Mathematical Foundations For Deep Learning

Roadmap To Learn Machine Learning ✨

Three different learning styles in machine learning algorithms:

1. Supervised Learning

Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.

A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.

Example problems are classification and regression.

Example algorithms include: Logistic Regression and the Back Propagation Neural Network.

2. Unsupervised Learning

Input data is not labeled and does not have a known result.

A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

Example problems are clustering, dimensionality reduction and association rule learning.

Example algorithms include: the Apriori algorithm and K-Means.

3. Semi-Supervised Learning

Input data is a mixture of labeled and unlabelled examples.

There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.

Example problems are classification and regression.

Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

📘 SQL Challenges for Data Analytics – With Explanation 🧠

(Beginner ➡️ Advanced)

1️⃣ Select Specific Columns

SELECT name, email FROM users;

This fetches only the name and email columns from the users table.

✔️ Used when you don’t want all columns from a table.


2️⃣ Filter Records with WHERE

SELECT * FROM users WHERE age > 30;

The WHERE clause filters rows where age is greater than 30.

✔️ Used for applying conditions on data.


3️⃣ ORDER BY Clause

SELECT * FROM users ORDER BY registered_at DESC;

Sorts all users based on registered_at in descending order.
✔️ Helpful to get latest data first.


4️⃣ Aggregate Functions (COUNT, AVG)

SELECT COUNT(*) AS total_users, AVG(age) AS avg_age FROM users;

Explanation:
- COUNT(*) counts total rows (users).
- AVG(age) calculates the average age.
✔️ Used for quick stats from tables.


5️⃣ GROUP BY Usage

SELECT city, COUNT(*) AS user_count FROM users GROUP BY city;

Groups data by city and counts users in each group.

✔️ Use when you want grouped summaries.


6️⃣ JOIN Tables

SELECT users.name, orders.amount  
FROM users  
JOIN orders ON users.id = orders.user_id;

Fetches user names along with order amounts by joining users and orders on matching IDs.
✔️ Essential when combining data from multiple tables.


7️⃣ Use of HAVING

SELECT city, COUNT(*) AS total  
FROM users  
GROUP BY city  
HAVING COUNT(*) > 5;

Like WHERE, but used with aggregates. This filters cities with more than 5 users.
✔️ **Use HAVING after GROUP BY.**


8️⃣ Subqueries

SELECT * FROM users  
WHERE salary > (SELECT AVG(salary) FROM users);

Finds users whose salary is above the average. The subquery calculates the average salary first.

✔️ Nested queries for dynamic filtering9️⃣ CASE Statementnt**

SELECT name,  
  CASE  
    WHEN age < 18 THEN 'Teen'  
    WHEN age <= 40 THEN 'Adult'  
    ELSE 'Senior'  
  END AS age_group  
FROM users;

Adds a new column that classifies users into categories based on age.
✔️ Powerful for conditional logic.

🔟 Window Functions (Advanced)

SELECT name, city, score,  
  RANK() OVER (PARTITION BY city ORDER BY score DESC) AS rank  
FROM users;

Ranks users by each city.

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🚀 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 — 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺

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𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.

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9 tips to master Power BI for Data Analysis:

📥 Learn to import data from various sources

🧹 Clean and transform data using Power Query

🧠 Understand relationships between tables using the data model

🧾 Write DAX formulas for calculated columns and measures

📊 Create interactive visuals: bar charts, slicers, maps, etc.

🎯 Use filters, slicers, and drill-through for deeper insights

📈 Build dashboards that tell a clear data story

🔄 Refresh and schedule your reports automatically

📚 Explore Power BI community and documentation for new tricks

Power BI Free Resources: https://t.iss.one/PowerBI_analyst

Hope it helps :)

#powerbi

Being a Generalist Data Scientist won't get you hired.
Here is how you can specialize 👇

Companies have specific problems that require certain skills to solve. If you do not know which path you want to follow. Start broad first, explore your options, then specialize.

To discover what you enjoy the most, try answering different questions for each DS role:


- 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
Qs:
“How should we monitor model performance in production?”

- 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 / 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭
Qs:
“How can we visualize customer segmentation to highlight key demographics?”

