Day 21 — Started Pandas today 📊 Learned what Pandas is, why it's used, and how it makes data analysis super easy compared to lists or loops. Excited to work with real datasets next! #100DaysOfDataScience #Pandas #LearningInPublic

DAY 2 #100Daysofdatascience

Day 11 — Started Project 1 (Coders of Delhi). Loaded the JSON data using pure Python and explored user connections & liked pages. First hands-on feel of real-world social network data 🔥 #100DaysOfDataScience #Python #LearningInPublic

Day 13 — Started with NumPy today! Learned how arrays work and why NumPy is much faster than Python lists. Created basic arrays, checked shapes, and played with random data. Excited for the real data analysis part 🔥 #100DaysOfDataScience #LearningInPublic #Python #numpy

Day 9 — Learned list comprehensions today. Crazy how one line can replace entire loops in Python 😅 Practiced with squares, evens, filters & even made a small matrix. Feeling smoother with Python now! #100DaysOfDataScience #LearningInPublic #Python

Day 20— Practiced NumPy basics: shapes, indexing, stats and normalizing data. NumPy makes data work so much faster. Feeling more comfortable every day. #100DaysOfDataScience #NumPy #LearningInPublic

Day-9: 100 Days Of Data Science Below are the topics I covered Today. => Lambda Function => List Comprehension => Random Module #100DaysOfDataScience #100DaysOfCode

Days_8 #100DaysOfCode #100DaysOfDataScience #DataCamp #DataScience @DataCamp #R

Hey everyone! It's Day 91 of #100DaysOfDataScience! Today, I covered ,bi-directional RNNs also implemented various RNN variants and word embeddings. Additionally, I gained understanding of the encoder-decoder architecture, the attention mechanism. That's all for today! 😊

Hey everyone! 👋🤗 It's #Day9 of #100DaysOfDataScience 😇 Today was highly productive!😊 I revised Python libraries pandas, numpy, matplotlib, and seaborn then studied statistics for data science 😌 also created a project that converts images to sketches using Python😁 SYT👋

Day 2 of #100DaysOfDataScience -> Continued with Campus videos -> started learning about more Libraries like matplotlib scikit-learn

Hey! It’s Day 87 of the #100DaysOfDataScience! Today, I explored Simple RNNs, focusing on the intuition behind forward and backward propagation, along with some challenges associated with it. While implementing, I also learned about word embedding layers. That’s all for today!

Hey!! It's #day 88 of #100DaysOfDataScience ! 😁 Today, I dove deeper into ANN and worked on its implementation. I also completed a mini project using a simple RNN for text classification ✌️ That's all for today! 😌 See you tomorrow! 👋

Hey!!☺️ It's #Day 86 of #100DaysOfDataScience! 😁 Today, I delved deeper into the implementation of ANN and started learning RNNs. I got a brief introduction to their architecture and functionality 😇 That's all for today!🤘

Hey!!👋🤗 It's #Day 84 of #100DaysOfDataScience! 😁 Today, I explored CNNs, learning about their similarities to the human brain, convolution, padding, pooling, flattening, and how ANN steps integrate into CNNs 🤟😇 That's all for today!👋☺️

Day 1 of #100DaysOfDataScience Although started learning 20 days back, but I'll post from today. The things I did: -> practiced Python codes -> started 100 days ml by campusx -> Completed numpy and pandas(1 topic left) from geekforgeeks Ps: laptop gone for repair, it crashed

Data Science Roadmap #DataScience #DataAnalytics #python #Machinelearning #100DaysOfDataScience #buildinginpublic

-did some basic readings today nothing much - solved array problem - trying to get regular on leetcode :( -deep knowledge is the goal #100DaysOfAI -learnt model , inference , predictions #100DaysofDataScience -understood culture & expectations of a data scientist

Hey!! It's Day 89 of #100DaysOfDataScience! 😁 Today, I studied LSTM RNN—its intro, why it's imp , its architecture, and how it works. I learned about the forget gate, input gate, candidate memory, output gate, and explored the mathematical intuition🤘 That's all for today!✌️

day4: sourcing data from public domain #100daysofdatascience