NLP-Natural Language Processing in Python for Beginners

Focused View

AI Sciences

23:29:28

248 View

01.01-introduction to the course.mp4

00:55

01.02-introduction to the instructor.mp4

05:44

01.03-introduction to the co-instructor.mp4

00:34

01.04-course introduction.mp4

11:16

02.01-what is regular expression.mp4

05:56

02.02-why regular expression.mp4

06:31

02.03-eliza chatbot.mp4

04:23

02.04-python regular expression package.mp4

04:24

03.01-metacharacters.mp4

02:27

03.02-metacharacters bigbrackets exercise.mp4

04:19

03.03-meta characters bigbrackets exercise solution.mp4

03:59

03.04-metacharacters bigbrackets exercise 2.mp4

02:11

03.05-metacharacters bigbrackets exercise 2-solution.mp4

04:37

03.06-metacharacters cap.mp4

03:18

03.07-metacharacters cap exercise 3.mp4

02:03

03.08-metacharacters cap exercise 3-solution.mp4

04:44

03.09-backslash.mp4

05:02

03.10-backslash continued.mp4

07:59

03.11-backslash continued-01.mp4

03:51

03.12-backslash squared brackets exercise.mp4

01:38

03.13-backslash squared brackets exercise solution.mp4

03:58

03.14-backslash squared brackets exercise-another solution.mp4

04:43

03.15-backslash exercise.mp4

01:43

03.16-backslash exercise solution and special sequences exercise.mp4

05:12

03.17-solution and special sequences exercise solution.mp4

04:57

03.18-metacharacter asterisk.mp4

05:18

03.19-metacharacter asterisk exercise.mp4

04:53

03.20-metacharacter asterisk exercise solution.mp4

05:04

03.21-metacharacter asterisk homework.mp4

04:28

03.22-metacharacter asterisk greedy matching.mp4

05:54

03.23-metacharacter plus and question mark.mp4

05:46

03.24-metacharacter curly brackets exercise.mp4

03:25

03.25-metacharacter curly brackets exercise solution.mp4

05:18

04.01-pattern objects.mp4

05:05

04.02-pattern objects match method exercise.mp4

02:43

04.03-pattern objects match method exercise solution.mp4

07:11

04.04-pattern objects match method versus search method.mp4

05:18

04.05-pattern objects finditer method.mp4

03:20

04.06-pattern objects finditer method exercise solution.mp4

06:22

05.01-metacharacters logical or.mp4

06:28

05.02-metacharacters beginning and end patterns.mp4

04:57

05.03-metacharacters parentheses.mp4

07:03

06.01-string modification.mp4

03:49

06.02-word tokenizer using split method.mp4

04:34

06.03-sub method exercise.mp4

04:44

06.04-sub method exercise solution.mp4

04:30

07.01-what is a word.mp4

04:41

07.02-definition of word is task dependent.mp4

05:20

07.03-vocabulary and corpus.mp4

05:38

07.04-tokens.mp4

03:14

07.05-tokenization in spacy.mp4

09:10

08.01-yelp reviews classification mini project introduction.mp4

05:35

08.02-yelp reviews classification mini project vocabulary initialization.mp4

06:48

08.03-yelp reviews classification mini project adding tokens to vocabulary.mp4

04:54

08.04-yelp reviews classification mini project look up functions in vocabulary.mp4

05:57

08.05-yelp reviews classification mini project building vocabulary from data.mp4

09:16

08.06-yelp reviews classification mini project one-hot encoding.mp4

06:51

08.07-yelp reviews classification mini project one-hot encoding implementation.mp4

09:47

08.08-yelp reviews classification mini project encoding documents.mp4

06:50

08.09-yelp reviews classification mini project encoding documents implementation.mp4

