Learning Data Science related topics

Many times I see a lot of Learning Data Science related topics

Some of the things I still learn and keep learning for work, and personal learning are at all levels: advanced - recent trends.

I tried to learn all again and again all below topics :)

• Maths fundamentals
• Differential calculus 
• Basics Linear algebra, matrix decomposition, pca
• Algo fundamentals, Trees, entropy, svm kernels, higher dimensions representations
• Feature engineering, boosting, bagging
• backprop basics, cnn, pooling, convolution, activation, dense, softmax
• CNN network design, loss functions, gradient descent techniques, Transfer Learning
• RNN, LSTM
• Transformers, encoders, decoders
• Basics of NLP, NER, Preprocessing, CRF, Naive Bayes, Sentiment Classification, NER Custom detection, Topic Mining
• Vision - detection, classification,segmentation
• Forecasting - Time series, linear regression, TFT, GluonTS
• Recommendations - Basics, Apriori, User-User, Item-Item, Recency-Frequency-Value, Ranking, Contextualize,Realtime, Batched
• MLOps, Deployment, FastAPI, AWS Lambda, SaaS Approaches, MLFLow, Dockerize, Streamlit, API

This is on top of other DB stuff :).

• Learn to connect on top of what you already know. 
• Grades, certifications does not mean expertise
• To grow, compile code, build your solution, copy and run existing code all are different levels of skills and expertise...

Learn at our own pace...Ever better...

It takes time to know 'Why it works, How it works, How it works when I debug Step by Step'

Be authentic to yourself, that's all Life is :)

• Anxiety is built into the very nature of leadership. It can—and should—be harnessed into a force for good. 
• Disrupting our anxiety starts by recognizing that anxiety is just data and it exists on a spectrum

Ref - Link 

Keep Going!!!