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I will do data science, ai, machine learning and deep learning projects

I will do data science, ai, machine learning and deep learning projects

Artificial intelligence (AI) and machine learning (ML) are often used interchangeably, but machine learning is a subset of the broader category of AI1. 

Get data science, ai, machine learning and deep learning projects

There are many projects that you can do in data science, AI, machine learning and deep learning. Here are some project ideas that you can try:

  1. Titanic Survival Project: Use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.
  2. Image Classification Project: Use deep learning to classify images into different categories.
  3. Sentiment Analysis Project: Use machine learning to analyze the sentiment of text data.
  4. Fraud Detection Project: Use machine learning to detect fraudulent transactions.
  5. Recommendation System Project: Use machine learning to recommend products or services to users based on their preferences.
You can find more project ideas and tutorials on websites like Kaggle1, Coursera2, and Dev3.

In simplest terms, AI is computer software that mimics the ways that humans think in order to perform complex tasks, such as analyzing, reasoning, and learning2. Machine learning, meanwhile, is a type of AI that uses algorithms trained on data to produce models that can perform such complex tasks2.

In other words, machine learning is a way of achieving artificial intelligence3. Machine learning algorithms learn from data and improve their performance over time without being explicitly programmed1.

Some of the most popular deep learning frameworks today are TensorFlow, PyTorch, Keras, SciKit-Learn (SKLearn), Apache MXNet, Eclipse Deeplearning4j (DL4J), MATLAB, and Sonnet1.

TensorFlow is a popular choice for image recognition and classification2, while Keras is a good option for building simple, concise models2. PyTorch is a newer framework that’s growing in popularity for its flexibility and ease of use2.

There are many ways to get started with data science! Here are some steps you can take:

  1. Learn the basics of statistics and probability theory.
  2. Learn how to code in Python or R.
  3. Learn how to use data analysis tools like Pandas, Numpy, and Matplotlib.
  4. Learn how to use machine learning libraries like Scikit-Learn and TensorFlow.
  5. Practice on real-world datasets.

There are many online courses and resources available to help you learn data science. Some popular ones include Coursera, edX, Udacity, DataCamp, and Kaggle.

Do you need for you assignment or project to be developed in a way that you and any other team members could understand and deploy an AI-based application with useful and clear insights, as well as with step-by-step guides? If so, you already have found the correct match!

Hi, here is your next Data Scientist professional ready for your inquiries! I develop ML/DL solutions for you, in which documentation and code understanding and clear conclusions are important keys for a well-structured and reliable project.

Main programming languages with industrial-expertise domain:

  • Python (Data Science field)
  • MATLAB (Modeling and simulation)
  • PostgreSQL (Database query)

Data Science tools in which I have a solid background:

  • Pandas
  • Sklearn (Machine Learning domain)
  • NER/SpaCy (Deep Learning and NLP domain)
  • Weight & Biases (for data visualization and model optimization purposes)
  • Scrapy/HTML/CSS (for scraping techniques)
  • Docker (container management)
  • Some tools and methodologies for Version Control Systems:

Git and GitHub

Scrum (professional Agile methodology)

Let's get in touch regarding any inquiries you have in mind! I'm willing to work with you and expand my skillset! Thanks in advance!

Academics Level : $75

Enterprise Level : $1,000

Long-term Level : $3,200

Similar to Enterprise Level, but with timelapses of 1 month or more

data science, ai, machine learning and deep learning projects | Fiverr