https://youtu.be/cy708ChEi9U
Hi guys a lot of you have not subscribed my channel yet. If you're reading this message then don't forget to subscribe my channel and comment your views.  At least half of you go and subscribe my channel.
Thank you in advance ☺️

https://youtu.be/620L59zpdQw

Hi guys a lot of you have not subscribed my channel yet. If you're reading this message then don't forget to subscribe my channel and comment your views.  At least half of you go and subscribe my channel.
Thank you in advance ☺️

https://youtu.be/ZOJvKbbc6cw


Hi guys a lot of you have not subscribed my channel yet. If you're reading this message then don't forget to subscribe my channel and comment your views.  At least half of you go and subscribe my channel.
Thank you in advance ☺️

If tomorrow I were hit by a bus, got a concussion, and lost all of my memory.
I would be SAD, but I'd still want to learn ML engineering from scratch. And here is how I would do it in 2024, mostly in order:

Learn math:
- linear algebra: Matrices, vectors, linear equations and transformations,
- probability and statistics: Distributions, hypothesis testing, Bias-Variance Tradeoff, and conditional probability.
- calculus: Derivatives, integrals, gradient descent.

Learn Python:
- data structures: Lists, arrays, and dictionaries.
- libraries: NumPy, Pandas for data manipulations, scikit-learn for ml and Matplotlib for data viz.
- code organization and control flow: Functions, loops, and conditionals.

Data Preprocessing:
- learn how to handle missing values, duplicates, and outliers
- read on feature engineering
- try out data normalization and scaling

Core ML Algorithms:
- Supervised Learning: Linear and Logistic Regression, and Classification algorithms(K-NN, Decision Trees, SVM)
- Unsupervised Learning: Clustering (K-means) and dimensionality reduction techniques (PCA)
- Reinforcement Learning: Basics of agents, environments, and reward systems

Model Evaluation and Validation:
- learn metrics like accuracy, precision, recall, and F1 score
- cross-validation techniques
- learn about overfitting and underfitting, and how to address them

Get Hands-On Experience with Deployment:
- use datasets from Kaggle and/or models from Huggingface.
- learn to deploy with Flask, FastAPI, and on Cloud (AWS or Azure or GCP)
- participate in ml competitions or workshops.

Digest everything before diving into advanced topics like:
- ml frameworks like PyTorch, and TensorFlow
- neural networks, convolutional networks, and recurrent networks
- understand (NLP) and computer vision (CV) applications and ensemble techniques

Some frequently Asked SQL Interview Questions with Answers in data analyst interviews:

1. Write a SQL query to find the average purchase amount for each customer. Assume you have two tables: Customers (CustomerID, Name) and Orders (OrderID, CustomerID, Amount).

SELECT c.CustomerID, c. Name, AVG(o.Amount) AS AveragePurchase
FROM Customers c
JOIN Orders o ON c.CustomerID = o.CustomerID
GROUP BY c.CustomerID, c. Name;

2. Write a query to find the employee with the minimum salary in each department from a table Employees with columns EmployeeID, Name, DepartmentID, and Salary.

SELECT e1.DepartmentID, e1.EmployeeID, e1 .Name, e1.Salary
FROM Employees e1
WHERE Salary = (SELECT MIN(Salary) FROM Employees e2 WHERE e2.DepartmentID = e1.DepartmentID);

3. Write a SQL query to find all products that have never been sold. Assume you have a table Products (ProductID, ProductName) and a table Sales (SaleID, ProductID, Quantity).

SELECT p.ProductID, p.ProductName
FROM Products p
LEFT JOIN Sales s ON p.ProductID = s.ProductID
WHERE s.ProductID IS NULL;

4. Given a table Orders with columns OrderID, CustomerID, OrderDate, and a table OrderItems with columns OrderID, ItemID, Quantity, write a query to find the customer with the highest total order quantity.

