For those of you who are new to Neural Networks, let me try to give you a brief overview.

Neural networks are computational models inspired by the human brain's structure and function. They consist of interconnected layers of nodes (or neurons) that process data and learn patterns. Here's a brief overview:

1. Structure: Neural networks have three main types of layers:
- Input layer: Receives the initial data.
- Hidden layers: Intermediate layers that process the input data through weighted connections.
- Output layer: Produces the final output or prediction.

2. Neurons and Connections: Each neuron receives input from several other neurons, processes this input through a weighted sum, and applies an activation function to determine the output. This output is then passed to the neurons in the next layer.

3. Training: Neural networks learn by adjusting the weights of the connections between neurons using a process called backpropagation, which involves:
- Forward pass: Calculating the output based on current weights.
- Loss calculation: Comparing the output to the actual result using a loss function.
- Backward pass: Adjusting the weights to minimize the loss using optimization algorithms like gradient descent.

4. Activation Functions: Functions like ReLU, Sigmoid, or Tanh are used to introduce non-linearity into the network, enabling it to learn complex patterns.

5. Applications: Neural networks are used in various fields, including image and speech recognition, natural language processing, and game playing, among others.

Overall, neural networks are powerful tools for modeling and solving complex problems by learning from data.

30 Days of Data Science: https://t.iss.one/datasciencefun/1704

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Are you looking to become a machine learning engineer? The algorithm brought you to the right place! 📌

I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer:

Math & Statistics

Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.

Here are the probability units you will need to focus on:

Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra

Python:

You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.

Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking

Machine Learning Prerequisites:

Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data

Machine Learning Fundamentals

Using scikit-learn library in combination with other Python libraries for:

Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)

Solving two types of problems:
Regression
Classification

Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.

Types of Neural Networks:

Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.

In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.

Deep Learning:

Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.

Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models

Machine Learning Project Deployment

Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:

Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs

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Guys, Big Announcement!

We’ve officially hit 5 Lakh followers on WhatsApp and it’s time to level up together! ❤️

I've launched a Python Learning Series — designed for beginners to those preparing for technical interviews or building real-world projects.

This will be a step-by-step journey — from basics to advanced — with real examples and short quizzes after each topic to help you lock in the concepts.

Here’s what we’ll cover in the coming days:

Week 1: Python Fundamentals

- Variables & Data Types

- Operators & Expressions

- Conditional Statements (if, elif, else)

- Loops (for, while)

- Functions & Parameters

- Input/Output & Basic Formatting


Week 2: Core Python Skills

- Lists, Tuples, Sets, Dictionaries

- String Manipulation

- List Comprehensions

- File Handling

- Exception Handling


Week 3: Intermediate Python

- Lambda Functions

- Map, Filter, Reduce

- Modules & Packages

- Scope & Global Variables

- Working with Dates & Time


Week 4: OOP & Pythonic Concepts

- Classes & Objects

- Inheritance & Polymorphism

- Decorators (Intro level)

- Generators & Iterators

- Writing Clean & Readable Code


Week 5: Real-World & Interview Prep

- Web Scraping (BeautifulSoup)

- Working with APIs (Requests)

- Automating Tasks

- Data Analysis Basics (Pandas)

- Interview Coding Patterns

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Learning Python Network
Programming

📚 book

The Only roadmap you need to become an ML Engineer 🥳

Phase 1: Foundations (1-2 Months)
🔹 Math & Stats Basics – Linear Algebra, Probability, Statistics
🔹 Python Programming – NumPy, Pandas, Matplotlib, Scikit-Learn
🔹 Data Handling – Cleaning, Feature Engineering, Exploratory Data Analysis

Phase 2: Core Machine Learning (2-3 Months)
🔹 Supervised & Unsupervised Learning – Regression, Classification, Clustering
🔹 Model Evaluation – Cross-validation, Metrics (Accuracy, Precision, Recall, AUC-ROC)
🔹 Hyperparameter Tuning – Grid Search, Random Search, Bayesian Optimization
🔹 Basic ML Projects – Predict house prices, customer segmentation

Phase 3: Deep Learning & Advanced ML (2-3 Months)
🔹 Neural Networks – TensorFlow & PyTorch Basics
🔹 CNNs & Image Processing – Object Detection, Image Classification
🔹 NLP & Transformers – Sentiment Analysis, BERT, LLMs (GPT, Gemini)
🔹 Reinforcement Learning Basics – Q-learning, Policy Gradient

Phase 4: ML System Design & MLOps (2-3 Months)
🔹 ML in Production – Model Deployment (Flask, FastAPI, Docker)
🔹 MLOps – CI/CD, Model Monitoring, Model Versioning (MLflow, Kubeflow)
🔹 Cloud & Big Data – AWS/GCP/Azure, Spark, Kafka
🔹 End-to-End ML Projects – Fraud detection, Recommendation systems

Phase 5: Specialization & Job Readiness (Ongoing)
🔹 Specialize – Computer Vision, NLP, Generative AI, Edge AI
🔹 Interview Prep – Leetcode for ML, System Design, ML Case Studies
🔹 Portfolio Building – GitHub, Kaggle Competitions, Writing Blogs
🔹 Networking – Contribute to open-source, Attend ML meetups, LinkedIn presence

Follow this advanced roadmap to build a successful career in ML!

The data field is vast, offering endless opportunities so start preparing now.

𝟱 𝗖𝗼𝗱𝗶𝗻𝗴 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗧𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗠𝗮𝘁𝘁𝗲𝗿 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 💻

You don’t need to be a LeetCode grandmaster.
But data science interviews still test your problem-solving mindset—and these 5 types of challenges are the ones that actually matter.

