Machine learning

Question

What languages are needed for machine learning?

Answer 1

Python is currently the best language for that right now because of its powerful and countless libraries

Answer 2

You need to learn machine learning libraries like tensorflow and pytorch for training the models, matplotlib for the visualization, numpy for converting it into machine learning format, pandas for accessing the datasets like json or csv files, nltk for natural language, opencv for images and videos and many more.

Answer 3

Python, C++, and R is useful for machine learning. Bash is also really useful for speeding up and automating tasks, especially if you chain it with other codes like Python scripts.

Answer 4

Python, C++, and R is useful for machine learning. Bash is also really useful for speeding up and automating tasks, especially if you chain it with other codes like Python scripts.

Answer 5

Python > R
Then comes C++ and JS

Answer 6

.NET works well with ML, there is ML.NET which when used with C# can have great results, and MSoft is doing a lot with Python these days as well due to the rise of agentic programming.

Answer 7

1. Python (Most Popular)
Why: Simple syntax and massive libraries like Pandas, NumPy, scikit-learn, TensorFlow, and PyTorch.
Use: Data cleaning, analysis, visualization, model building, deep learning
2. R
Use: DWhy: Designed for statistics and data visualization.
data analytics, forecasting, hypothesis testing, creating rich plots with ggplot2 and Shiny dashboards.
3. Java
Why: Strong performance and scalability for big data solutions.
Use: Enterprise-level machine learning and analytics systems (e.g., Apache Spark with Java APIs).
4. JavaScript
Why: Useful for interactive data dashboards and web-based ML models.
Use: Visualizing analytics in the browser, building real-time ML-enabled applications using TensorFlow.js.
5. C++
Why: High-performance computation and control.
Use: Building low-latency data analytics engines and ML algorithms behind the scenes.
6. Julia
Why: Fast and math-friendly.
Use: Ideal for scientific computing, heavy data analysis, and modeling.
7. SQL (Must-Have for Data Analytics)
Why: Core skill for querying, filtering, and joining datasets in analytics workflows.
Use: ETL processes, business intelligence, reporting, and preparing data for ML models.
For beginners or professionals, Python is the go-to language for both machine learning and data analytics—easy to learn, flexible, and supported by a massive community.

Answer 8

python

Answer 9

Python is the most needed