#100daysofmachinelearning search results
Day 28 of #100DaysOfCode - Practice Python questions on Hacker rank. - Completed Pong Game project in Python #100DaysOfCode #100DaysOfMachineLearning
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..
Day 27 of #100DaysOfCode - Completed snake game project. - Solve 2 questions #100DaysOfCode #100DaysOfMachineLearning
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 3 of #100DaysOfMachineLearning Completed two topics -> Matplotlib (some subtopics are difficult) -> Scikit-learn
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 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:
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 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 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 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 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 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 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 82 of #100DaysofMachineLearning Topic = Regression Tree - Decision Tree visualization with Dtreeviz 🧵
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 74: ✅ Explored the foundation of gradient-free learning in Tangled Program Graphs ✅ 60-minute walk and dance💃🏻🔋 #100DaysofMachineLearning #AI
🚀 Starting my #100DaysOfMachineLearning journey with CampusX! A new chapter begins — 100 days of consistency, learning, and growth in AI & ML . Let’s build the future, one concept at a time. 💡🤖
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 28 of #100DaysOfCode - Practice Python questions on Hacker rank. - Completed Pong Game project in Python #100DaysOfCode #100DaysOfMachineLearning
Day 27 of #100DaysOfCode - Completed snake game project. - Solve 2 questions #100DaysOfCode #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
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
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 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
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 3 of #100DaysOfMachineLearning Completed two topics -> Matplotlib (some subtopics are difficult) -> Scikit-learn
🚀 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
Day 31 #100DaysOfCode - Learn about file management system in Python. - Solve some questions . #100daysofcoding #100DaysofMachineLearning
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