06:28

08.10-yelp reviews classification mini project train test splits.mp4

03:41

08.11-yelp reviews classification mini project feature computation.mp4

06:08

08.12-yelp reviews classification mini project classification.mp4

10:45

09.01-tokenization in detial introduction.mp4

04:28

09.02-tokenization is hard.mp4

05:16

09.03-tokenization byte pair encoding.mp4

06:08

09.04-tokenization byte pair encoding example.mp4

08:33

09.05-tokenization byte pair encoding on test data.mp4

07:14

09.06-tokenization byte pair encoding implementation get pair counts.mp4

08:11

09.07-tokenization byte pair encoding implementation merge in corpus.mp4

07:49

09.08-tokenization byte pair encoding implementation bfe training.mp4

06:10

09.09-tokenization byte pair encoding implementation bfe encoding.mp4

06:25

09.10-tokenization byte pair encoding implementation bfe encoding one pair.mp4

09:11

09.11-tokenization byte pair encoding implementation bfe encoding one pair 1.mp4

07:53

10.01-word normalization case folding.mp4

04:43

10.02-word normalization lemmatization.mp4

06:48

10.03-word normalization stemming.mp4

02:31

10.04-word normalization sentence segmentation.mp4

06:27

11.01-spelling correction minimum edit distance introduction.mp4

07:59

11.02-spelling correction minimum edit distance example.mp4

09:04

11.03-spelling correction minimum edit distance table filling.mp4

09:10

11.04-spelling correction minimum edit distance dynamic programming.mp4

07:17

11.05-spelling correction minimum edit distance pseudocode.mp4

03:55

11.06-spelling correction minimum edit distance implementation.mp4

06:57

11.07-spelling correction minimum edit distance implementation bug fixing.mp4

02:40

11.08-spelling correction implementation.mp4

07:42

12.01-what is a language model.mp4

05:50

12.02-language model formal definition.mp4

06:31

12.03-language model curse of dimensionality.mp4

04:27

12.04-language model markov assumption and n-grams.mp4

07:06

12.05-language model implementation setup.mp4

04:24

12.06-language model implementation n-grams function.mp4

08:42

12.07-language model implementation update counts function.mp4

05:46

12.08-language model implementation probability model function.mp4

06:35

12.09-language model implementation reading corpus.mp4

12:17

12.10-language model implementation sampling text.mp4

18:17

13.01-one-hot vectors.mp4

04:10

13.02-one-hot vectors implementation.mp4

05:41

13.03-one-hot vectors limitations.mp4

04:45

13.04-one-hot vectors used as target labeling.mp4

03:42

13.05-term frequency for document representations.mp4

03:12

13.06-term frequency for document representations implementations.mp4

05:31

13.07-term frequency for word representations.mp4

05:17

13.08-tfidf for document representations.mp4

05:01

13.09-tfidf for document representations implementation reading corpus.mp4

04:24

13.10-tfidf for document representations implementation computing document frequency.mp4

04:59

13.11-tfidf for document representations implementation computing tfidf.mp4

07:22

13.12-topic modeling with tfidf 1.mp4

04:20

13.13-topic modeling with tfidf 2.mp4

04:27

13.14-topic modeling with tfidf 3.mp4

04:51

13.15-topic modeling with tfidf 4.mp4

04:40

13.16-topic modeling with gensim.mp4

13:27

14.01-word co-occurrence matrix.mp4

06:25

14.02-word co-occurrence matrix versus document-term matrix.mp4

05:47

14.03-word co-occurrence matrix implementation preparing data.mp4

05:08

14.04-word co-occurrence matrix implementation preparing data 2.mp4

04:12

14.05-word co-occurrence matrix implementation preparing data getting vocabulary.mp4

04:26

14.06-word co-occurrence matrix implementation final function.mp4

11:37

14.07-word co-occurrence matrix implementation handling memory issues on large corpora.mp4

