Data Science | ML | AI Resources

15 Machine Learning Projects (Step by Step Guide) along with Source Code👇 https://www.instagram.com/reel/DegGsclyJJU/?xtok=MWFvazJseHk1ZXRzMA==

Asked in a Netflix AI Engineer interview 👇 A Netflix user watches a movie and says, “Show me something like this, but more exciting.” How would you design a GenAI recommendation system that understands the user’s intent and generates personalized recommendations? "More exciting" is a direction, not a query. The request is an anchor plus a vector, and a plain similarity search answers the wrong question. 1. Parse the instruction into anchor, axis and constraints - Anchor: the title they just watched - Axis and magnitude: pace and intensity, moved up a notch - What must not change: genre, language, tone, maturity rating 2. Label the catalogue on the axes people actually use - Pace, intensity, humour, complexity, ending type, violence - Derive them from metadata, subtitles and trailers; LLM-tag what is missing - Human-verify the axes that drive the most requests — this is the real work 3. Move the vector, then retrieve - Take the anchor embedding, add a delta along the excitement axis, run ANN over the catalogue - Then apply availability, language and maturity filters 4. Personalise the result, do not replace it - Blend with the member's taste model so two people asking the same thing get different rows - The instruction constrains, the taste model orders 5. Generate the explanation, never the item - The LLM writes "faster paced, same heist premise"; the catalogue decides what exists - Ungrounded generation recommends titles you do not have the rights to 6. Keep the session open - "Even more" should travel further along the same axis, not reset - Log every direction request — they are free, precise preference labels

Become a Job Ready Data Engineer(In Just 7 Weeks) Week 1: Learn SQL Beginner to Advanced in Under 4 Hours https://www.youtube.com/watch?v=OT1RErkfLNQ Week 2: Learn Python - Full Course for Beginners [Tutorial] https://www.youtube.com/watch?v=rfscVS0vtbw Python for Data Science - Course for Beginners (Learn Python, Pandas, NumPy, Matplotlib) https://www.youtube.com/watch?v=LHBE6Q9XlzI Week 3: Data Modeling Masterclass For Data Engineers https://www.youtube.com/watch?v=K7C1sWKQU-o Apache Airflow One Shot- Building End To End ETL Pipeline Using AirFlow And Astro https://www.youtube.com/watch?v=Y_vQyMljDsE Week 4: Databricks Tutorial | Databricks Free Edition Tutorial with End-to-End Data + AI Project https://www.youtube.com/watch?v=761SQ9Hxbic PySpark Tutorial | Full Course (From Zero to Pro!) https://www.youtube.com/watch?v=94w6hPk7nkM Week 5: [Data Build Tool] DBT - The Ultimate Guide | With CI/CD https://www.youtube.com/watch?v=B8uwFmVt4sU Week 6: AWS Masterclass for Data Engineers with Project https://www.youtube.com/watch?v=yvAWbbQa8eE Azure Data Engineer Full Course | Azure Data Factory Tutorial For Beginners https://www.youtube.com/watch?v=YkK0mdcJfHg Week 7: End to End Data Engineering Project using Databricks Free Edition | FMCG Domain https://www.youtube.com/watch?v=U6ZUKWdfSLY Zomato AI Data Analytics | End-To-End AI Data Engineering Project https://www.youtube.com/watch?v=kYwaNMQ3XT8

7 Real World AI Projects to get hired at MANGOS👇 https://www.instagram.com/reel/DeQ0dE2AWSg/?srtk=MXN5MmtyeHM3cnhiZw==

7 Real World AI Projects to get hired at MANGOS👇 https://www.instagram.com/reel/DeQ0dE2AWSg/?srtk=MXN5MmtyeHM3cnhiZw==

Become a Job Ready Data Engineer(In Just 7 Weeks) Week 1: Learn SQL Beginner to Advanced in Under 4 Hours https://www.youtube.com/watch?v=OT1RErkfLNQ Week 2: Learn Python - Full Course for Beginners [Tutorial] https://www.youtube.com/watch?v=rfscVS0vtbw Python for Data Science - Course for Beginners (Learn Python, Pandas, NumPy, Matplotlib) https://www.youtube.com/watch?v=LHBE6Q9XlzI Week 3: Data Modeling Masterclass For Data Engineers https://www.youtube.com/watch?v=K7C1sWKQU-o Apache Airflow One Shot- Building End To End ETL Pipeline Using AirFlow And Astro https://www.youtube.com/watch?v=Y_vQyMljDsE Week 4: Databricks Tutorial | Databricks Free Edition Tutorial with End-to-End Data + AI Project https://www.youtube.com/watch?v=761SQ9Hxbic PySpark Tutorial | Full Course (From Zero to Pro!) https://www.youtube.com/watch?v=94w6hPk7nkM Week 5: [Data Build Tool] DBT - The Ultimate Guide | With CI/CD https://www.youtube.com/watch?v=B8uwFmVt4sU Week 6: AWS Masterclass for Data Engineers with Project https://www.youtube.com/watch?v=yvAWbbQa8eE Azure Data Engineer Full Course | Azure Data Factory Tutorial For Beginners https://www.youtube.com/watch?v=YkK0mdcJfHg Week 7: End to End Data Engineering Project using Databricks Free Edition | FMCG Domain https://www.youtube.com/watch?v=U6ZUKWdfSLY Zomato AI Data Analytics | End-To-End AI Data Engineering Project https://www.youtube.com/watch?v=kYwaNMQ3XT8

Asked in a Meta AI/ML Engineer interview 👇 Instagram has millions of posts uploaded every minute. How would you design an AI system that predicts which posts a particular user is most likely to engage with and ranks their feed accordingly? You cannot score millions of posts per user. Say that first. Feed ranking is a funnel where each stage keeps 10x fewer candidates and spends 10x more compute on each one. 1. Build the funnel - Retrieval: thousands of candidates in a few milliseconds - Light ranker: a cheap model cuts that to a few hundred - Heavy ranker: the expensive multi-task model scores the survivors - Policy layer: diversity, frequency caps, integrity filters 2. Generate candidates from several sources - The follow graph, plus two-tower embeddings for ANN over fresh posts - Trending and topic pools for discovery beyond the graph - New posts must be indexed within seconds or the feed feels stale 3. Predict actions, not "engagement" - One model, several heads: P(like), P(comment), P(share), P(dwell > 5s), P(hide) - The final score is a weighted sum, and negatives subtract - Those weights are a product decision, not a modelling one 4. The features that matter - Viewer × author affinity from past interactions, and recency of the post - Content embeddings for image, video and caption - Real-time counters: how a post performed in its first minute is the strongest single signal 5. Do not optimise the wrong number - Clickbait wins on click-through, so add dwell, meaningful interactions and survey data - Cap posts per author and enforce topic diversity, or the feed collapses onto one creator 6. Prove it online - Offline AUC and NDCG only gate the experiment; A/B tests decide - Watch retention and reported satisfaction, not just session engagement