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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.

You don’t need to know every Linux command, but mastering the basics will get you out of almost any situation. I've created this cheatsheet with essential Linux commands for daily use 👇

Here's what it covers:

𝗙𝗶𝗹𝗲 𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗼𝗻:

• 'ls' and 'cd' help you move around folders
• 'pwd' shows your current location
• 'mkdir' and 'rmdir' create and remove directories

𝗙𝗶𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:

• 'cp' copies files from one place to another
• 'mv' moves or renames your files
• 'rm' removes files when you need to clean up
• 'touch' creates empty files quickly

𝗧𝗲𝘅𝘁 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴:

• 'cat' lets you view and combine files
• 'head' and 'tail' show file beginnings and endings
• 'grep' finds specific text in files

𝗦𝘆𝘀𝘁𝗲𝗺 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁:

• 'chmod' and 'chown' control file access
• 'ps' and 'top' monitor running programs
• 'sudo' gives you admin rights when needed

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Gina Acosta Gutiérrez

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

All the FREE IBM Data Science Courses + Certificates 👇

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

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

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

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

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

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All the free Kaggle Data Science courses + Certificates 👇

Learn about Feature Engineering, Data Cleaning, GenAI, Time Series, and more ...

1. 5-Day Gen AI Intensive Course - https://lnkd.in/dGfBNpWt

2. Python - https://lnkd.in/eMZAhANT

3. Intro to Machine Learning - https://lnkd.in/ezYxipjp

4. Pandas - https://lnkd.in/eC6VPUzM

5. Intermediate Machine Learning - https://lnkd.in/exY_DUHN

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9. Advanced SQL - https://lnkd.in/eb8qyFmB

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15. Machine Learning Explainability - https://lnkd.in/e4Yhh3mh

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Machine Learning and Deep Learning: Stanford's CS229 Course Notes 👇

The "CS229 Lecture Notes" by Andrew Ng and Tengyu Ma is an essential resource for anyone interested in machine learning, covering everything from the basics of supervised learning to the complexities of reinforcement learning.

This guide covers the following topics:

𝟭. 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: This section explains ways to teach computers using labeled data. It includes topics like linear regression, logistic regression, and support vector machines.

𝟮. 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Here, the authors explain how neural networks work and how we can use them to solve complex problems.

𝟯. 𝗚𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗥𝗲𝗴𝘂𝗹𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻: This section is all about making sure our machine learning models work well on new data, not just the data we used to train them.

𝟰. 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Covers methods for finding patterns in data without labels, like clustering and dimensionality reduction.

𝟱. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: The last part explores how we can teach computers to make decisions and control systems.


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CS229 Lecture Notes by Andrew Ng

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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Just keep coding ...


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How Convolutional Neural Networks Work👇


A Convolutional Neural Network (CNN) works by using layers to process images:
• Convolutional layers scan the input image using filters to detect features like edges, textures, and patterns. 
• Next, Pooling layers reduce the spatial dimensions while preserving important information.
• Then, fully connected layers take these extracted features and make final classifications by weighting connections between all neurons.

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All the Python NumPy commands you need for Data Science and ML 👇

NumPy is a must-know library for anyone working in data science or machine learning. I’ve created this cheat sheet to simplify the most important NumPy commands for you:

𝗔𝗿𝗿𝗮𝘆 𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻 & 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝘀
• np.array(): Create arrays from lists or tuples.
• np.zeros(), np.ones(): Generate arrays filled with zeros or ones.
• np.arange(): Create sequences with a defined step.
• np.shape(), np.dtype(): Check array dimensions and data types.

𝗔𝗿𝗿𝗮𝘆 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 & 𝗥𝗲𝘀𝗵𝗮𝗽𝗶𝗻𝗴
• np.reshape(): Change the shape of an array.
• np.concatenate(), np.vstack(), np.hstack(): Combine arrays in different ways.
• np.split(), np.transpose(), np.resize(): Split, transpose, or resize arrays.

𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
• Functions like np.sum(), np.mean(), np.median(), np.std(), and np.var() make calculations easy.
• Use np.cov() and np.corrcoef() for covariance and correlation.
• Generate random values with np.random.rand() or np.random.randn().

𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 & 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴
• Extract or filter data with np.extract() and np.where().
• Sort arrays using np.sort() or find unique values with np.unique().

𝗙𝗶𝗹𝗲 𝗜/𝗢
• Save and load arrays with np .save() and np.load().

Save this guide for reference! I'd love to hear your thoughts 💬 What other Python cheatsheets would be useful to you?

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90% of EDA Visualization Tasks Made Easy with These Essential Python Commands 👇

Every data science project starts with understanding your data. This cheatsheet breaks down the most useful Python visualization commands for exploratory data analysis (EDA), making it easier to spot patterns and trends.

• 𝗦𝗶𝗻𝗴𝗹𝗲 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀

Using histograms and box plots to understand how individual variables behave, perfect for checking data distributions and finding outliers.

• 𝗧𝘄𝗼-𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀

Scatter plots and bar charts help show relationships between pairs of variables, making it simple to spot correlations and patterns.

• 𝗠𝘂𝗹𝘁𝗶-𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀

Correlation heatmaps and pair plots give a big-picture view of how all variables work together, essential for complex datasets.

• 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀

Special plots for time-based data help track changes and trends over different periods, including moving averages and seasonal patterns.

Save this guide for reference! I'd love to hear your thoughts 💬 What other Python cheatsheets would be useful to you?

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4 Ways Algorithms Learn From Data — Pick the Right One for Your Project 👇


𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
• Works with labeled data
• Target variables: continuous (regression) or categorical (classification)
• Applications: housing price prediction, medical imaging

𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
• Works with unlabeled data
• No target variables available
• Techniques: clustering, association
• Applications: customer segmentation, market basket analysis

𝗦𝗲𝗺𝗶-𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
• Combines labeled and unlabeled data
• Handles categorical variables and clustering
• Applications: text classification

𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
• Based on reward systems
• Can work with or without specific target variables
• Applications: GPS lane detection, marketing optimization, autonomous vehicles

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Today, let's break down these nine types of databases and see how each can be a game-changer in different scenarios ↓

1. 𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Handle spatial data, perfect for applications involving maps, GIS, and location-based services. Ideal for navigating a world of coordinates!

2. 𝗜𝗻-𝗠𝗲𝗺𝗼𝗿𝘆 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Lightning-fast data retrieval by storing everything in RAM. Perfect for applications where speed is of the essence.

3. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Spread your data across multiple servers, ensuring scalability, fault tolerance, and improved performance in a distributed environment.

4. 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Classic and structured, these databases use tables to organize and link data, adhering to the principles of relational algebra. Think SQL databases!

5. 𝗚𝗿𝗮𝗽𝗵 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Unravel complex relationships in data through nodes and edges, making them perfect for social networks, fraud detection, and network analysis.

6. 𝗧𝗶𝗺𝗲-𝗦𝗲𝗿𝗶𝗲𝘀 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Tailored for managing time-stamped data, crucial for applications involving IoT, financial trading, and system monitoring.

7. 𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Like Relational Databases, but they store data in objects, mirroring real-world entities. Perfect for systems where data is naturally object-oriented.

8. 𝗡𝗼𝗦𝗤𝗟 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Perfect for unstructured or semi-structured data, allowing you to manage a variety of data types efficiently. They offer horizontal scalability, high availability, and flexibility in schema design.

9. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Harness the power of vectors to handle complex mathematical computations efficiently, ideal for applications like machine learning and data analytics.

Understanding their differences is not just about knowledge, it's about making informed decisions for efficient and effective data handling.

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All the FREE Stanford University Machine Learning Lectures 👇

Learn about Probability, NLP, LLMs, Transformers, and more ...

