Manish Kumar mahto’s Post

🚀 Day 54 of #180DaysofDataScience 🚀 🔸 Statistics: Today's Adventure: 🔍 What I Explored: Discrete Uniform Distribution: --> A distribution where all outcomes has equal chances of occurrence 📚 Key Takeaways: --> Applicable to discrete random variables --> Ex: Dice roll, where the probability of all the outcomes is same What's Next: 🔮 Upcoming Exploration: --> Tomorrow, I will continue with Data Distributions #DataScience #180DaysOfData #LearningJourney #TechExploration #DataScienceCommunity #StayCurious

Day 221 of My Data Science Journey 🚀 Today, I delved into the fascinating world of Feature Selection. 🌟 - Why Feature Selection? 🤔 - Types of Feature Selection 🛠️ - Filter-Based Feature Selection 📊 - Variance Threshold Method 🎯 - Correlation 🔗 - ANOVA 📈 - Chi-Square 🎲 Each of these concepts helps in refining the model by selecting the most relevant features, making the model both efficient and accurate! 💪 Excited to put this into practice and see the impact on my models. Onward and upward! 🚀 #DataScience #MachineLearning #FeatureSelection #LearningJourney #DataScienceJourney

📊 Day 129: Exploring Probability Distributions in Data Science journey: - Bernoulli & Binomial Distributions tackled. - Solved real-world problems with Binomial Distribution. - Mastered PDF formula & graph representation. - Implemented Binomial Distribution in code. - Discussed criteria & applications in data science. - Highlighted importance of Sampling Distribution for inference. - Key takeaway: Sampling Distribution guides data-driven decisions. #DataScience #Probability #Statistics #BinomialDistribution #SamplingDistribution

🌟 Curious about non-parametric causal inference? 🎙️ In our recent webinar, Nathaniel Forde delves into the world of non-parametric causal inference with PyMC, focusing on propensity scores. This webinar covers various weighting schemes and explores the flexibility of Bayesian additive regression trees (BART) models. Available right now to our Patrons! 👩💻 Whether you're a seasoned data scientist or someone eager to delve into Bayesian modelling, becoming a Patron offers you the chance to support the show and unlock exclusive access to insightful, hands-on working sessions. 🔗 Explore more and become a Patron here: https://lnkd.in/etf-RxMQ #bayesian #machinelearning #pymc #causalinference #NonParametricCausalInference #BayesianModelling #PropensityScores #BARTModels #BayesianAdditiveRegressionTrees #PatronExclusive #handsonlearning #datascienceeducation #SupportTheShow #dataanalysis

#66daysofdata Days 2-4: Heads on with Stats 🏃♀️ Topics covered: - Understanding distributions, population and estimated parameters - Mathematical models, bootstrapping and sampling - Hypothesis Testing - NHST, Alternative Hypothesis, p-values, p-hacking Understanding the deeper what and how behind those concepts at a grassroots level was fun. Knowledge of implementing these in R, acquired through my exposure to projects during my master's, made even more sense today. ✨️ Ps: Starting the next session on exploring Big Data today :) #datanalytics #bigdata #statistics

The Data Science Pronto! video series offers bite-sized explanations of common #datascience concepts. If you need a refresher on things like backpropagation, what a learning rate is, or what REST Services are, then check out the playlist on #KNIMETV 📺 ⏯ https://lnkd.in/eKi9dyDE And guess what? New Data Science Pronto! videos are in the making 📹 so stay tuned! ✨ #learning #KNIME #opensource

Excited to introduce DataStalgia! As part of my journey into Computer Vision, I’ve decided to revisit the major areas of Data Science that I’ve already mastered—an exciting "nostalgia trip" to reinforce foundational concepts before exploring new horizons. Throughout this journey, I’ll cover key topics in data science, sharing insights and reflections along the way. Join me on this DataStalgia adventure! #DataScience #DataStalgia #LearningJourney #ComputerVision #Growth

🚀 Day 83 of #180DaysofDataScience 🚀 🔸 Statistics: Today's Adventure: 🔍 What I Explored: Variance Ratio Test: --> A statistical test used to determine if the variance of a time series or a sequence of data points behaves as expected under the assumption of a random walk 📚 Key Takeaways: --> Examines how the variance changes over different time intervals What's Next: 🔮 Upcoming Exploration: --> Tomorrow, I will continue with hypothesis testing #DataScience #180DaysOfData #LearningJourney #TechExploration #DataScienceCommunity #StayCurious

🚀 Just unveiled a sneak peek into my latest data science project! 📊 Explored a diabetes prediction dataset using PCA and LDA for dimensionality reduction. 📈 With PCA, visualized data in 2D to capture variance and identified key features influencing diabetes predictions. Leveraged LDA to enhance class separability, boosting classification performance. 🤓 Checked out explained variance ratios, uncovering insights into feature importance. Shared compelling visualizations including variance histograms and correlation matrices. Excited about the potential impact on predictive modeling! 💡 Check out the full project and findings on my GitHub: https://lnkd.in/gWNRnwtf #DataScience #PCA #LDA #DiabetesPrediction #MachineLearning 🌐

I really enjoyed participating in the Data Science Pronto! series! 😊 Check my colleagues and I explaining #datascience concepts in a concise, animated way here ⬇️ #KNIME #freesoftware #opensource #data #analytics #nocode #lowcode