Welcome to the 100 Days of Machine Learning series — one of the most watched and most trusted and Evergreen Machine Learning playlists on Indian YouTube, followed by millions of learners who want to build a strong foundation in ML. This playlist is designed as a step-by-step roadmap to master Machine Learning, starting from the absolute basics and gradually moving toward advanced concepts and real-world implementation. Instead of jumping directly into code, the series focuses on building clear intuition, strong fundamentals, and practical understanding of how machine learning actually works. What you will learn in this series: • AI vs Machine Learning vs Deep Learning • Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning) • Data preprocessing and feature engineering • Exploratory Data Analysis (EDA) • Important ML algorithms (Regression, Classification, Clustering, etc.) • Model evaluation and validation techniques • Real-world ML workflow and best practices Each video covers a specific concept in a simple and structured way so you can build knowledge one step at a time, just like a 100-day learning roadmap. If you want to build a strong Machine Learning foundation for Data Science, AI, or ML Engineering, this playlist will guide you from beginner concepts to industry-level understanding. Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes
Curated by: CampusX (134 videos)
The preliminary analysis of data to discover relationships between measures in the data and to gain an insight on the trends, patterns, and relationships among various entities present in the data set with the help of statistics and visualization tools is called Exploratory Data Analysis (EDA). Exploratory data analysis is cross-classified in two different ways where each method is either graphical or non-graphical. And then, each method is either univariate, bivariate or multivariate. Uni means one and variate means variable, so in univariate analysis, there is only one dependable variable. The objective of the univariate analysis is to derive the data, define and summarize it, and analyze the pattern present in it. In a dataset, it explores each variable separately. It is possible for two kinds of variables- Categorical and Numerical. Code used : https://github.com/campusx-official/100-days-of-machine-learning/tree/main/day20-univariate-analysis ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 Instagram: https://www.instagram.com/campusx.official E-mail us at support@campusx.in ⌚Time Stamps⌚ 00:00 - Intro 00:52 - What is univariate Analysis 02:00 - Types of Data 03:50 - Code Demo on Titanic Dataset 12:05 - Countplot 13:55 - Piechart 15:56 - Working with Numerical Data 19:40 - Distplot 22:05 - Boxplot
Automatically track which videos you have watched. Your completion status is updated at a glance, preventing you from re-watching episodes by mistake.
Never lose your spot. Our custom player remembers your exact video and timestamp, allowing you to dive right back in seamlessly.
Sync your playlist states, watched progress, and premium preferences across your desktop, laptop, tablet, and mobile phone automatically.
Simply paste any YouTube playlist URL or channel link in the application search bar to immediately generate a custom, sorted, and progress-tracked workspace. No registration required to start.
Explore Playlist Guides & How-Tos