1. Probability for Computer Scientists - https://lnkd.in/e6sCyZGj

2. Machine Learning Full Course taught by Andrew Ng - https://lnkd.in/eWs74qyR

3. NLP with Deep Learning - https://lnkd.in/eazqcvmk

4. Machine Learning Explainability - https://lnkd.in/evimZ5Za

5. Reinforcement Learning - https://lnkd.in/eEf5PETJ

6. Deep Generative Models - https://lnkd.in/euZ2e3xU

7. Building Large Language Models (LLMs) - https://lnkd.in/eVUkaJuF

8. Machine Learning with Graphs - https://lnkd.in/eF_d3iwq

9. Transformers United - https://lnkd.in/eXdGBqQq

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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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All the SQL Window Functions you need to know 👇
A comprehensive cheatsheet covering all functions you'll ever need.

SQL window functions are essential tools for data scientists and ML engineers working with relational databases. Let's break down five key categories:

𝗥𝗮𝗻𝗸𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
• ROW_NUMBER(), RANK(), DENSE_RANK() assign positions to rows
• NTILE(n) splits data into equal groups
• PERCENT_RANK() and CUME_DIST() calculate relative positions

𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗼𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
• LAG() and LEAD() access previous or next row values
• FIRST_VALUE(), LAST_VALUE(), and NTH_VALUE() retrieve specific rows

𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
• SUM(), AVG(), COUNT(), MIN(), MAX() calculate across windows
• _DISTINCT variants work with unique values only

𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
• MEDIAN(), MODE(), PERCENTILE functions for distribution analysis
• STDDEV(), VARIANCE(), SKEWNESS() for statistical measures

𝗧𝗶𝗺𝗲-𝗕𝗮𝘀𝗲𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
• DATE_TRUNC(), TIME_BUCKET() group time data
• Functions for handling intervals and time differences

Save this guide for reference! I'd love to hear your thoughts 👉 What other cheatsheets would be useful to you?

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90% of Data Cleaning and Preprocessing scenarios can be handled with these SQL commands 👇

Here’s a quick breakdown of must-know SQL commands:
• String Operations: Use 𝚃𝚁𝙸𝙼 to remove extra spaces, 𝚄𝙿𝙿𝙴𝚁/𝙻𝙾𝚆𝙴𝚁 for case conversion, and 𝚁𝙴𝙿𝙻𝙰𝙲𝙴 to fix text issues.
• NULL Handling: Replace missing values with 𝙲𝙾𝙰𝙻𝙴𝚂𝙲𝙴, or filter them out using 𝚆𝙷𝙴𝚁𝙴 𝚌𝚘𝚕 𝙸𝚂 𝙽𝙾𝚃 𝙽𝚄𝙻𝙻.
• Numeric Fixes: Round numbers with 𝚁𝙾𝚄𝙽𝙳, or get absolute values using 𝙰𝙱𝚂().
• Date/Time: Extract parts of dates with 𝙴𝚇𝚃𝚁𝙰𝙲𝚃, or calculate differences using 𝙳𝙰𝚃𝙴𝙳𝙸𝙵𝙵.
• Aggregation: Use 𝙶𝚁𝙾𝚄𝙿 𝙱𝚈 with 𝙲𝙾𝚄𝙽𝚃, 𝚂𝚄𝙼, or 𝙰𝚅𝙶 to summarize data.
• Logic & Updates: Apply conditional logic with 𝙲𝙰𝚂𝙴 𝚆𝙷𝙴𝙽, or modify data using 𝚄𝙿𝙳𝙰𝚃𝙴.

Data Cleaning and Preprocessing is a crucial step in any data project. These commands simplify 90% of these tasks.

Bookmark this for your next project!

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Amazing Visualization of How LLMs like ChatGPT Work 

This 3D interactive tool makes it easy to grasp how large language models work, including the layers, matrix operations, and how everything fits together.

Check it out here → https://bbycroft.net/llm

This is the same tool that Andrej Karpathy uses to demonstrate how LLM works in his "Deep Dive into LLMs like ChatGPT" youtube video.

