Aashish Jangid

Learning NLP? 𝐃𝐨𝐧’𝐭 𝐜𝐨𝐧𝐟𝐮𝐬𝐞 𝐭𝐡𝐞𝐬𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐭𝐞𝐫𝐦𝐬. Many NLP beginners (and even pros) mix up concepts like stemming vs. lemmatization, or tokenization vs. chunking — and that leads to poor preprocessing and model performance. I built this quick visual to help you clearly understand 10+ commonly confused NLP techniques — with definitions, examples, and key differences. ✅ Great for anyone working on: • Text classification • Chatbots & LLMs • Preprocessing for RAG & fine-tuning 👉 Swipe to check the full comparison sheet. 💬 Have a favorite NLP trick or confusion to add? Drop it in the comments! 👍Like | 🔖 Save | 🔁 Share with NLP learners ➕ Follow Naresh Edagotti for more content that makes complex AI topics feel simple.

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Interview Readiness Playlist for Aspiring Data Scientists 📌ML Design: Combating Counterfeit Fraud youtu.be/YQZBgvLB_EQ 📌ML Design: Detecting Buyer Returns Fraud youtu.be/as4i1tUo0EA 📌Derive & Implement Logistic Regression from Scratch youtu.be/cT_U40djaww  📌PCA Explained Simply with Codes youtu.be/yxz3UpPMycg 📌 Ongoing Interviewing Trends: youtu.be/XrZXojl5Zd8 📌 Essential Mathematics for ML: youtu.be/oMY2uKjx_Zc 📌Categorical Variable Encoding Strategies youtu.be/MKuAQv6ybc8  🎯Complete guide for Linear Regression lnkd.in/gdRsMHbn 🎯Complete playlist for Logistic Regression lnkd.in/gtPfmQUv 📌 Classification Metrics: youtu.be/E2HRSJKU-_4 🎯Data Drift Detection & Model Monitoring youtu.be/tQjRQWfYQ10 📌 Conversational AI: Tech behind Chat-GPT: youtu.be/JKoJ5YIr2O4 📌 Pushing LLM Boundaries Sparse & Flash Attention, Quantisation, Pruning, Distillation, LORA : youtu.be/mF7OM_XU2S4 📌 PySpark Essentials youtu.be/aruptWppgSs #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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If you are preparing for AI / ML / GenAI interviews - math & stats are the first thing interviwer will ask. Here is a step by step roadmap of what to prepare. ✅ Linear Algebra – The Core Most ML algorithms are matrix math under the hood. • Vectors & Matrices: addition, multiplication, transpose • Dot Product & Norms: cosine similarity, distance metrics • Eigenvalues / SVD: PCA, dimensionality reduction - Interview question: "Explain how embeddings or PCA work" ⸻ ✅ Probability & Statistics – For Uncertainty • Random Variables & Distributions (Gaussian, Bernoulli, etc.) • Expectation & Variance • Bayes’ Theorem: conditional probabilities, Naïve Bayes (super important) • Hypothesis Testing & p-values - Interview angle: "How would you evaluate a classifier with imbalanced data?" ⸻ ✅ Calculus – Just the Essentials You need enough to understand optimization: • Derivatives & Gradients • Chain Rule (for backpropagation) • Partial Derivatives & Gradient Descent - Interview angle: "Walk me through backprop in a neural net" ⸻ ✅ Linear/Nonlinear Optimization • Convex vs non-convex problems • Lagrange multipliers (basics) • Understanding loss landscapes - Interview angle: "Why does gradient descent sometimes get stuck?" ⸻ ✅ Discrete Math & DSA Crossover For coding rounds: • Graph theory (BFS/DFS) • Probability in algorithms (hashing, randomization) • Combinatorics for sampling questions ⸻ ✅ How to Study (Without Burning Out) • YouTube: StatQuest (probability/stats), 3Blue1Brown (linear algebra & calculus), Gilbert Strang lectures (very good) • Practice: Derive gradients of simple models (linear regression, softmax) by hand. ⸻ If you need complete roadmap and guidance feel free to checkout my e-book [Link in comment]

