K job descriptions out of which about 800 are fake. The data consists of both textual information and meta-information about the jobs. The dataset can be used to create classification models which can learn the job descriptions which are fraudulent.

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#python #data #ai #machinelearning #programming

Wait for it 😂


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#softwareengineering #data #ai #programming #coding

Roadmap to Learn and Master SQL with FREE Resources 👇

I've compiled this handy roadmap to guide you on your journey from beginner to advanced SQL user, packed with my top recommendations for free online courses, tutorials, practice resources, and more.

1. Intro to SQL - Kaggle
https://lnkd.in/ebTWM7FS

2. Learn SQL - CodeAcademy
https://lnkd.in/eaABTh5C

3. Intermediate SQL Tutorials - Alex The Analyst
https://lnkd.in/enKMd7Mz

4. SQL for Data Analysis - Udacity
https://lnkd.in/eSifcBY4

5. SQL For Data Analysis: Music Store Data Analysis Project using SQL
https://lnkd.in/ea26PRJ5

6. Cracking The SQL Interview - Sajjad Salaria (xoraus - Github)
https://lnkd.in/eWAEz7fz

7. Solving Complex SQL Query from HackerRank - TechTFQ
https://lnkd.in/esKnC-jq

8. Practice Writing SQL Queries using Real Dataset
https://lnkd.in/eD7UASAz

👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck

#python #sql #ai #programming

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