Need some path to Learn new courses

[srinima]

HI All,

 

Planning to learn new courses. CUrrently I work on reporting tools, SQL and have some insight into python scripting. 

 

Databricks course anukuntuna. Can someone suggest where to start and structure of the course we need to learn? 

[Sam480]

16 minutes ago, srinima said:

HI All,

 

Planning to learn new courses. CUrrently I work on reporting tools, SQL and have some insight into python scripting. 

 

Databricks course anukuntuna. Can someone suggest where to start and structure of the course we need to learn? 

Almost nadi kuda similar profile, I work mainly on SQL and recent ga python nerchukunna.

Data Science, Data Engineer and Machine learning lo edo oka side forward vellali ani planning

[a4apple]

Databricks is not a technology.

Learn PyTorch, tensorflow, NLP

[Undiporade]

15 minutes ago, a4apple said:

Databricks is not a technology.

Learn PyTorch, tensorflow, NLP

Anna endi ivi ?

[srinima]

Course outline emina cheppani bro.. not just names like pyTOrch, Tensorflow, NLP.

[dasari4kntr]

ask chatgpt…or deekseep suggestions for …learning path or career path …

you can ask for…30 days learning path for tensorflow for  beginners….

it will give you 30 days learning plan…

[enigmatic]

Databricks is a platform for managed pyspark.

[Aquaman]

[Aquaman]

[Aquaman]

[Aquaman]

[Aquaman]

[Aquaman]

[Aquaman]

[Aquaman]

Roadmap to Master Natural Language Processing (NLP) in 2025

If you're planning to dive into Natural Language Processing (NLP) in 2025, here's a comprehensive learning roadmap and a free course for you to guide your journey from the fundamentals to advanced concepts: https://lnkd.in/ginbH-Qp

 

Prerequisites

- Before you get started, ensure you're comfortable with:

- Python Programming: A must for NLP implementation.

- Machine Learning & Deep Learning Concepts: Basics like supervised/unsupervised learning, neural networks, and optimization techniques.

Essential NLP Libraries

- Here are the tools you'll need to master for building efficient NLP models: NLTK, spaCy, TextBlob (for traditional NLP tasks) TensorFlow, PyTorch, Hugging Face (for deep learning-based NLP), LangChain (for chaining tasks and prompt engineering)

Core NLP Fundamentals

Get a solid grasp of fundamental NLP tasks:

- Text Preprocessing: Tokenization, stemming, lemmatization, stop words, punctuation, case sensitivity.

- Language Models: N-grams, TF-IDF, Bag of Words.

- Semantic Analysis: Sentiment analysis, Named Entity Recognition (NER), POS tagging.

- Basic Sequence Models: Hidden Markov Models (HMMs), Conditional Random Fields (CRFs).

- Tools to practice: NLTK, spaCy, TextBlob.

Deep Learning for NLP

Leverage powerful deep learning techniques:

- Word Embeddings: Word2Vec, GloVe, FastText.

- RNNs, GRUs, LSTMs: For text generation, translation.

- Attention Mechanisms & Seq2Seq Models: With attention to improve translation, summarization.

- Transformers: The architecture powering modern NLP, including BERT, GPT, T5.

- Tools to learn: TensorFlow, PyTorch, Hugging Face.