- 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭
Qs:
“How can we use clustering to identify new customer segments for targeted marketing?”

- 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫
Qs:
“What novel architectures can we explore to improve model robustness?”

- 𝐌𝐋𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
Qs:
“How can we automate the deployment of machine learning models to ensure continuous integration and delivery?”

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

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𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.

👉 Join today: https://go.readytensor.ai/cert-542-agentic-ai-certification

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A-Z of essential data science concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.iss.one/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

7 Must-Have Tools for Data Analysts in 2025:

✅ SQL – Still the #1 skill for querying and managing structured data
✅ Excel / Google Sheets – Quick analysis, pivot tables, and essential calculations
✅ Python (Pandas, NumPy) – For deep data manipulation and automation
✅ Power BI – Transform data into interactive dashboards
✅ Tableau – Visualize data patterns and trends with ease
✅ Jupyter Notebook – Document, code, and visualize all in one place
✅ Looker Studio – A free and sleek way to create shareable reports with live data.

Perfect blend of code, visuals, and storytelling.

React with ❤️ for free tutorials on each tool

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

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Join our international online AI competition. Register now for the contest! Award fund — RUB 6.5 mln!

Choose your track:

· 🤖 Agent-as-Judge — build a universal “judge” to evaluate AI-generated texts.

· 🧠 Human-centered AI Assistant — develop a personalized assistant based on GigaChat that mimics human behavior and anticipates preferences. Participants will receive API tokens and a chance to get an additional 1M tokens.

· 💾 GigaMemory — design a long-term memory mechanism for LLMs so the assistant can remember and use important facts in dialogue.

Why Join
Level up your skills, add a strong line to your resume, tackle pro-level tasks, compete for an award, and get an opportunity to showcase your work at AI Journey, a leading international AI conference.

How to Join
1. Register here: https://shorturl.at/l07fA
2. Choose your track.
3. Create your solution and submit it by 30 October 2025.

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What is the difference between data scientist, data engineer, data analyst and business intelligence?

🧑🔬 Data Scientist
Focus: Using data to build models, make predictions, and solve complex problems.
Cleans and analyzes data
Builds machine learning models
Answers “Why is this happening?” and “What will happen next?”
Works with statistics, algorithms, and coding (Python, R)
Example: Predict which customers are likely to cancel next month

🛠️ Data Engineer
Focus: Building and maintaining the systems that move and store data.
Designs and builds data pipelines (ETL/ELT)
Manages databases, data lakes, and warehouses
Ensures data is clean, reliable, and ready for others to use
Uses tools like SQL, Airflow, Spark, and cloud platforms (AWS, Azure, GCP)
Example: Create a system that collects app data every hour and stores it in a warehouse

📊 Data Analyst
Focus: Exploring data and finding insights to answer business questions.
Pulls and visualizes data (dashboards, reports)
Answers “What happened?” or “What’s going on right now?”
Works with SQL, Excel, and tools like Tableau or Power BI
Less coding and modeling than a data scientist
Example: Analyze monthly sales and show trends by region

📈 Business Intelligence (BI) Professional
Focus: Helping teams and leadership understand data through reports and dashboards.
Designs dashboards and KPIs (key performance indicators)
Translates data into stories for non-technical users
Often overlaps with data analyst role but more focused on reporting
Tools: Power BI, Looker, Tableau, Qlik
Example: Build a dashboard showing company performance by department

🧩 Summary Table
Data Scientist - What will happen? Tools: Python, R, ML tools, predictions & models
Data Engineer - How does the data move and get stored? Tools: SQL, Spark, cloud tools, infrastructure & pipelines
Data Analyst - What happened? Tools: SQL, Excel, BI tools, reports & exploration
BI Professional - How can we see business performance clearly? Tools: Power BI, Tableau, dashboards & insights for decision-makers

🎯 In short:
Data Engineers build the roads.
Data Scientists drive smart cars to predict traffic.
Data Analysts look at traffic data to see patterns.
BI Professionals show everyone the traffic report on a screen.

