#100daysofmachinelearning search results
Day 14 of my #100DaysOfMachineLearning โ Learned how to frame a problem the right way โ a crucial first step before building any ML model! Better questions โ better solutions ๐ค๐ก #MachineLearning #AI #DataScience #100DaysOfCode
Day 28 of #100DaysOfCode - Practice Python questions on Hacker rank. - Completed Pong Game project in Python #100DaysOfCode #100DaysOfMachineLearning
Day 10 of #100DaysOfMachineLearning ๐ Todayโs focus: Evaluation Metrics โ how to truly measure model performance. Accuracy โ Everything. Real ML understanding begins with: โ Precision โ Recall โ F1-Score โ AUC A model isnโt great because itโs accurate โ itโs greatโฆ
Day 9 of my #100DaysOfMachineLearning โ Learned about the Machine Learning Development Life Cycle (MLDLC) โ the step-by-step process of building, training, and deploying ML models! ๐๐ค #MachineLearning #AI #100DaysOfCode
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 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 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!๐
Day 27 of #100DaysOfCode - Completed snake game project. - Solve 2 questions #100DaysOfCode #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 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 Hereโs 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
Day 82 of #100DaysofMachineLearning Topic = Regression Tree - Decision Tree visualization with Dtreeviz ๐งต
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 99 #100DaysOfMachineLearning - Finished a programming assignment today. - 99% done with the Deep Learning course ๐๐
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 โ understanding how one feature can predict an outcome. 2. Moved to Multiple Linear Regression โ where things get more real.
Day 6 of #100DaysOfMachineLearning I learnt about pandas profiling and feature engineering today. Let's talk about Feature Engineering: โขIt is the process of turning raw data into useful features that help improve the performance of ml models. Continued..
Day 100 ๐ #100DaysOfMachineLearning After 5 months of taking 5 different courses with over 25 programming exercises and several hours of video materials, I've finally completed the Deep Learning Specialization ๐ฅณ๐.
Day 14 of my #100DaysOfMachineLearning โ Learned how to frame a problem the right way โ a crucial first step before building any ML model! Better questions โ better solutions ๐ค๐ก #MachineLearning #AI #DataScience #100DaysOfCode
Day 10 of #100DaysOfMachineLearning ๐ Todayโs focus: Evaluation Metrics โ how to truly measure model performance. Accuracy โ Everything. Real ML understanding begins with: โ Precision โ Recall โ F1-Score โ AUC A model isnโt great because itโs accurate โ itโs greatโฆ
Day 13 of my #100DaysOfMachineLearning โ Worked on an end-to-end toy project โ applying everything Iโve learned so far! Great hands-on experience to strengthen my ML fundamentals ๐ค๐ช #MachineLearning #AI #DataScience #100DaysOfCode
Day 9 of #100DaysOfMachineLearning ๐ง Todayโs topic: Classification โ how AI learns to make decisions, not just predictions. From spam filters to fraud detection to facial recognition โ classification helps machines separate data into categories based on patterns. ๐ Free toโฆ
Day 12 of my #100DaysOfMachineLearning โ Set up my ML environment today! ๐ง ๐ป Downloaded Anaconda and explored Jupyter Notebook, Google Colab, and Kaggle โ ready to code, learn, and experiment! ๐ #MachineLearning #AI #DataScience #100DaysOfCode
Day 11 of my #100DaysOfMachineLearning โ Learned about Tensors โ the core data structures in ML! Explored examples of 1D to 5D tensors and how they represent data in multiple dimensions. ๐ข๐ค #MachineLearning #AI #DeepLearning #100DaysOfCode
Day 10 of my #100DaysOfMachineLearning โ Explored the various job roles in Machine Learning โ from Data Scientist to ML Engineer, AI Researcher, and more. So many exciting paths ahead! ๐๐ค #MachineLearning #AI #100DaysOfCode
Day 9 of my #100DaysOfMachineLearning โ Learned about the Machine Learning Development Life Cycle (MLDLC) โ the step-by-step process of building, training, and deploying ML models! ๐๐ค #MachineLearning #AI #100DaysOfCode
Day 28 of #100DaysOfCode - Practice Python questions on Hacker rank. - Completed Pong Game project in Python #100DaysOfCode #100DaysOfMachineLearning
Day 14 of my #100DaysOfMachineLearning โ Learned how to frame a problem the right way โ a crucial first step before building any ML model! Better questions โ better solutions ๐ค๐ก #MachineLearning #AI #DataScience #100DaysOfCode
Day 13 of my #100DaysOfMachineLearning โ Worked on an end-to-end toy project โ applying everything Iโve learned so far! Great hands-on experience to strengthen my ML fundamentals ๐ค๐ช #MachineLearning #AI #DataScience #100DaysOfCode
Day 27 of #100DaysOfCode - Completed snake game project. - Solve 2 questions #100DaysOfCode #100DaysOfMachineLearning
Day 9 of my #100DaysOfMachineLearning โ Learned about the Machine Learning Development Life Cycle (MLDLC) โ the step-by-step process of building, training, and deploying ML models! ๐๐ค #MachineLearning #AI #100DaysOfCode
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
๐ Day 19 of #100DaysOfMachineLearning: Explored Upper Confidence Bound (UCB) in Reinforcement Learning today! ๐ฒ๐ก 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 12 of my #100DaysOfMachineLearning โ Set up my ML environment today! ๐ง ๐ป Downloaded Anaconda and explored Jupyter Notebook, Google Colab, and Kaggle โ ready to code, learn, and experiment! ๐ #MachineLearning #AI #DataScience #100DaysOfCode
โ Day 15 of #100daysofmachinelearning i have come to the concept of #centrallimittheorem #machine_learning #datsets #Statistics #coding #DataScience #maths
Day 18 of #100DaysOfCode - Solve some array based questions #100Daysofmachinelearning #100daysofcodechallenge
Day 10 of my #100DaysOfMachineLearning โ Explored the various job roles in Machine Learning โ from Data Scientist to ML Engineer, AI Researcher, and more. So many exciting paths ahead! ๐๐ค #MachineLearning #AI #100DaysOfCode
Day 11 of my #100DaysOfMachineLearning โ Learned about Tensors โ the core data structures in ML! Explored examples of 1D to 5D tensors and how they represent data in multiple dimensions. ๐ข๐ค #MachineLearning #AI #DeepLearning #100DaysOfCode
๐Hello Folks โ So Today I Start a Series on Machine Learning { Day- 1 }๐ #MachineLearning #100daysofMachinelearning #DataScience #100DaysOfCode @CodingMantras #100daysofcodechallenge #DataScientists #python #sql #powerbi @avikumart_ @avizyt @daansan_ml @NickSinghTech
๐ Day 20 of #100DaysOfMachineLearning: Explored Thompson Sampling in Reinforcement Learning today! ๐ฒ๐ It is a Bayesian approach to decision-making, balancing exploration & exploitation to maximize rewards in dynamic environments. #MachineLearning #Connect #LearnInPublic
Day 34 of #100DaysOfCode - Explore some methods and features of pandas in Python along with CSV files. - Solve a problem in DSA. #100daysofcoding #100DaysOfMachineLearning
Day 31 #100DaysOfCode - Learn about file management system in Python. - Solve some questions . #100daysofcoding #100DaysofMachineLearning
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