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11 Data Structures Every Developer Must Know 👇

Data structures are the backbone of efficient programming. Here's a simple guide to help you understand them:

• 𝗔𝗿𝗿𝗮𝘆: 
 - Like a row of boxes holding similar items
 - You can grab any item quickly if you know its position
 - Great for storing a fixed number of elements

• 𝗤𝘂𝗲𝘂𝗲:
 - Works like a line at a store
 - New items join at the back, items leave from the front
 - Useful for managing tasks in order

• 𝗧𝗿𝗲𝗲:
 - Branches out from a main "root" into smaller parts
 - Helps organize info in a family tree-like structure
 - Good for representing hierarchies

• 𝗠𝗮𝘁𝗿𝗶𝘅:
 - A grid of numbers or data
 - Organized in rows and columns
 - Handy for tables or image processing

• 𝗚𝗿𝗮𝗽𝗵:
 - Shows how different things connect
 - Uses dots (nodes) and lines (edges) to map relationships
 - Perfect for social networks or maps

• 𝗟𝗶𝗻𝗸𝗲𝗱 𝗟𝗶𝘀𝘁:
 - A chain of items, each pointing to the next
 - Easy to add or remove items anywhere in the list
 - Flexible for changing data

• 𝗠𝗮𝘅 𝗛𝗲𝗮𝗽:
 - A tree where the biggest value is always on top
 - Keeps things partially sorted
 - Efficient for finding the largest item quickly

• 𝗦𝘁𝗮𝗰𝗸:
 - Last in, first out - like a stack of plates
 - Used in undo functions or for tracking program execution
 - Helps manage temporary data in a structured way

• 𝗧𝗿𝗶𝗲:
 - A tree made for storing words or strings
 - Shares common prefixes to save space
 - Fast for spell-checking or autocomplete

• 𝗛𝗮𝘀𝗵𝗠𝗮𝗽:
 - Stores data using unique keys for quick retrieval
 - Like a super-fast dictionary lookup
 - Used in caches or for storing user data in apps

• 𝗛𝗮𝘀𝗵𝗦𝗲𝘁:
 - Keeps a collection of unique items
 - Checks if something is in the set very quickly
 - Useful for removing duplicates or tracking membership

Data structures help organize and store data in a way that makes it easy to access and modify. Understanding these structures can make your coding more efficient and effective.

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5 Loss Functions Every ML Practitioner Should Know 👇

Loss functions are key abilities that help machine learning models learn from data. Here's a simple guide created by my friend Prakhar Deroliya breaking down the most important ones:

𝗠𝗲𝗮𝗻 𝗦𝗾𝘂𝗮𝗿𝗲𝗱 𝗘𝗿𝗿𝗼𝗿 (𝗠𝗦𝗘)
• Used in regression problems
• Calculates average squared difference between predictions and actual values
• Always non-negative and differentiable

𝗕𝗶𝗻𝗮𝗿𝘆 𝗖𝗿𝗼𝘀𝘀 𝗘𝗻𝘁𝗿𝗼𝗽𝘆 (𝗕𝗖𝗘)
• Perfect for binary classification tasks
• Based on probability theory
• Works with outputs between 0 and 1

𝗙𝗼𝗰𝗮𝗹 𝗟𝗼𝘀𝘀
• Tackles class imbalance problems
• Gives more weight to hard-to-classify examples
• Uses parameters to adjust focus on difficult cases

𝗗𝗶𝗰𝗲 𝗟𝗼𝘀𝘀
• Specialized for image segmentation
• Measures overlap between predicted and actual masks
• Handles imbalanced pixel distributions naturally

𝗧𝗿𝗶𝗽𝗹𝗲𝘁 𝗟𝗼𝘀𝘀
• Used in metric learning and face verification
• Works with anchor, positive, and negative samples
• Creates distance between similar and different items

Each loss function serves specific needs in machine learning, making them essential abilities for model training.

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Common Loss Functions in Machine Learning

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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FREE Book – "Mathematics for Machine Learning" 👇

This UC Berkeley practical guide breaks down essential mathematical concepts needed for machine learning success, making complex topics clear and practical.

Here’s what the book covers:

• Linear Algebra fundamentals - vectors, matrices, eigenvalues and how they power ML algorithms
• Calculus & Optimization techniques that help train better models faster
• Probability theory basics that form the backbone of statistical learning
• Real-world applications showing how these concepts apply to actual ML problems

Perfect for anyone wanting to strengthen their mathematical foundation in ML.

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