Here’s what to focus on (with examples) 👇

🔹 1. String Manipulation (Common in Data Cleaning)

✅ Parse messy columns (e.g., split “Name_Age_City”)
✅ Regex to extract phone numbers, emails, URLs
✅ Remove stopwords or HTML tags in text data

Example: Clean up a scraped dataset from LinkedIn bias

🔹 2. GroupBy and Aggregation with Pandas

✅ Group sales data by product/region
✅ Calculate avg, sum, count using .groupby()
✅ Handle missing values smartly

Example: “What’s the top-selling product in each region?”

🔹 3. SQL Join + Window Functions

✅ INNER JOIN, LEFT JOIN to merge tables
✅ ROW_NUMBER(), RANK(), LEAD(), LAG() for trends
✅ Use CTEs to break complex queries

Example: “Get 2nd highest salary in each department”

🔹 4. Data Structures: Lists, Dicts, Sets in Python

✅ Use dictionaries to map, filter, and count
✅ Remove duplicates with sets
✅ List comprehensions for clean solutions

Example: “Count frequency of hashtags in tweets”

🔹 5. Basic Algorithms (Not DP or Graphs)

✅ Sliding window for moving averages
✅ Two pointers for duplicate detection
✅ Binary search in sorted arrays

Example: “Detect if a pair of values sum to 100”

🎯 Tip: Practice challenges that feel like real-world data work, not textbook CS exams.

Use platforms like:

StrataScratch
Hackerrank (SQL + Python)
Kaggle Code

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Data-Driven Decision Making

Data-driven decision-making (DDDM) involves using data analytics to guide business strategies instead of relying on intuition. Key techniques include A/B testing, forecasting, trend analysis, and KPI evaluation.

1️⃣ A/B Testing & Hypothesis Testing

A/B testing compares two versions of a product, marketing campaign, or website feature to determine which performs better.

✔ Key Metrics in A/B Testing:

Conversion Rate

Click-Through Rate (CTR)

Revenue per User


✔ Steps in A/B Testing:

1. Define the hypothesis (e.g., "Changing the CTA button color will increase clicks").


2. Split users into Group A (control) and Group B (test).


3. Analyze differences using statistical tests.



✔ SQL for A/B Testing:

Calculate average purchase per user in two test groups

SELECT test_group, AVG(purchase_amount) AS avg_purchase  
FROM ab_test_results  
GROUP BY test_group;

Run a t-test to check statistical significance (Python)

from scipy.stats import ttest_ind
t_stat, p_value = ttest_ind(group_A['conversion_rate'], group_B['conversion_rate'])
print(f"T-statistic: {t_stat}, P-value: {p_value}")

🔹 P-value < 0.05 → Statistically significant difference.
🔹 P-value > 0.05 → No strong evidence of difference.


2️⃣ Forecasting & Trend Analysis

Forecasting predicts future trends based on historical data.

✔ Time Series Analysis Techniques:

Moving Averages (smooth trends)

Exponential Smoothing (weights recent data more)

ARIMA Models (AutoRegressive Integrated Moving Average)


✔ SQL for Moving Averages:

7-day moving average of sales

SELECT order_date,  
       sales,  
       AVG(sales) OVER (ORDER BY order_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS moving_avg  
FROM sales_data;

✔ Python for Forecasting (Using Prophet)

from fbprophet import Prophet
model = Prophet()
model.fit(df)
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)
model.plot(forecast)

3️⃣ KPI & Metrics Analysis

KPIs (Key Performance Indicators) measure business performance.

✔ Common Business KPIs:

Revenue Growth Rate → (Current Revenue - Previous Revenue) / Previous Revenue

Customer Retention Rate → Customers at End / Customers at Start

Churn Rate → % of customers lost over time

Net Promoter Score (NPS) → Measures customer satisfaction


✔ SQL for KPI Analysis:

Calculate Monthly Revenue Growth

SELECT month,  
       revenue,  
       LAG(revenue) OVER (ORDER BY month) AS prev_month_revenue,  
       (revenue - prev_month_revenue) / prev_month_revenue * 100 AS growth_rate  
FROM revenue_data;

✔ Python for KPI Dashboard (Using Matplotlib)

import matplotlib.pyplot as plt
plt.plot(df['month'], df['revenue_growth'], marker='o')
plt.title('Monthly Revenue Growth')
plt.xlabel('Month')
plt.ylabel('Growth Rate (%)')
plt.show()

4️⃣ Real-Life Use Cases of Data-Driven Decisions

📌 E-commerce: Optimize pricing based on customer demand trends.
📌 Finance: Predict stock prices using time series forecasting.
📌 Marketing: Improve email campaign conversion rates with A/B testing.
📌 Healthcare: Identify disease patterns using predictive analytics.


Mini Task for You: Write an SQL query to calculate the customer churn rate for a subscription-based company.

Data Analyst Roadmap: 👇
https://t.iss.one/sqlspecialist/1159

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Breaking into Data Science doesn’t need to be complicated.

If you’re just starting out,

Here’s how to simplify your approach:

Avoid:
🚫 Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once.
🚫 Spending months on theoretical concepts without hands-on practice.
🚫 Overloading your resume with keywords instead of impactful projects.
🚫 Believing you need a Ph.D. to break into the field.

Instead:

✅ Start with Python or R—focus on mastering one language first.
✅ Learn how to work with structured data (Excel or SQL) - this is your bread and butter.
✅ Dive into a simple machine learning model (like linear regression) to understand the basics.
✅ Solve real-world problems with open datasets and share them in a portfolio.
✅ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests.

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

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#ai #datascience

An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.

Basically, there are 3 different layers in a neural network :

Input Layer (All the inputs are fed in the model through this layer)

Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)

Output Layer (The data after processing is made available at the output layer)

Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.