NLP-Natural Language Processing in Python for Beginners

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Course curriculum

1. Introduction to Course
2. Introduction to Instructor
3. Introduction to Co-Instructor
4. Course Introduction
5. Links for the Course's Materials and Codes
1. what is regular expression
2. why regular expression
3. ELIZA chatbot
4. Python regular expression package
1. meta characters
2. meta characters_bigBrackets_exercise
3. meta characters_bigBrackets_exercise_solution
4. meta characters_bigBrackets_exercise_2
5. meta characters_bigBrackets_exercise_2_solution
6. meta characters_cap
7. meta characters_cap_exercise_3
8. meta characters_cap_exercise_3_solution
9. backSlash
10. backSlash_continued
11. backSlash_continued 01
12. backSlash_Squared_brackets_exercise
13. backSlash_Squared_brackets_exercise_solution
14. backSlash_Squared_brackets_exercise_another_solution
15. backSlash_exercise
16. backSlash_exercise_Solution_and_Special Sequences_exercise
17. Solution_and_Special Sequences_exercise_solution
18. Meta_character_esterics
19. Meta_character_esterics_exercise
20. Meta_character_esterics_exercise_solution
21. Meta_character_esterics_Homework
22. Meta_character_esterics_GreedyMatching
23. Meta_character_Plus_and_questionMark
24. Meta_character_curly_brackets_exercise
25. Meta_character_curly_brackets_exercise_solution
1. pattern_objects
2. pattern_objects_match_method_exersize
3. pattern_objects_match_method_exersize_solution
4. pattern_objects_match_method_vs_search_method
5. pattern_objects_finditer_method
6. pattern_objects_finditer_method_exersize_solution
1. meta_characters_logical_or
2. meta_characters_beginning_and_end_patterns
3. meta_characters_paranthesis
1. String_modification
2. Word_tokenizer_using_split_method
3. sub_method_exercise
4. sub_method_exercise_solution
1. what is a word
2. definition of word is task dependent
3. Vocabulary and Corpus
4. Tokens
5. Tokenization in spaCy
1. Yelp_Reviews_classification_mini_project_introduction
2. Yelp_Reviews_classification_mini_project_Vocabulary_initialization
3. Yelp_Reviews_classification_mini_project_Adding_tokens_to_vocabulary
4. Yelp_Reviews_classification_mini_project_look_up_functions_in_vocabulary
5. Yelp_Reviews_classification_mini_project_building_vocabulary_from_data
6. Yelp_Reviews_classification_mini_project_one_hot_encoding
7. Yelp_Reviews_classification_mini_project_one_hot_encoding_implementation
8. Yelp_Reviews_classification_mini_project_Encoding_documents
9. Yelp_Reviews_classification_mini_project_Encoding_documents_implementation
10. Yelp_Reviews_classification_mini_project_train_test_splits
11. Yelp_Reviews_classification_mini_project_FeatureComputation
12. Yelp_Reviews_classification_mini_project_classification
1. Tokenization_in_detial_introduction
2. Tokenization_is_hard
3. Tokenization_Byte_pair_encoding
4. Tokenization_Byte_pair_encoding_example
5. Tokenization_Byte_pair_encoding_on_test_data
6. Tokenization_Byte_pair_encoding_Implementation_getPairCounts
7. Tokenization_Byte_pair_encoding_Implementation_mergeInCorpus
8. Tokenization_Byte_pair_encoding_Implementation_BPE_training
9. Tokenization_Byte_pair_encoding_Implementation_BPE_encoding
10. Tokenization_Byte_pair_encoding_Implementation_BPE_encoding_one_pair
11. Tokenization_Byte_pair_encoding_Implementation_BPE_encoding_one_pair
1. Word_normalization_CaseFolding
2. Word_normalization_Lematization
3. Word_normalization_Stemming
4. Word_normalization_Sentence_segmentation
1. Spelling_correction_Minimum_Edit_Distance_intro
