Starting today, I'm going to learn/review one ML topic everyday for the next year and try to note down the important details here!
- Day 1 - Overview of Machine Learning and ML Pipeline Process
- Day 2 - Exploratory Data Analysis - An Overview
- Day 3 - Data Quantity Requirement Analysis for ML
- Day 4 - Kinds of Data Variables in a Dataset
- Day 5 - Investigation of Data Variables in a Dataset
- Day 6 - Demystifying Duplicate Values, their Causes and Preventions
- Day 7-9 - Identifying and Dealing with Fully and Partial Duplicate Values
- Day 10-16 - Deep Dive into Near Duplicates and Record Linkage
- Day 17 - Applications of Near Duplicates in Entity Similarity
- Day 18 - Plagiarism Detection
- Day 19-20 - Recommender Systems
- Day 21-24 - Content Oriented Recommender Systems
- Day 25-30 - Overview of NLP Pre-processing Techniques
- Day 31-34 - Implementing Content Filtering Recommenders on arXiv Dataset
- Day 35-37 - Collaborative Oriented Recommender Systems
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