Data Science Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python
| | |-- R
| | |-- SQL
|
|-- Data Collection and Cleaning
| |-- Data Sources
| | |-- APIs
| | |-- Web Scraping
| | |-- Databases
| |
| |-- Data Cleaning
| | |-- Missing Values
| | |-- Data Transformation
| | |-- Data Normalization
|
|-- Data Analysis
| |-- Exploratory Data Analysis (EDA)
| | |-- Descriptive Statistics
| | |-- Data Visualization
| | |-- Hypothesis Testing
| |
| |-- Data Wrangling
| | |-- Pandas
| | |-- NumPy
| | |-- dplyr (R)
|
|-- Machine Learning
| |-- Supervised Learning
| | |-- Regression
| | |-- Classification
| |
| |-- Unsupervised Learning
| | |-- Clustering
| | |-- Dimensionality Reduction
| |
| |-- Reinforcement Learning
| | |-- Q-Learning
| | |-- Policy Gradient Methods
| |
| |-- Model Evaluation
| | |-- Cross-Validation
| | |-- Performance Metrics
| | |-- Hyperparameter Tuning
|
|-- Deep Learning
| |-- Neural Networks
| | |-- Feedforward Networks
| | |-- Backpropagation
| |
| |-- Advanced Architectures
| | |-- Convolutional Neural Networks (CNN)
| | |-- Recurrent Neural Networks (RNN)
| | |-- Transformers
| |
| |-- Tools and Frameworks
| | |-- TensorFlow
| | |-- PyTorch
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing
| | |-- Tokenization
| | |-- Stop Words Removal
| | |-- Stemming and Lemmatization
| |
| |-- NLP Techniques
| | |-- Word Embeddings
| | |-- Sentiment Analysis
| | |-- Named Entity Recognition (NER)
|
|-- Data Visualization
| |-- Basic Plotting
| | |-- Matplotlib
| | |-- Seaborn
| | |-- ggplot2 (R)
| |
| |-- Interactive Visualization
| | |-- Plotly
| | |-- Bokeh
| | |-- Dash
|
|-- Big Data
| |-- Tools and Frameworks
| | |-- Hadoop
| | |-- Spark
| |
| |-- NoSQL Databases
| |-- MongoDB
| |-- Cassandra
|
|-- Cloud Computing
| |-- Cloud Platforms
| | |-- AWS
| | |-- Google Cloud
| | |-- Azure
| |
| |-- Data Services
| |-- Data Storage (S3, Google Cloud Storage)
| |-- Data Pipelines (Dataflow, AWS Data Pipeline)
|
|-- Model Deployment
| |-- Serving Models
| | |-- Flask/Django
| | |-- FastAPI
| |
| |-- Model Monitoring
| |-- Performance Tracking
| |-- A/B Testing
|
|-- Domain Knowledge
| |-- Industry-Specific Applications
| | |-- Finance
| | |-- Healthcare
| | |-- Retail
|
|-- Ethical and Responsible AI
| |-- Bias and Fairness
| |-- Privacy and Security
| |-- Interpretability and Explainability
|
|-- Communication and Storytelling
| |-- Reporting
| |-- Dashboarding
| |-- Presentation Skills
|
|-- Advanced Topics
| |-- Time Series Analysis
| |-- Anomaly Detection
| |-- Graph Analytics
| |-- *PH4N745M*
└-- Comments
|-- # Single-line comment (Python)
└-- /* Multi-line comment (Python/R) */

Useful AI courses for free: 📱🤖

𝟭. Prompt Engineering Basics:
https://skillbuilder.aws/search?searchText=foundations-of-prompt-engineering&showRedirectNotFoundBanner=true

𝟮. ChatGPT Prompts Mastery:
https://deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

𝟯. Intro to Generative AI:
https://cloudskillsboost.google/course_templates/536

𝟰. AI Introduction by Harvard:
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python/2023-05

𝟱. Microsoft GenAI Basics:
https://linkedin.com/learning/what-is-generative-ai/generative-ai-is-a-tool-in-service-of-humanity