SELECT o.CustomerID, SUM(oi.Quantity) AS TotalQuantity
FROM Orders o
JOIN OrderItems oi ON o.OrderID = oi.OrderID
GROUP BY o.CustomerID
ORDER BY TotalQuantity DESC
LIMIT 1;

5. Write a SQL query to find the earliest order date for each customer from a table Orders (OrderID, CustomerID, OrderDate).

SELECT CustomerID, MIN(OrderDate) AS EarliestOrderDate
FROM Orders
GROUP BY CustomerID;

6. Given a table Employees with columns EmployeeID, Name, ManagerID, write a query to find the number of direct reports for each manager.

SELECT ManagerID, COUNT(*) AS NumberOfReports
FROM Employees
WHERE ManagerID IS NOT NULL
GROUP BY ManagerID;


7. Given a table Customers with columns CustomerID, Name, JoinDate, and a table Orders with columns OrderID, CustomerID, OrderDate, write a query to find customers who placed their first order within the last 30 days.

SELECT c.CustomerID, c. Name
FROM Customers c
JOIN Orders o ON c.CustomerID = o.CustomerID
WHERE o.OrderDate = (SELECT MIN(o2.OrderDate) FROM Orders o2 WHERE o2.CustomerID = c.CustomerID)
AND o.OrderDate >= CURRENT_DATE - INTERVAL '30 day';

GridSearchCV vs RandomizedSearchCV in Machine Learning: Differences, Advantages & Disadvantages of Each, and Use Cases

1. GridSearchCV
- Definition: GridSearchCV is an exhaustive search over specified parameter values for an estimator. It uses cross-validation to evaluate the performance of each combination of parameter values.

How it Works:
- Parameter Grid: Define a grid of parameters to search over.
- Exhaustive Search: Evaluate all possible combinations of parameters in the grid.
- Cross-Validation: For each combination, perform cross-validation to assess the model's performance.
- Best Parameters: Select the combination that results in the best performance based on a predefined metric (e.g., accuracy, F1-score).

2. RandomizedSearchCV
- Definition: RandomizedSearchCV performs a random search over specified parameter values for an estimator. It samples a fixed number of parameter settings from the specified distributions.

How it Works:
- Parameter Distributions: Define distributions from which to sample parameter values.
- Random Sampling: Randomly sample a fixed number of parameter combinations.
- Cross-Validation: For each sampled combination, perform cross-validation to assess the model's performance.
- Best Parameters: Select the combination that results in the best performance based on a predefined metric.

Advantages and Disadvantages
- GridSearchCV:
-- Advantages:
1. Exhaustive Search: Guarantees finding the optimal combination within the specified grid.
2. Deterministic: Always produces the same results for the same parameter grid and data.
-- Disadvantages:
1. Computationally Expensive: Evaluates all combinations, which can be very slow for large grids.
2. Scalability Issues: Not feasible for high-dimensional parameter spaces.

- RandomizedSearchCV:
-- Advantages:
1. Efficiency: Can be faster than GridSearchCV by evaluating a fixed number of parameter combinations.
2. Scalability: More feasible for high-dimensional parameter spaces.
3. Exploration: Can potentially find good parameter combinations that GridSearchCV might miss due to its limited grid.
-- Disadvantages:
1. Non-Exhaustive: May not find the optimal combination if the number of iterations is too low.
2. Randomness: Results can vary between runs unless a random seed is set.

Use Cases
- GridSearchCV:
1. Small Parameter Spaces: Suitable when the parameter grid is small and computational resources are sufficient.
2. High Precision: When the goal is to find the exact optimal parameters within the defined grid.
3. Limited Time Constraint: When there is enough time to perform an exhaustive search.
- RandomizedSearchCV:
1. Large Parameter Spaces: Suitable for larger and high-dimensional parameter spaces where an exhaustive search is impractical.
2. Time Efficiency: When there is a need to balance between time and performance, providing a good solution quickly.
3. Exploratory Analysis: Useful in the early stages of model tuning to quickly identify promising parameter regions.

