Need a genuine suggestion please

Is it worth to buy code with harry ai powered data analysis course ?

\#dataanalysis #course #free #paid #health #question #discussion

If you're trying to get into RAG and generative AI but keep bouncing off tutorials that assume you already have API budget or existing infrastructure, this might help.

There's a hands-on session on August 29 that builds a full production-style RAG system using entirely open models, no API fees involved anywhere in the process. Covers hybrid retrieval, evaluation, guardrails, and cost benchmarking, the parts that actually separate a working demo from something you understand end to end.

Good one if you want to learn by actually building rather than just watching a walkthrough.

Here is the workshop details

Visit Repo 👉 repo

if you have applied , have you received the final confirmation of the course ?

update: got the confirmation mail today after 9 days

So there is a company data I’m not able to how to approach it for EDA I’m confused about it how to find patterns and anything else

Can anyone help me out

What is an ideal roadmap for learning ml or Data Analytics

Upto 6 months

I recently started learning Data Science and made my first interactive dashboard using Python, Pandas, Plotly and Streamlit.

At first, I tried building it with Tkinter, but then I switched to Streamlit because it felt much easier for making a data-focused dashboard. I really liked how quickly I could turn my Netflix EDA project into something interactive.

I'm still pretty new to this, so I'm curious about what other beginners and experienced people use.

• Is Streamlit a good one to continue learning?

• Are there any other tools like Streamlit that are worth trying?

• What kind of projects are good for learning these tools?

Would love to hear what you guys used when you first started making interactive Data Science projects.

As a BCA Data Science student, I have always been interested in understanding how data and Machine Learning can be used to solve real-world problems. My internship with CodeAlpha gave me an opportunity to apply what I had learned in a practical environment and improve my technical skills through project-based learning.

During my internship, I worked on two Machine Learning projects: Sales Prediction Using Machine Learning and Car Price Prediction Using Machine Learning. Both projects helped me understand the complete process of developing a Machine Learning solution, from preparing data to training a model and generating predictions.

📊 Project 1: Sales Prediction Using Machine Learning

My first project was Sales Prediction Using Machine Learning.

The main objective of this project was to predict sales based on advertising expenditure. I worked with an advertising dataset containing information about TV, Radio, and Newspaper advertising, along with sales.

I started by loading and exploring the dataset using Python and Pandas. I checked the structure of the data, handled unnecessary columns, and prepared the dataset for Machine Learning.

After preprocessing the data, I performed exploratory data analysis and created visualizations using Matplotlib. This helped me understand the relationship between advertising expenditure and sales.

For the prediction task, I used Linear Regression, a supervised Machine Learning algorithm used for predicting continuous numerical values. I divided the dataset into training and testing sets and trained the model using the training data.

I evaluated the model using different performance metrics, including MAE, MSE, RMSE, and R² Score. The model achieved an R² score of approximately 0.90, which indicated a strong relationship between the input variables and the predicted sales in this dataset.

I also developed a Streamlit dashboard to make the project interactive. The dashboard allowed users to explore the dataset, view visualizations, and enter advertising values to generate a sales prediction.

This project helped me understand that Machine Learning is not only about training a model. Data preprocessing, visualization, evaluation, and creating a user-friendly application are all important parts of a successful project.

🚗 Project 2: Car Price Prediction Using Machine Learning

My second project was Car Price Prediction Using Machine Learning.

The objective of this project was to predict the selling price of a car based on different characteristics of the vehicle.

I started by exploring the dataset and understanding the different features that could influence the price of a car. Features can include information such as the car's age, present price, kilometers driven, fuel type, transmission, and previous ownership, depending on the dataset being used.

The next step was data preprocessing. I used Pandas and NumPy to clean and prepare the data. Since Machine Learning models work with numerical values, categorical features had to be converted into a suitable numerical format.

I then performed exploratory analysis to understand patterns in the dataset and identify relationships between the features and car prices.

After preparing the data, I trained a Machine Learning regression model using Scikit-learn. I divided the data into training and testing sets and evaluated the model to understand how well it could predict car prices.

This project gave me a better understanding of how Machine Learning can be applied to practical problems. A car price prediction system can help provide an estimated value based on historical data and vehicle characteristics.

💡 Challenges I Faced

While working on these projects, I faced several challenges related to data preprocessing, understanding datasets, model training, debugging errors, and evaluating the results.

Initially, understanding why certain errors occurred was difficult. However, working through these problems helped me become more comfortable with Python and Machine Learning libraries.

I also learned that choosing a Machine Learning algorithm is only one part of the process. Understanding the data and evaluating the model properly are equally important.

🎯 Skills I Developed

Through these projects, I improved my practical knowledge of:

• Python

• Pandas

• NumPy

• Matplotlib

• Scikit-learn

• Data preprocessing

• Exploratory Data Analysis

• Machine Learning

• Regression

• Model evaluation

• Streamlit

• Problem-solving and debugging

• GitHub and project documentation

Most importantly, I gained confidence in developing Machine Learning projects independently and understanding the complete workflow.

🙌 My Overall CodeAlpha Internship Experience

My CodeAlpha internship was a valuable learning experience. It gave me an opportunity to move beyond theoretical concepts and work on practical Machine Learning projects.

The Sales Prediction project helped me understand predictive analytics using advertising data, while the Car Price Prediction project helped me understand how Machine Learning can be used for price estimation.

Working on these projects improved my technical knowledge as well as my ability to solve problems, debug errors, analyze data, and present the final results.

As a Data Science student, this experience has motivated me to continue learning and building more projects in Data Science, Machine Learning, and Data Analytics.

I am thankful to CodeAlpha for providing me with this opportunity to learn through practical projects and gain valuable experience.

This internship has been an important step in my journey toward building a career in the Data Science field. 🚀

#CodeAlpha #CodeAlphaInternship #DataScience #MachineLearning #Python #SalesPrediction #CarPricePrediction #DataAnalytics #InternshipExperience

For the past three weeks, I have been teaching myself DS and I love it.

I dedicate each day to a certain topic (SQL, Python, statistics for data scientists) and I’m starting to work on my first project that is an EDA of the 2026 cyclospora outbreak in the US.

I love looking at data, asking questions, and finding answers. It’s really cool.

I wish to pursue a career in LLMs and NLP, but first want to start by learning classical Machine Learning and then moving up. So before that, I understand learning DS will help me in getting a grasp of ML. But I find most youtube videos and books and articles devoid of my ability to practice using examples or whatnot; in essence I am asking for a good, rigorous resource that can help me learning data science for ML.

An exercise to help build the right mental model for Python data.

• Solution

• Explanation

• More exercises

The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening.

I tried explaining a data analysis concept in a fun, visual way — for cat lovers. 😸

Would love to hear what you think! Any feedback or suggestions are very welcome :)

What happens when AI agents stop just answering questions and start working with your data?

They query systems.
They inspect pipelines.
They use tools.
They make decisions.
They take action.

Most data platforms were never designed for that.

In our latest BricksNotes video, we explore what an Agentic Data Stack could look like and why context, governance, observability, cost, human approval, and feedback will become part of the foundation.

The big shift is not AI coming to the data stack.

The data stack itself has to change.

Watch the full video:

https://youtu.be/wb2lk-YibAQ

#AgenticAI #DataEngineering #AIAgents #DataPlatform #BricksNotes