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🚩 You Can’t Grow in Data if You Don’t Learn This First Someone asked me this after my last post on business framing, and it’s such a good question. 1. Pause and Probe: When someone says “Can you pull customer data for Q2?” 
📍 Don’t open Jupyter Notebook immediately. Pause and ask:
→ What are they trying to decide?
→ What would make this analysis useful?
→ Are they asking the right question? 2. Learn to ask “Why?” and “Then what?” Someone says:
“We need a dashboard for marketing.” 📍 You say:
→ Why do you need it?
→ What decisions will it help you make?
→ What would you do differently after seeing it? You’re not being difficult. You’re helping. 3. Practice turning vague questions into specific ones: Business:
“Why are fewer people renewing their policy?” 📍 You learn to reframe: 
→ Is renewal rate dropping more for first-time customers vs returning ones? 
→ Did the drop begin after a pricing or communication change? This is what framing looks like. 4. Do this with any dataset you find: Go to Kaggle or anywhere. Pick a real-world dataset not dummy data. 
Before doing any analysis, do this: 📍 Ask yourself: 
→ If I was hired by this company, what problem would they want me to solve? → What would I ask them before writing code? 
→ What would be bad advice to give from this data? 📍This is how you practice the thinking. 5. Work backwards from the decision: 📍 Don’t start from the data. Start from the goal. → What’s the decision they want to make? 
→ What are they worried about? → What would “value” look like in this case? 📍That’s how you stop being a task-taker and start becoming a strategic thinker. → Business framing is not a tool. 
→ It won’t make your portfolio look fantastic. 
📍 But it will make you the analyst/scientists that gets invited into every meeting because you ask the right questions. 📍That’s how you move from executing tasks to influencing strategy. ♻️ Repost so others can learn #DataLiving #FavourIbude

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10 Probability Distributions Every Data Scientist Must Know 1. Uniform Distribution Every outcome has equal probability. Use Case: Backbone of simulations. 2. Binomial Distribution Counts the number of successes in a fixed number of independent trials. Use Case: Predicting customer conversion rates 3. Multinomial Distribution Extension of the binomial for multi-class outcomes. Use Case: Multi-class classification, text generation 4. Normal (Gaussian) Distribution The classic bell curve, common in natural and social phenomena. Use Case: Hypothesis testing, anomaly detection 5. Chi-Square Distribution Used to test for independence and goodness of fit. Use Case: Feature selection, variance analysis 6. t-Distribution Similar to normal but with heavier tails, better for small samples. Use Case: t-tests with limited data 7. Multivariate Normal Distribution Generalizes the normal distribution to multiple variables. Use Case: Gaussian mixture models, variational autoencoders 8. Gamma Distribution Models waiting times and is a generalization of the exponential distribution. Use Case: Bayesian inference, reliability modeling 9. Beta Distribution Defined on [0, 1], often used to model probabilities. Use Case: A/B testing, Bayesian posterior distributions 10. Dirichlet Distribution Multivariate extension of the beta distribution for categorical outcomes. Use Case: Topic modeling (e.g., LDA) --- Follow Prakhar for more Data & AI concepts. :)

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🚀 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐯𝐬. 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 — 𝐖𝐡𝐚𝐭'𝐬 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞? If you're exploring a career in data, it’s essential to understand the distinction between two of the most in-demand roles in the field: Data Science Certification Course :- https://lnkd.in/gnzK7dwt 🔹 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 Focus: Past trends & insights Tools: Excel, SQL, Tableau, Power BI Skills: Reporting, data visualization, dashboards Goal: Answer "What happened?" and "Why?" Great for: Entry into the data world, especially with a business background 🔸 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 Focus: Predictive modeling & automation Tools: Python, TensorFlow, Spark, Scikit-learn Skills: Machine learning, experimentation, deployment Goal: Answer "What will happen?" and "How can we optimize it?" Great for: Those with strong technical and statistical expertise 👉 While both roles work with data, they serve different purposes within an organization. Analysts make sense of the past, while scientists shape the future.

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AI Agents for Data Engineering? (Quick Guide📝) From the last few days, I have been exploring AI Agents. They are powerful and can do lot of automation for repetitive task. Everyone’s talking about AI agents in coding, customer support, marketing… But here’s what’s about to happen in data engineering 👇🏻 ✅ Automated Data Quality Checks Imagine an AI agent that constantly monitors your pipelines, detects anomalies, and opens a Jira ticket. Before your analyst even notices the numbers are off. ✅ Self-Healing Pipelines our Airflow job fails at 2AM. The AI agent: - Reads the logs - Finds the root cause - Applies the known fix - Reruns the job No human on-call needed. ✅ Schema Change Adaptation Instead of breaking when a new column arrives… The AI agent updates the schema mapping, validates it, and informs downstream teams. All automatically. ✅ Query Optimization in Real-Time You write a slow SQL query. The AI agent rewrites it with better joins, partition filters, and indexes. You get results 10x faster without touching the execution plan. ✅ Smart Documentation An AI agent reads your dbt models, DAGs, and logs, and auto-generates documentation that’s always up to date. 🧠 My take: We’re not replacing data engineers. We’re removing the repetitive pain so engineers can focus on design, architecture, and business impact. Question: Would you trust an AI agent to fix your production pipeline without your approval? Why or why not? No matter what happens Data Engineers are going to be backbone of AI Model and this is the best time to get into Data Engineering (check below) ⬇️ #dataengineer #dataengineering

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