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This is a personal learning log by Avik Jain documenting 100 days of machine learning coding, following the #100DaysOfMLCode challenge proposed by Siraj Raval. It records daily progress on ML topics with notes, code, and infographics.
This is a personal learning log by Avik Jain documenting 100 days of machine learning coding, following the #100DaysOfMLCode challenge proposed by Siraj Raval. It records daily progress on ML topics with notes, code, and infographics.Is this a library or a learning log?It's not a library or tool — it's a day-by-day diary with short write-ups, links to code implementations, and infographics for each ML topic covered.
It's not a library or tool — it's a day-by-day diary with short write-ups, links to code implementations, and infographics for each ML topic covered.How is the log organized and where should I start?Entries are organized chronologically by day (Day 1 through Day 54+), each tagged with a topic heading like 'Simple Linear Regression' or 'K Means Clustering'. A beginner should start at Day 1 (Data PreProcessing) and read through in order since later days build on earlier concepts like linear algebra and calculus.
Entries are organized chronologically by day (Day 1 through Day 54+), each tagged with a topic heading like 'Simple Linear Regression' or 'K Means Clustering'. A beginner should start at Day 1 (Data PreProcessing) and read through in order since later days build on earlier concepts like linear algebra and calculus.Do I need Python or specific libraries to follow along?Yes — the code examples use Python with scikit-learn for most classical ML algorithms (SVM, Naive Bayes, Decision Trees, K-NN), plus NumPy, Pandas, and Matplotlib for data manipulation and visualization later on.
Yes — the code examples use Python with scikit-learn for most classical ML algorithms (SVM, Naive Bayes, Decision Trees, K-NN), plus NumPy, Pandas, and Matplotlib for data manipulation and visualization later on.Is every day's entry equally detailed with code?No — some days are just infographics or notes on external video lectures and courses with no accompanying code, while only select days link to actual implementation code.
No — some days are just infographics or notes on external video lectures and courses with no accompanying code, while only select days link to actual implementation code.Who is this log useful for?It's aimed at self-learners who want a roadmap of what to study day-by-day when starting out in machine learning, covering topics from regression and classification to neural networks and deep learning frameworks.
It's aimed at self-learners who want a roadmap of what to study day-by-day when starting out in machine learning, covering topics from regression and classification to neural networks and deep learning frameworks.What topics are covered across the 100 days?The log covers data preprocessing, linear and logistic regression, SVM, K-NN, Naive Bayes, decision trees, random forests, K-means clustering, hierarchical clustering, linear algebra and calculus refreshers, and deep learning basics with TensorFlow/Keras.
The log covers data preprocessing, linear and logistic regression, SVM, K-NN, Naive Bayes, decision trees, random forests, K-means clustering, hierarchical clustering, linear algebra and calculus refreshers, and deep learning basics with TensorFlow/Keras.Where do the datasets come from?The author links to a separate source for datasets used across the exercises, though the exact dataset repository link is embedded in the README rather than described in detail.
The author links to a separate source for datasets used across the exercises, though the exact dataset repository link is embedded in the README rather than described in detail.Who is Siraj Raval and why does he matter here?Siraj Raval proposed the original #100DaysOfMLCode challenge that inspired this log, and the author references his challenge as the motivation for the daily coding habit.
Siraj Raval proposed the original #100DaysOfMLCode challenge that inspired this log, and the author references his challenge as the motivation for the daily coding habit.Does the author recommend any external resources?Yes — the log repeatedly points to outside resources like 3Blue1Brown's YouTube series on linear algebra and calculus, Bloomberg's ML lecture series, Caltech's CS 156 course, and the book 'Python Data Science Handbook' by Jake VanderPlas.
Yes — the log repeatedly points to outside resources like 3Blue1Brown's YouTube series on linear algebra and calculus, Bloomberg's ML lecture series, Caltech's CS 156 course, and the book 'Python Data Science Handbook' by Jake VanderPlas.What are similar projects?100-Days-Of-ML-Code, ailearning, python-machine-learning-book, cheatsheets-ai, PythonDataScienceHandbook, Virgilio, machine-learning-zoomcamp, MLAlgorithms
100-Days-Of-ML-Code, ailearning, python-machine-learning-book, cheatsheets-ai, PythonDataScienceHandbook, Virgilio, machine-learning-zoomcamp, MLAlgorithmsWhich topics is it in?svm, machine-learning-algorithms, linear-algebra, scikit-learn, pandas, jupyter-notebook, numpy, neural-network, data-science, learning, tutorial, curated-list
svm, machine-learning-algorithms, linear-algebra, scikit-learn, pandas, jupyter-notebook, numpy, neural-network, data-science, learning, tutorial, curated-list