2. Spelling_correction_Minimum_Edit_Distance_Example
3. Spelling_correction_Minimum_Edit_Distance_Table_filling
4. Spelling_correction_Minimum_Edit_Distance_Dynamic_programming
5. Spelling_correction_Minimum_Edit_Distance_psudoCode
6. Spelling_correction_Minimum_Edit_Distance_implementation
7. Spelling_correction_Minimum_Edit_Distance_implementation_bugFixing
8. Spelling_correction_implementation
1. what is a language model
2. language model_formal_definition
3. language model_curse_of_dimensionality
4. language model_markov_assumption_and_n-grams
5. language model_implementation_setup
6. language model_implementation_ngrams_function
7. language model_implementation_updateCounts_function
8. language model_implementation_probabilityModel_funciton
9. language model_implementation_readingCorpus
10. language model_implementation_samplingText
1. one hot vectors
2. one hot vectors_implementaton
3. one hot vectors_limitations
4. one hot vectors_uses_as_target_labeling
5. term_frequency_for_document_representations
6. term_frequency_for_document_representations_implementations
7. term_frequency_for_word_representations
8. TfIdf_for_document_representations
9. TfIdf_for_document_representations_implementation_reading_corpus
10. TfIdf_for_document_representations_implementation_computing_document_frequency
11. TfIdf_for_document_representations_implementation_computing_tfidf
12. TopicModeling_with_TFIDF_1
13. TopicModeling_with_TFIDF_3
14. TopicModeling_with_TFIDF_4
15. TopicModeling_with_TFIDF_5
16. Topic_modeling_with_Gensim
1. wordCoocurrenceMatrix
2. wordCoocurrenceMatrix_vs_wordDocumentMatrix
3. wordCoocurrenceMatrix_implementation_preparing_data
4. wordCoocurrenceMatrix_implementation_preparing_data_2
5. wordCoocurrenceMatrix_implementation_preparing_data_gettingVocabulary
6. wordCoocurrenceMatrix_implementation_final_function
7. wordCoocurrenceMatrix_implementation_handling_memory_issues_on_large_corpus
8. wordCoocurrenceMatrix_sparsity
9. wordCoocurrenceMatrix_positive_point_wise_mutual_information_PPMI
10. PCA_for_dense_embeddings
11. Latent_Semantic_Analysis
12. Latent_Semantic_Analysis_implementation
1. cosine_similarity
2. cosine_similarity_geting_norms_of_vectors
3. cosine_similarity_normalizing_vectors
4. cosine_similarity_with_more_than_one_vectors
5. cosine_similarity_getting_most_similar_words_in_the_vocabulary
6. cosine_similarity_getting_most_similar_words_in_the_vocabulary_fixingBug_of_dimensions
7. cosine_similarity_word2vec_embeddings
8. wordsAnalogies
9. wordsAnalogies_implemenation_1
10. wordsAnalogies_implemenation_2
11. wordsvisualizations
12. wordsvisualizations_implementaion
13. wordsvisualizations_implementaion_2
1. static_and_dynamic_embeddings
2. self_supervision
3. word2vec_algorithm_abstract
4. word2vec_why_negative_sampling
5. word2vec_what_is_skip_gram
6. word2vec_how_to_define_probability_law
7. word2vec_sigmoid
8. word2vec_formalizing_loss_function
9. word2vec_loss_function
10. word2vec_gradient_descent_step
11. word2vec_implemenation_preparing_data
12. word2vec_implemenation_gradient_step
13. word2vec_implemenation_driver_function
1. Why_RNNs_for_NLP
2. PyTorch_installation_and_Tensors_intro
3. automatic_diffrenciation_pytorch
1. Why_DNNs_in_machineLearning
2. Representational_power_and_data_utilization_capacity_of_DNN
3. perceptron
4. perceptron_implementation
5. DNN_architecture
6. DNN_forwardStep_implementation
7. DNN_why_activation_function_is_required
8. DNN_properties_of_activation_function
9. DNN_activation_functions_in_pytorch
