code image-classification en dataset:teknium/OpenHermes-2.5 license:mit region:us
---
license: mit
datasets:
- teknium/OpenHermes-2.5
language:
- en
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- code
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d351977d6147c2ced39549/Mc6dcD-633aFORzcMLNu8.png)


# Table of contents
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   - Introduction to Machine learning:
    - Analytics
    - Categories of ML algorithms
    - A typical ML algorithm uses the following steps
    - Framework for develping ML models 
  - **Machine learning Fundamentals and application:** This repository provides a comprehensive introduction to machine learning concepts, algorithms, and applications. It covers the essential theoretical foundations of machine learning, as well as practical examples and hands-on projects to solidify your understanding.
  - **Deep Learning:** Delve into the world of deep learning, exploring its theoretical underpinnings and applying it to real-world problems. This folder contains notebooks and related files that guide you through the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks.
  - **Projects-ML:** Hands-on experience is invaluable for mastering machine learning. This folder offers a collection of sample projects that apply the concepts and algorithms discussed in the other folders. These projects provide practical exposure to machine learning techniques and allow you to apply your knowledge to real-world scenarios.
  - **Statistical fundamentals:** Statistics plays a pivotal role in machine learning. This folder delves into the intricacies of statistics, particularly inferential and descriptive statistics. It provides detailed explanations of statistical concepts and introduces applications of statistics in machine learning.
  - **ML-Data:** Datasets are indispensable for machine learning projects. This folder contains the data files used in the sample projects, ensuring that you have access to the relevant data to replicate and experiment with the provided examples.
  - References
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<!---------------------------- Reference ------------------------------>
# Reference

Individual references are given in individual notebooks. Here are few general references that one can look at:

1. Machine learning using python, Manarajan Pradhan, U Dinesh Kumar
2. Please follow lecture series provided at [Jovian.ml](https://jovian.ai/), i.e. [Data Analysis with Python: Zero to Pandas](https://jovian.ai/learn/data-analysis-with-python-zero-to-pandas)
3. [Python roadmap](https://roadmap.sh/python/)
4. [Python tutorials at w3school](https://www.w3schools.com/python/default.asp)
5. [30 Days of Python](https://github.com/asabeneh/30-days-of-python)
6. [Python official documents](https://docs.python.org/3/tutorial/)
7. [Data science roadmaps](https://github.com/codebasics/py/blob/master/TechTopics/DataScienceRoadMap2020/data_science_roadmap_2020.md)
8. Statistics, Murray R. Spiegel, Larry J. Stephens