Search results for #100DaysOfMachineLearning
Finished my MERN full-stack journey and ready for the next big leap 🚀 Starting #100DaysOfMachineLearning with @CAMPUSX 🤖 From building websites to building smart systems… let’s see where this takes me! 🙌 Who’s learning ML too? Let’s connect! #AI #ML #CampusX #LearningTogether
Week 3 of my #100DaysOfMachineLearning has been intense! From Day 15 to Day 22, these are some topics that I did: 1. Explored Simple Linear Regression – understanding how one feature can predict an outcome. 2. Moved to Multiple Linear Regression – where things get more real.
Day 14 of #100DaysOfMachineLearning Completed these within the last few days- 1. Complete case analysis 2. Arbitrary value imputation 3. Missing categorical value 4. Automatically select imputer parameters 5. KNN Imputer 6. Outlier removal using Z Score
Day 13 of #100DaysOfMachineLearning Here’s what I coded in the past few days: 1. Handling missing categorical data 2. Doing a complete case analysis (basically dropping rows with missing values) 3.Trying out arbitrary value imputation
Day 12 of #100DaysOfMachineLearning I did these topics within the last 3 days: 1. From Statistics, I did- probability distribution function (pdf, pmf, cdf) and Normal distribution. 2. I did 6 cases of handling missing data using:
Day 11 of #100DaysOfMachineLearning I started diving into maths for machine learning. I've always heard that Statistics is a non-negotiable for machine learning. Although was never particularly a very big fan of this subject, but I did these topics under Descriptive Statistics
Day 10 of #100DaysOfMachineLearning So, I coded the stuff learnt the previous day( column Transformer, sklearn Pipelines, function transformer, power transformer). I started the theory of machine learning from geeksforgeeks too. I really like this site for concept clarity.
Day 9 of #100DaysOfMachineLearning Honestly in the previous week, I wasn't able to post consistently because of personal issues. Now, that everything is fine by my side, I'll continue with this challenge again daily. So, yesterday I did the theory of ✔️ One Hot Encoding
Day 8 of #100DaysOfMachineLearning Honestly this week was super difficult for me. My mom was admitted to the hospital , so I was mostly at the hospital. Good thing, she'll be discharged tomorrow.🙏 Didn't get time to continue with my 100 day challenge.
Day 7 of #100DaysOfMachineLearning I started the OG of machine learning - Hands on ML with Scikit-Learn, Teras and TensorFlow Did the 1st chapter - Fundamentals of Machine Learning. Also completed 3 videos about understanding data some days back. I forgot to mention before.
Day 6 of #100DaysOfMachineLearning I learnt about pandas profiling and feature engineering today. Let's talk about Feature Engineering: •It is the process of turning raw data into useful features that help improve the performance of ml models. Continued..
Day 5 Part 2 of #100DaysOfMachineLearning So, I had the dataset of IPL Squad 2023 Auction. I analyzed it. any type of improvements/suggestions are always welcome! what more details could I have fetched from it? (Details about it in comments) Off to Day 6 tomorrow!🚀
Day 5 of #100DaysOfMachineLearning It was all about data. Learnt about 1. Working with CSV files 2. Handling JSON/SQL 3. Fetching data from APIs 4. Web scraping( okay, so this felt illegal at first but what an interesting topic!) Geeksforgeeks->highly recommended for concepts
Day 4 of #100DaysOfMachineLearning The steps which I implemented in the Zomato analysis project: 1. Loaded the data from csv file. 2. Performed data cleaning: -> converted the fraction column into a float one using handleRate function 3. Identified the popular restaurants
Day 4 of #100DaysOfMachineLearning In the previous weeks, I learnt about libraries . I implemented whatever I learnt in the Zomato Data Analysis project( of course, took lil' help too since it was my first project) Uploading it here, and the steps are in the comments.
Day 3 of #100DaysOfMachineLearning I got my laptop back after repair, yay! -> So, I read about Seaborn. Will practice it soon. -> Completed Day 16 of the CampusX ML course. -> Created my GitHub account. Now, I'll start practicing whatever is taught in the 100 Days ML module.
Day 3 of #100DaysOfMachineLearning Completed two topics -> Matplotlib (some subtopics are difficult) -> Scikit-learn
Day 1: #100DaysofMachineLearning journey Diving into linear algebra fundamentals! Today's focus: systems of linear equations, matrices, and their operations. Challenge details: lnkd.in/ddxR6nQt All credits to @geeksforgeeks 💪 #100DaysOfML #100DaysOfCode #ML
Starting the #100DaysOfMachineLearning challenge by @geeksforgeeks today! 👨💻 Sharpening my ML skills from fundamentals to advanced concepts. I would love to have fellow learners join me on this journey! 💪 Details: lnkd.in/ddxR6nQt #MachineLearning #DataScience #ML
That’s a wrap for Day 19! 🎬 Today, we asked basic questions about our data using simple Python commands. Tomorrow (Day 20): We’ll dive into EDA - Univariate Analysis! 🚀 Stay tuned! 👋 #100DaysOfMachineLearning #DataScience #AI #LearnInPublic