09:19

14.08-word co-occurrence matrix sparsity.mp4

05:19

14.09-word co-occurrence matrix positive point wise mutual information ppmi.mp4

07:58

14.10-pca for dense embeddings.mp4

04:09

14.11-latent semantic analysis.mp4

04:25

14.12-latent semantic analysis implementation.mp4

06:50

15.01-cosine similarity.mp4

07:05

15.02-cosine similarity getting norms of vectors.mp4

09:18

15.03-cosine similarity normalizing vectors.mp4

06:36

15.04-cosine similarity with more than one vector.mp4

11:00

15.05-cosine similarity getting most similar words in the vocabulary.mp4

10:05

15.06-cosine similarity getting most similar words in the vocabulary fixing bug.mp4

06:23

15.07-cosine similarity word2vec embeddings.mp4

08:21

15.08-word analogies.mp4

04:50

15.09-words analogies implementation 1.mp4

05:55

15.10-word analogies implementation 2.mp4

06:13

15.11-word visualizations.mp4

02:16

15.12-word visualizations implementation.mp4

04:28

15.13-word visualizations implementation 2.mp4

06:37

16.01-static and dynamic embeddings.mp4

06:17

16.02-self supervision.mp4

04:23

16.03-word2vec algorithm abstract.mp4

05:21

16.04-word2vec why negative sampling.mp4

03:49

16.05-word2vec what is skip gram.mp4

04:43

16.06-word2vec how to define probability law.mp4

04:00

16.07-word2vec sigmoid.mp4

05:21

16.08-word2vec formalizing loss function.mp4

05:35

16.09-word2vec loss function.mp4

03:25

16.10-word2vec gradient descent step.mp4

04:21

16.11-word2vec implementation preparing data.mp4

10:37

16.12-word2vec implementation gradient step.mp4

07:18

16.13-word2vec implementation driver function.mp4

13:38

17.01-why rnns for nlp.mp4

13:25

17.02-pytorch installation and tensors introduction.mp4

10:32

17.03-automatic differentiation pytorch.mp4

08:26

18.01-why dnns in machine learning.mp4

04:13

18.02-representational power and data utilization capacity of dnn.mp4

07:13

18.03-perceptron.mp4

05:08

18.04-perceptron implementation.mp4

07:26

18.05-dnn architecture.mp4

03:52

18.06-dnn forwardstep implementation.mp4

08:21

18.07-dnn why activation function is required.mp4

04:47

18.08-dnn properties of activation function.mp4

06:04

18.09-dnn activation functions in pytorch.mp4

03:49

18.10-dnn what is loss function.mp4

07:10

18.11-dnn loss function in pytorch.mp4

05:45

19.01-dnn gradient descent.mp4

07:07

19.02-dnn gradient descent implementation.mp4

05:58

19.03-dnn gradient descent stochastic batch minibatch.mp4

06:51

19.04-dnn gradient descent summary.mp4

02:37

19.05-dnn implementation gradient step.mp4

04:02

19.06-dnn implementation stochastic gradient descent.mp4

13:53

19.07-dnn implementation batch gradient descent.mp4

06:46

19.08-dnn implementation minibatch gradient descent.mp4

09:04

19.09-dnn implementation in pytorch.mp4

15:19

20.01-dnn weights initializations.mp4

04:35

20.02-dnn learning rate.mp4

04:03

20.03-dnn batch normalization.mp4

02:05

20.04-dnn batch normalization implementation.mp4

02:41

20.05-dnn optimizations.mp4

04:08

20.06-dnn dropout.mp4

03:58

20.07-dnn dropout in pytorch.mp4

02:03

20.08-dnn early stopping.mp4

03:34

20.09-dnn hyperparameters.mp4

03:33

20.10-dnn pytorch cifar10 example.mp4

15:46

21.01-what is rnn.mp4

04:54

21.02-understanding rnn with a simple example.mp4

08:29

21.03-rnn applications human activity recognition.mp4

02:53

21.04-rnn applications image captioning.mp4

02:37

21.05-rnn applications machine translation.mp4

03:16

21.06-rnn applications speech recognition stock price prediction.mp4

04:04

21.07-rnn models.mp4

07:07

22.01-language modeling next word prediction.mp4

03:43

22.02-language modeling next word prediction vocabulary index.mp4

04:04

22.03-language modeling next word prediction vocabulary index embeddings.mp4

03:16