Yesterday's Llama 3.1 release marked a big milestone for LLM researchers and practitioners. Llama 3.1 405B is the biggest and most capable LLM with openly available LLMs. And particularly exciting is that the new Llama release comes with a 93-page research paper this time. Below, I want to share a few interesting facts from the paper, and I will likely write a longer analysis this weekend.

Model sizes

Llama 3.1 now comes in 3 sizes: 8B, 70B, and 405B parameters. The 8B and 70B variants are sight upgrades from the previous Llama 3 models that have been released in April 2024. (See the figure below for a brief performance comparison). The 405B model was used to improve the 8B and 70B via synthetic data during the finetuning stages.

Pretraining Data

The 93-page report by Meta (a link to the report is in the comments below) offers amazing detail. Particularly, the section on preparing the 15.6 trillion tokens for pretraining offers so much detail that it would make it possible to reproduce the dataset preparation. However, Meta doesn't share the dataset sources. All we know is that it's trained primarily on "web data." This is probably because of the usual copyright concerns and to prevent lawsuits.

Still, it's a great writeup if you plan to prepare your own pretraining datasets as it shares recipes on deduplication, formatting (removal of markdown markers), quality filters, removal of unsafe content, and more.

Long-context Support

The models support a context size of up to 128k tokens. The researchers achieved this via a multiple-stage process. First, they pretrained it on 8k context windows (due to resource constraints), followed by continued pretraining on longer 128k token windows. In the continued pretraining, they increased the context length in 6 stages. Moreover, they also observed that finetuning requires 0.1% of long-context instruction samples; otherwise, the long-context capabilities will decline.

Alignment

In contrast to earlier rumors, Llama 3 was not finetuned using both RLHF with proximal policy optimization (PPO) and direct preference optimization (DPO). Following a supervised instruction finetuning stage (SFT), the models were only trained with DPO, not PPO. (Unlike in the Llama 2 paper, unfortunately, the researchers didn't include a chart analyzing the improvements made via this process.). Although they didn't use PPO, they used a reward model for rejection sampling during the instruction finetuning stage.

Inference

The 405B model required 16k H100 GPUs for training. During inference, the bfloat16-bit version of the model still requires 16 H100 GPUs. However, Meta also has an FP8 version that runs on a single server node (that is, 8xH100s).

Performance
You are probably curious about how it compares to other models. The short answer is "very favorable", on par with GPT4.

Here are 5 beginner-friendly data science project ideas

Loan Approval Prediction
Predict whether a loan will be approved based on customer demographic and financial data. This requires data preprocessing, feature engineering, and binary classification techniques.

Credit Card Fraud Detection
Detect fraudulent credit card transactions with a dataset that contains transactions made by credit cards. This is a good project for learning about imbalanced datasets and anomaly detection methods.

Netflix Movies and TV Shows Analysis
Analyze Netflix's movies and TV shows to discover trends in ratings, popularity, and genre distributions. Visualization tools and exploratory data analysis are key components here.

Sentiment Analysis of Tweets
Analyze the sentiment of tweets to determine whether they are positive, negative, or neutral. This project involves natural language processing and working with text data.

Weather Data Analysis
Analyze historical weather data from the National Oceanic and Atmospheric Administration (NOAA) to look for seasonal trends, weather anomalies, or climate change indicators. This project involves time series analysis and data visualization.

https://youtu.be/ZOJvKbbc6cw


Hi guys a lot of you have not subscribed my channel yet. If you're reading this message then don't forget to subscribe my channel and comment your views.  At least half of you go and subscribe my channel.
Thank you in advance

Remove Consecutive Duplicates in Python from a list – Data Science With Ved
https://datasciencewithved.wordpress.com/2024/08/28/remove-consecutive-duplicates-in-python-from-a-list/

Check out my new blog.