#100daysofmachinelearning search results

Day 74: ✅ Explored the foundation of gradient-free learning in Tangled Program Graphs ✅ 60-minute walk and dance💃🏻🔋 #100DaysofMachineLearning #AI

Day 30 of #100DaysOfMachineLearning I completed Exploratory Data Analysis. It included -> EDA in python -> Advance EDA ->Time Series Data Visualization. Off to Model Evaluation next..

Part 1 of Data Preprocessing of #100DaysOfMachineLearning These are the steps that we have to go through to deploy a machine learning model. Problem Definition ->Data Collection ->Data Cleaning & Preprocessing-> Exploratory Data Analysis (EDA) -> Feature Engineering & Selection

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 28 of #100DaysOfCode - Practice Python questions on Hacker rank. - Completed Pong Game project in Python #100DaysOfCode #100DaysOfMachineLearning

Day 88 of #100DaysofMachineLearning Topic - K-means Clustering in ML 🧵

Day 27 of #100DaysOfCode - Completed snake game project. - Solve 2 questions #100DaysOfCode #100DaysOfMachineLearning

Day 68 of #100DaysofMachineLearning Topic - ElasticNet Regression 🧵

#weekendwithdata 😌 #100DaysofMachineLearning

🚀 Day 19 of #100DaysOfMachineLearning: Explored Upper Confidence Bound (UCB) in Reinforcement Learning today! 🎲💡 UCB is a powerful algorithm for balancing exploration and exploitation in multi-armed bandit problems. #MachineLearning #DataScience #Connect #LearnInPublic #AI

Day 19 of #100DaysOfCode - Build a coffee Machine program in Python #100Daysofmachinelearning #100daysofcodechallenge

✅In Day7 of #100daysofmachinelearning I have read the concepts of plotting graphs that is #univariate #bivariate #multivariate

Day 18 of #100DaysOfCode - Solve some array based questions #100Daysofmachinelearning #100daysofcodechallenge

✅Day 15 of #100daysofmachinelearning i have come to the concept of #centrallimittheorem #machine_learning #datsets #Statistics #coding #DataScience #maths

🚀 Day 20 of #100DaysOfMachineLearning: Explored Thompson Sampling in Reinforcement Learning today! 🎲📈 It is a Bayesian approach to decision-making, balancing exploration & exploitation to maximize rewards in dynamic environments. #MachineLearning #Connect #LearnInPublic

Day 3 of 100days of ML🚩 1) Python: Today I have learn about advanced concepts, iterator, decorators in python. #Python #MachineLearning #100daysofmachinelearning

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 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

Part 1 of Data Preprocessing of #100DaysOfMachineLearning These are the steps that we have to go through to deploy a machine learning model. Problem Definition ->Data Collection ->Data Cleaning & Preprocessing-> Exploratory Data Analysis (EDA) -> Feature Engineering & Selection

Day 3 of #100DaysOfMachineLearning Completed two topics -> Matplotlib (some subtopics are difficult) -> Scikit-learn

Day 95 #100DaysOfMachineLearning I basically just went over some of the concepts I learnt already (word embedding techniques, negative sampling, and sentiment classification), but in more details. I was also able to complete the emojifier programming exercise.

🚀 Day 8 of #100DaysOfMachineLearning: Today, I delved into Logistic Regression for Classification - a versatile technique for binary classification tasks. Let's dive into its application! 📊🎯 #MachineLearning #DataScience #Connect #LearnInPublic #ArtificialIntelligence

Day 34 of #100DaysOfCode - Explore some methods and features of pandas in Python along with CSV files. - Solve a problem in DSA. #100daysofcoding #100DaysOfMachineLearning

📍Hello Folks ✅So Today I Start a Series on Machine Learning { Day- 1 }😃 #MachineLearning #100daysofMachinelearning #DataScience #100DaysOfCode @CodingMantras #100daysofcodechallenge #DataScientists #python #sql #powerbi @avikumart_ @avizyt @daansan_ml @NickSinghTech