22.04-language modeling next word prediction rnn architecture.mp4

04:08

22.05-language modeling next word prediction python 1.mp4

07:12

22.06-language modeling next word prediction python 2.mp4

09:02

22.07-language modeling next word prediction python 3.mp4

07:27

22.08-language modeling next word prediction python 4.mp4

05:33

22.09-language modeling next word prediction python 5.mp4

04:35

22.10-language modeling next word prediction python 6.mp4

13:34

23.01-vocabulary implementation.mp4

09:46

23.02-vocabulary implementation helpers.mp4

05:49

23.03-vocabulary implementation from file.mp4

06:26

23.04-vectorizer.mp4

05:17

23.05-rnn setup.mp4

07:20

23.06-rnn setup 1.mp4

21:23

24.01-rnn in pytorch introduction.mp4

02:04

24.02-rnn in pytorch embedding layer.mp4

07:21

24.03-rnn in pytorch nn rnn.mp4

08:47

24.04-rnn in pytorch output shapes.mp4

04:11

24.05-rnn in pytorch gated units.mp4

03:39

24.06-rnn in pytorch gated units gru lstm.mp4

03:55

24.07-rnn in pytorch bidirectional rnn.mp4

02:44

24.08-rnn in pytorch bidirectional rnn output shapes.mp4

04:26

24.09-rnn in pytorch bidirectional rnn output shapes separation.mp4

04:33

24.10-rnn in pytorch example.mp4

09:48

25.01-rnn encoder decoder.mp4

03:01

25.02-rnn attention.mp4

03:28

26.01-introduction to dataset and packages.mp4

05:10

26.02-implementing language class.mp4

05:22

26.03-testing language class and implementing normalization.mp4

09:44

26.04-reading datafile.mp4

05:17

26.05-reading building vocabulary.mp4

07:56

26.06-encoderrnn.mp4

06:14

26.07-decoderrnn.mp4

06:13

26.08-decoderrnn forward step.mp4

12:08

26.09-decoderrnn helper functions.mp4

04:54

26.10-training module.mp4

13:31

26.11-stochastic gradient descent.mp4

07:12

26.12-nmt training.mp4

05:06

26.13-nmt evaluation.mp4

11:13

9781803249193 Code.zip

Description

Natural Language Processing (NLP), a subdivision of Artificial Intelligence (AI), is the ability of a computer to understand human language the way it’s spoken and written. Human language is typically referred to as natural language.

Humans also have different sensors. For instance, ears perform the function of hearing and eyes perform the function of seeing. Similarly, computers have programs for reading and microphones for collecting audio. Just as the human brain processes an input, a computer program processes a specific input. And during processing, the program converts the input to code that the computer understands.

This course, Natural Language Processing (NLP), Theory and Practice in Python, introduces you to the concepts, tools, and techniques of machine learning for text data. You will learn the elementary concepts as well as emerging trends in the field of NLP. You will also learn about the implementation and evaluation of different NLP applications using deep learning methods.

Code bundles are available here: https://github.com/PacktPublishing/NLP-Natural-Language-Processing-in-Python-for-Beginners

More details

User Reviews

Rating

average 0

Total votes0

Focused display

Python

Natural Language Processing

AI Sciences

Instructor's Courses

AI Sciences is a group of experts, PhDs, and practitioners of AI, ML, computer science, and statistics. Some of the experts work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.They have produced a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of machine learning, statistics, artificial intelligence, and data science.Initially, their objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory. Today, they also publish more complete courses for a wider audience. Their courses have had phenomenal success and have helped more than 100,000 students master AI and data science.

PacktPub

View courses PacktPub

Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.