What's your favorite approach to learning new technologies?

1. Online courses
2. Tutorials and documentation
3. Books
4. Hands-on projects
5. Community forums and meetups

If any other, add in comments 👇👇

Here’s a detailed breakdown of critical roles and their associated responsibilities:


🔘 Data Engineer: Tailored for Data Enthusiasts

1. Data Ingestion: Acquire proficiency in data handling techniques.
2. Data Validation: Master the art of data quality assurance.
3. Data Cleansing: Learn advanced data cleaning methodologies.
4. Data Standardisation: Grasp the principles of data formatting.
5. Data Curation: Efficiently organise and manage datasets.

🔘 Data Scientist: Suited for Analytical Minds

6. Feature Extraction: Hone your skills in identifying data patterns.
7. Feature Selection: Master techniques for efficient feature selection.
8. Model Exploration: Dive into the realm of model selection methodologies.

🔘 Data Scientist & ML Engineer: Designed for Coding Enthusiasts

9. Coding Proficiency: Develop robust programming skills.
10. Model Training: Understand the intricacies of model training.
11. Model Validation: Explore various model validation techniques.
12. Model Evaluation: Master the art of evaluating model performance.
13. Model Refinement: Refine and improve candidate models.
14. Model Selection: Learn to choose the most suitable model for a given task.

🔘 ML Engineer: Tailored for Deployment Enthusiasts

15. Model Packaging: Acquire knowledge of essential packaging techniques.
16. Model Registration: Master the process of model tracking and registration.
17. Model Containerisation: Understand the principles of containerisation.
18. Model Deployment: Explore strategies for effective model deployment.

These roles encompass diverse facets of Data and ML, catering to various interests and skill sets. Delve into these domains, identify your passions, and customise your learning journey accordingly.

Evolution in #data and #AI.
Data analyst -> data scientist -> AI engineer -> ???

What kind of problems neural nets can solve?

Neural nets are good at solving non-linear problems. Some good examples are problems that are relatively easy for humans (because of experience, intuition, understanding, etc), but difficult for traditional regression models: speech recognition, handwriting recognition, image identification, etc.

Future Trends in Artificial Intelligence 👇👇

1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.

2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.

3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.

4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.

5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.

6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.

7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.

Like for more ❤️

Artificial Intelligence

Hi guys,

So, today I am working on something interesting.

I am creating an youtube channel on songs.

What's special: These songs are AI Generated.

I'll ask you guys for feedback as well

Stay tuned 😄❤️

First song on youtube
👇👇
https://youtu.be/GrtZsXaZcCI?si=HPGd-cKx1Xm-lLa0

Share your views in the comments 😄

Another version of this song 👇
https://youtu.be/Nb6NueqTD0g?si=pORmUccxPhkUH5CG

A new song coming up specially dedicated to all data aspirants, hopefully you guys can relate with it 😄❤️

Here is the next song - data dreams
👇👇
https://youtu.be/CxC0T9mpFL0?si=UxNq_ZGUlW4ZsrTD

Dedicated to all data lovers ❤️

Please like and share your comments on this 😁❤️

Tech stack for Machine Learning in 2024:

- ml workflow orchestrator: Kubeflow
- experiment tracking: MLflow
- data ingestion: Airbyte
- job orchestrator: Apache Airflow
- batch pipeline: Apache Spark
- message queue for real-time streaming: Apache Kafka
- feature engineering: Scikit-learn
- model selection and training: Pytorch
- hyperparameter tuning: Ray Tune
- model evaluation: Weights & Biases
- model monitoring: Grafana
- CI/CD: Github actions
- model versioning: neptune
- model serving: BentoML
- web app framework: Flask
- front-end: React
- feature store: Qwak
- Graph database: Neo4j
- Vector database: ChromaDB
- NoSQL database: MongoDB
- In-memory data store: Redis

...

What is your current ML tech stack?

Data Science vs Data Engineering vs AI Song
👇👇
https://youtu.be/WQOzBawrTsQ?si=8wVYA3Me_SGM2GDs

Took a lot of efforts, please share your views in comments 😄

Has anyone watched Vinland Saga? It will change the way you look at life.

I would recommend it even if you aren't an anime lover.

Very important concept
The 80/20 Principle

How to convert image to pdf in Python

# Python3 program to convert image to pfd
# using img2pdf library
 
# importing necessary libraries
import img2pdf
from PIL import Image
import os
 
# storing image path
img_path = "Input.png"
 
# storing pdf path
pdf_path = "file_pdf.pdf"
 
# opening image
image = Image.open(img_path)
 
# converting into chunks using img2pdf
pdf_bytes = img2pdf.convert(image.filename)
 
# opening or creating pdf file
file = open(pdf_path, "wb")
 
# writing pdf files with chunks
file.write(pdf_bytes)
 
# closing image file
image.close()
 
# closing pdf file
file.close()
 
# output
print("Successfully made pdf file")

pip3 install pillow && pip3 install img2pdf

⭐⭐⭐ Advance Level Data science Projects ⭐⭐⭐

1) Identify your Digits Dataset : https://www.kaggle.com/c/digit-recognizer/data

2) Recommendation Engine : https://cseweb.ucsd.edu/~jmcauley/datasets.html

3) Visual QA : https://visualqa.org/download.html

4) Vox Celebrity : https://www.robots.ox.ac.uk/~vgg/data/voxceleb/

5) Breast cancer classification : https://www.kaggle.com/martinab/breast-cancer-classification-wisconsin-dataset

6) Traffic signals : https://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset

7) Image caption generator : https://academictorrents.com/details/9dea07ba660a722ae1008c4c8afdd303b6f6e53b

What is the output of following Python Code?

6 Data Science Projects for your portfolio
 
1.   Predictive Analytics Project
Build a model to predict future outcomes based on historical data.
Skills Demonstrated: ML, data preprocessing, feature engineering, model evaluation.
2.   Time Series Analysis Project
Analyze time series data to identify trends, seasonal patterns, and anomalies. You could work on projects like stock market analysis.
Skills Demonstrated: Time series decomposition, forecasting models, data preprocessing
3.   Recommender System
Develop a recommendation engine for products, articles, songs any other items. You can use collaborative filtering, content-based filtering, or hybrid methods.
Skills Demonstrated: Recommendation algorithms, data preprocessing, model evaluation.
4.   Customer Segmentation Project
Use clustering algorithms to segment customers based on their behavior and characteristics. This could involve dividing customers into groups for targeted marketing.
Skills Demonstrated: Clustering algorithms (K-means, DBSCAN), data preprocessing, feature selection.
5.   Anomaly Detection Project
Develop a model to detect anomalies in data, such as fraud detection in financial transactions.
Skills Demonstrated: Anomaly detection techniques, data preprocessing, model evaluation.
6.   Churn Prediction for Subscription Services
Predict which customers are likely to cancel their subscriptions based on their usage patterns and other factors.
Skills Demonstrated: Machine learning, data preprocessing, feature engineering, model evaluation.

Join for more: https://t.iss.one/pythonspecialist