The 100 Days of Machine Learning is a challenge for individuals to learn and explore the fundamentals of machine learning. It is a self-paced, hands-on approach to learning the basics of artificial intelligence (AI) and machine learning (ML). By taking this challenge, individuals can become better equipped to use ML solutions for real-world application.
Table Of Content:
- Avik-Jain/100-Days-Of-ML-Code
- 100 Days of ML Coding ️ | Data Science and Machine Learning ...
- 100 DAYS OF MACHINE LEARNING CODE: My Journey to ML | by ...
- Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation ...
- Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation ...
- My 100 Days Of ML Code Journey. Machine learning is a subset of ...
- Journey to Machine Learning – 100 Days of ML Code - KDnuggets
- 100 Days of Artificial Intelligence | by Alex Moltzau | Towards Data ...
- The 100 Days of ML Code Challenge : r/learnmachinelearning
- #100DaysOfCode: Machine Learning & Data Visualization ...
1. Avik-Jain/100-Days-Of-ML-Code
https://github.com/Avik-Jain/100-Days-Of-ML-CodeStarted Deep learning Specialization on Coursera | Day 17. Completed the whole Week 1 and Week 2 on a single day. Learned Logistic regression as Neural Network.
2. 100 Days of ML Coding ️ | Data Science and Machine Learning ...
https://www.kaggle.com/general/255115
100 Days of Machine Learning Coding as proposed by Siraj Raval. Data PreProcessing. Simple Linear Regression. Multiple Linear Regression.
3. 100 DAYS OF MACHINE LEARNING CODE: My Journey to ML | by ...
https://becominghuman.ai/100-days-of-machine-learning-code-my-journey-to-ml-c490dfeb72aa
100 DAYS OF MACHINE LEARNING CODE: My Journey to ML ... Remember all those goals you set at the start of the year? Chances are, there are at least a couple of ...
4. Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation ...
https://pubmed.ncbi.nlm.nih.gov/26240227/
... score for the prediction of day 100 OM, extending prediction to 2 yea … ... Mortality 100 Days After Transplantation Using a Machine Learning Algorithm: ...
5. Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation ...
https://ascopubs.org/doi/10.1200/JCO.2014.59.1339
Aug 3, 2015 ... Machine learning algorithms, which are part of the . ... Transplantation Mortality 100 Days After Transplantation Using a Machine Learning ...
6. My 100 Days Of ML Code Journey. Machine learning is a subset of ...
https://medium.com/tanay-toshniwal/my-100-days-of-ml-code-journey-5b39692501c8
Siraj Raval recently proposed a challenge #100DaysOfMLCode. Anyone who wants to contribute can participate in this journey of 100 days. It's a pledge to ...
7. Journey to Machine Learning – 100 Days of ML Code - KDnuggets
https://www.kdnuggets.com/2018/09/journey-machine-learning-100-days.html
Sep 7, 2018 ... A personal account from Machine Learning enthusiast Avik Jain on his experiences of #100DaysOfMLCode, a challenge that encourages beginners ...
8. 100 Days of Artificial Intelligence | by Alex Moltzau | Towards Data ...
https://towardsdatascience.com/100-days-of-artificial-intelligence-3a38c75b6a5d
Sep 11, 2019 ... 100 days dedicated to machine learning for social scientists with qualitative as well as quantitative methods sounds like a fun challenge ...
9. The 100 Days of ML Code Challenge : r/learnmachinelearning
https://www.reddit.com/r/learnmachinelearning/comments/969rrs/the_100_days_of_ml_code_challenge/
Aug 10, 2018 ... I took a Challenge by Siraj Raval called #100DaysOfMLCode, Under which the challenge taker is supposed to learn or code Machine Learning at ...
10. #100DaysOfCode: Machine Learning & Data Visualization ...
https://paulvanderlaken.com/2019/01/22/100daysofcode-machine-learning-data-visualization/
Jan 22, 2019 ... 1. Code minimum an hour every day for the next 100 days. 2. Tweet your progress every day with the #100DaysOfCode hashtag. 3. Each ...
What is the 100 Days of Machine Learning?
The 100 Days of Machine Learning is a challenge designed to help individuals learn and explore the fundamentals of machine learning. It encourages people to develop their understanding of AI and ML concepts through hands-on practice.
Who should take this challenge?
Anyone interested in increasing their knowledge and skills in the field of AI and ML can benefit from taking on this challenge. It offers an opportunity for beginners to get started with machine learning, as well as experienced practitioners looking to strengthen or update their skills.
What types of technologies are covered in this challenge?
This challenge covers several aspects of AI and ML, such as natural language processing (NLP), computer vision (CV), supervised & unsupervised learning algorithms, deep learning models, data preprocessing & visualisation techniques, reinforcement learning strategies, etc.
How much time will it take to complete this challenge?
Completing all the tasks within the timeframe provided by the 100 Days Of Machine Learning Challenge will be highly dependent on each individual’s experience level in AI/ML and also their time commitment towards completing the tasks effectively. Generally speaking, it would likely take around 8–10 hours per week for approximately 3 months in order to complete all tasks suggested in this challenge.
Are there any resources available to help me during the challenge?
Yes! During your journey into machine learning with the 100 Days Of Machine Learning Challenge you will have access to several helpful resources such as tutorials & lectures from industry experts, hands-on coding activities with real world datasets & projects – some examples include building an image classification model using CNNs or building an online recommender system for streaming platforms like Netflix/Hulu etc. Additionally participants will have access to discussion forums where they can connect with fellow students seeking help or advice on topics related to AI/ML.
Conclusion:
The 100 Days Of Machine Learning Challenge is a great way for individuals wanting to learn more about AI and ML technologies while developing their own knowledge and practical skills at home or in a classroom setting. With access to useful resources such as tutorials & lectures from industry experts along with engaging coding activities – participants can gain valuable insights while having fun!
