I will do python programming and ai devoloper apps, machine learning, data science
I will do python programming and ai devoloper apps, machine learning, data science
WebWith more organizations developing AI-based applications, it’s essential to use a programming language that reduces the complexity of code
Get I will do python programming and ai devoloper apps, machine learning, data science
That’s great! There are many resources available online to learn Python programming and AI development. You can start with the Python for Data Science, AI and Development course on Fiverr1 which teaches the basics of Python and begins by exploring some of the different data types such as integers, real numbers, and strings.
You can also check out Machine Learning in Python (Data Science and Deep Learning) on Udemy2 which is designed to help you master the essential concepts of machine learning and deep learning using Python.
Another option is Introduction to Data Science, Machine Learning and AI using Python on Learning Tree3 which teaches participants how to use Python libraries to build, evaluate, and deploy Machine Learning (ML) and Artificial Intelligence (AI) models to gain insights from data.
Data science is a field that studies data and how to extract meaning from it, whereas machine learning is a field devoted to understanding and building methods that utilize data to improve performance or inform predictions. Machine learning is a branch of artificial intelligence1.
In other words, data science is the process of extracting insights from data using various techniques such as data mining, machine learning, and statistical analysis.
Machine learning is a subset of artificial intelligence that involves training algorithms to make predictions or decisions based on data2.
There are many popular libraries used in Python for machine learning. Some of the most popular ones include:
- NumPy: A popular Python library for multi-dimensional array and matrix processing because it can be used to perform a variety of mathematical operations on arrays12.
- Scikit-learn: A very popular machine learning library that is built on NumPy and SciPy. It supports supervised and unsupervised learning and provides various tools for model selection, data preprocessing, and data analysis12.
- Pandas: A library that provides data structures for efficiently storing large datasets and tools for data manipulation and analysis12.
- Theano: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently1.
- TensorFlow: An open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks1.
- Keras: A high-level neural networks API that is written in Python and capable of running on top of TensorFlow1.
- PyTorch: An open source machine learning library based on the Torch library. It is used for applications such as natural language processing1.