𝟲. Prompt Engineering Pro:
https://learnprompting.org

𝟳. Google’s Ethical AI:
https://cloudskillsboost.google/course_templates/554

𝟴. Harvard Machine Learning:
https://pll.harvard.edu/course/data-science-machine-learning

𝟵. LangChain App Developer:
https://deeplearning.ai/short-courses/langchain-for-llm-application-development/

𝟭𝟬. Bing Chat Applications:
https://linkedin.com/learning/streamlining-your-work-with-microsoft-bing-chat

𝟭𝟭. Generative AI by Microsoft:
https://learn.microsoft.com/en-us/training/paths/introduction-to-ai-on-azure/

𝟭𝟮. Amazon’s AI Strategy:
https://skillbuilder.aws/search?searchText=generative-ai-learning-plan-for-decision-makers&showRedirectNotFoundBanner=true

𝟭𝟯. GenAI for Everyone:
https://deeplearning.ai/courses/generative-ai-for-everyone/

React ♥️ for more

✅ 100 Days Artificial Intelligence Roadmap – 2025 🤖🚀

📍 Days 1–10: Python for AI
– Install Python, Jupyter
– Learn Python basics & data structures
– Numpy & Pandas for data wrangling

📍 Days 11–20: Math & Statistics Foundations
– Linear algebra: vectors, matrices
– Probability, statistics, distributions
– Understand data normalization, scaling

📍 Days 21–30: Data Exploration & Visualization
– Data cleaning basics
– Use Matplotlib, Seaborn for visuals
– Explore and summarize datasets

📍 Days 31–40: SQL & Databases
– Learn SQL queries (SELECT, JOIN, GROUP BY)
– Practice extracting data from relational databases

📍 Days 41–50: Core Machine Learning
– Supervised & unsupervised learning
– Scikit-learn basics (classification, regression, clustering)
– Model evaluation/metrics

📍 Days 51–60: Advanced ML & Projects
– Feature engineering & selection
– Hyperparameter tuning, cross-validation
– Complete ML mini-projects

📍 Days 61–70: Deep Learning Foundations
– Neural networks overview
– Use TensorFlow or PyTorch
– Build & train simple neural networks

📍 Days 71–80: Specialization – NLP / Computer Vision
– Basics of NLP or Image recognition
– Preprocessing, embeddings, CNN/RNN basics
– Work on a small domain project

📍 Days 81–90: MLOps & Deployment
– Version control with Git
– Model deployment basics (Flask/FastAPI)
– Track experiments, monitor models

📍 Days 91–100: GenAI, Trends & Capstone
– Explore Generative AI (LLMs, image generation)
– Ethics, prompt engineering
– Complete a capstone project, share on GitHub/portfolio

📚 React ❤️ for more!

✅ Data Science Fundamental Concepts You Should Know 📊🧠

1️⃣ Data Collection
Gathering raw data from various sources like databases, APIs, or web scraping for analysis.

2️⃣ Data Cleaning & Preprocessing
Preparing data by handling missing values, removing duplicates, correcting errors, and formatting for analysis.

3️⃣ Exploratory Data Analysis (EDA)
Using statistics and visualization to understand data patterns, trends, and detect outliers.

4️⃣ Statistical Inference
Drawing conclusions about populations using sample data through hypothesis testing, confidence intervals, and p-values.

5️⃣ Data Visualization
Creating charts and graphs (bar, line, scatter, histograms) to communicate insights clearly using tools like Matplotlib, Seaborn, or Tableau.

6️⃣ Feature Engineering
Transforming raw data into meaningful features that improve model performance, such as scaling, encoding and creating new variables.

7️⃣ Machine Learning Basics
Building predictive models by training algorithms on data:
⦁ Supervised Learning (regression, classification)
⦁ Unsupervised Learning (clustering, dimensionality reduction)

8️⃣ Model Evaluation
Assessing model accuracy using metrics like accuracy, precision, recall, F1 score (classification) and RMSE, MAE (regression).

9️⃣ Model Deployment
Putting your trained model into production so it can make real-time predictions or support decision-making.

🔟 Big Data & Tools
Handling large datasets using technologies like Hadoop, Spark, and databases such as SQL/NoSQL.

1️⃣1️⃣ Programming & Libraries
Essential coding skills in Python or R, with libraries like Pandas, NumPy, Scikit-learn for analysis and modeling.

1️⃣2️⃣ Data Ethics & Privacy
Ensuring responsible use of data, respecting privacy laws (GDPR), and avoiding biases in models.

💡 Tap ❤️ for more!