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I will do data science, ai, machine learning, modelling projects or rstudio in python

I will do data science, ai, machine learning, modelling projects or rstudio in python

Here are some open-source projects that you can work on to learn more about data science, AI, machine learning, and modelling in Python:

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  • Kaggle: Kaggle is a platform that hosts data science competitions and provides datasets for practice. You can participate in competitions and work on real-world problems to improve your skills.
  • Dataquest: Dataquest offers interactive courses on data science and machine learning with Python. They also have a blog post that lists the top 20 Python AI and machine learning open-source projects1.
  • ProjectPro: ProjectPro has a list of 20 Python projects for data science in 20232. These projects cover topics such as data cleaning, manipulation, analysis, and machine learning.

There are many ways to get started with data science! Here are some tips that might help you:
  • Learn SQL: SQL is a query language that is used to manage and manipulate data in relational databases. It’s a good place to start if you’re new to data science1.
  • Learn a programming language: Python and R are two popular programming languages used in data science. They have many libraries and tools that make it easy to work with data1.
  • Learn basic statistics: Statistics is an important part of data science. You should learn concepts like variability, correlations, and basic probability theory2.
  • Learn machine learning algorithms: Machine learning is a subfield of data science that involves building models that can learn from data. You should learn about different types of machine learning algorithms and how they work2.
  • Practice and implementation: Practice is key when it comes to data science. You should work on projects and practice implementing what you’ve learned1.
There are many popular data science libraries in Python! Here are some of them:
  • NumPy: NumPy is a library that supports large multidimensional array objects and operations, which are essential for complex machine learning algorithms1.
  • SciPy: SciPy is an extension of NumPy that provides user-friendly and efficient routines for scientific and technical computing1.
  • Pandas: Pandas is considered to be one of the most popular Python libraries for data manipulation and analysis2.
  • Scikit-Learn: Scikit-Learn is a machine learning library that provides simple and efficient tools for data mining and data analysis3.
  • TensorFlow: TensorFlow is a library for high-performance numerical computations and deep learning1.
Sure! Here are some books on data science in Python that you might find helpful:

  • Python for Data Analysis by Wes McKinney: This book provides an introduction to data analysis using Python and Pandas.
  • Data Science from Scratch by Joel Grus: This book provides an introduction to data science using Python and covers topics such as statistics, machine learning, and data visualization.
  • Python Data Science Handbook by Jake VanderPlas: This book provides an introduction to data science using Python and covers topics such as NumPy, Pandas, Matplotlib, and Scikit-Learn.
  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron: This book provides an introduction to machine learning using Scikit-Learn, Keras, and TensorFlow.
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