10. DNN_what_is_loss_function
11. DNN_loss_function_in_pytorch
1. DNN_Gradient_descent
2. DNN_Gradient_descent_implementation
3. DNN_Gradient_descent_stochastic_batch_minibatch
4. DNN_Gradient_descent_summary
5. DNN_implemenation_gradient_step
6. DNN_implemenation_Stochastic_gradient_descent
7. DNN_implemenation_batch_gradient_descent
8. DNN_implemenation_minibatch_gradient_descent
9. DNN_implemenation_in_PyTorch
1. DNN_weights_initializations
2. DNN_learning_rate
3. DNN_batch_normalization
4. DNN_batch_normalization_implementation
5. DNN_optimizations
6. DNN_dropout
7. DNN_dropout_in_pyTorch
8. DNN_early_stopping
9. DNN_hyperparameters
10. DNN_Pytorch_CIFAR10_example
1. what_is_rnn
2. understanding_rnn_with_a_simple_example
3. RNN_applications_Human_activity_recognition
4. RNN_applications_image_captioning
5. RNN_applications_machine_Translation
6. RNN_applications_speech_recognition_stock_price_prediction
7. RNN_models
1. Language_modeling_Next_word_prediction
2. Language_modeling_Next_word_prediction_vocabulary_index
3. Language_modeling_Next_word_prediction_vocabulary_index_embeddings
4. Language_modeling_Next_word_prediction_RNN_architecture
5. Language_modeling_Next_word_prediction_Python_1
6. Language_modeling_Next_word_prediction_Python_2
7. Language_modeling_Next_word_prediction_Python_3
8. Language_modeling_Next_word_prediction_Python_4
9. Language_modeling_Next_word_prediction_Python_5
10. Language_modeling_Next_word_prediction_Python_6
1. vocabulary_implementation
2. vocabulary_implementation_helpers
3. vocabulary_implementation_from_file
4. Vectorizer
5. RNN_setup
6. RNN_setup
1. RNN_in_PyTorch_intro
2. RNN_in_PyTorch_Embedding_layer
3. RNN_in_PyTorch_nn_RNN
4. RNN_in_PyTorch_output_shapes
5. RNN_in_PyTorch_GatedUnits
6. RNN_in_PyTorch_GatedUnits_GRU_LSTM
7. RNN_in_PyTorch_bidirectional_RNN
8. RNN_in_PyTorch_bidirectional_RNN_output_shapes
9. RNN_in_PyTorch_bidirectional_RNN_output_shapes_seperation
10. RNN_in_PyTorch_example
1. RNN_Encoder_decoder
2. RNN_Attention
1. introduction_to_dataset_and_packages
2. implementing_language_class
3. Testing_language_class_and_implementing_normalization
4. reading_dataFile
5. reading_building_vocabulary
6. EncoderRNN
7. DecoderRNN
8. DecoderRNN_forward_step
9. DecoderRNN_helper_functions
10. trainingModule
11. Stochastic_Gradient_descent
12. NMT_training
13. NMT_evaluation

About this course

$199.99
232 lessons
23.5 hours of video content

Course curriculum

Introduction

Introduction(Regular Expressions)

Meta Characters(Regular Expressions)

Pattern Objects(Regular Expressions)

More Meta Characters(Regular Expressions)

String Modification(Regular Expressions)

Words and Tokens(Text Preprocessing)

Sentiment Classification(Text Preprocessing)

Language Independent Tokenization(Text Preprocessing)

Text Nomalization(Text Preprocessing)

String Matching and Spelling Correction(Text Preprocessing)

Language Modeling

Topic Modelling with Word and Document Representations

Word Embeddings LSI

Word Semantics

Word2vec(Optional)

Need of Deep Learning for NLP(NLP with Deep Learning DNN)

Introduction(NLP with Deep Learning DNN)

Training(NLP with Deep Learning DNN)

Hyper parameters(NLP with Deep Learning DNN)

Introduction(NLP with Deep Learning RNN)

Mini-project Language Modelling(NLP with Deep Learning RNN)

Mini-project Sentiment Classification(NLP with Deep Learning RNN)

RNN in PyTorch(NLP with Deep Learning RNN)

Advanced RNN models(NLP with Deep Learning RNN)

Neural Machine Translation

About this course