title
Complete Road Map To Prepare NLP-Follow This Video-You Will Able to Crack Any DS Interviews🔥🔥

description
In this video we will discussing about the complete road map to prepare for NLP so that you can crack or ace any data science interviews NLP Playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm Complete Deep Learning playlist : https://www.youtube.com/playlist?list=PLZoTAELRMXVPGU70ZGsckrMdr0FteeRUi Bottom Top Approach Of Learning 1.Text Preprocessing Level 1- Tokenization,Lemmatization,StopWords,POS 2.Text Preprocessing Level 2- Bag Of Words, TFIDF, Unigrams,Bigrams,n-grams 3.Text Preprocessing- Gensim,Word2vec,AvgWord2vec 4.Solve Machine Learning Usecases 5.Get the Understanding Of Artificial Neural Network 6.Understanding Recurrent Neural Networks, LSTM,GRU 7.Text Preprocessing Level 3- Word Embeddings, Word2vec 8.Bidirectional LSTM RNN, Encoders And Decoders, Attention Models 9.Transformers 10.BERT ----------------------------------------------------------------------------------------------------------------------- Recording Gears That I Use https://shorturl.at/wzI68 --------------------------------------------------------------------------------------------------------------------------------------------------------- Please donate if you want to support the channel through GPay UPID, Gpay: krishnaik06@okicici Discord Server Link: https://discord.gg/tvAJuuy Telegram link: https://t.me/joinchat/N77M7xRvYUd403DgfE4TWw Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more https://www.youtube.com/channel/UCNU_lfiiWBdtULKOw6X0Dig/join Please do subscribe my other channel too https://www.youtube.com/channel/UCjWY5hREA6FFYrthD0rZNIw Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06 #naturallanguageprocessing #nlp #nlpinterviews #revisionfordatascienceinterviews

detail
{'title': 'Complete Road Map To Prepare NLP-Follow This Video-You Will Able to Crack Any DS Interviews🔥🔥', 'heatmap': [{'end': 182.832, 'start': 100.666, 'weight': 0.875}, {'end': 980.385, 'start': 962.134, 'weight': 0.745}], 'summary': 'Provides a comprehensive roadmap for nlp, emphasizing its importance in data science interviews and covering text pre-processing, word vector conversion, deep learning techniques, and practical applications, with dedicated playlists for nlp and deep learning.', 'chapters': [{'end': 238.886, 'segs': [{'end': 62.154, 'src': 'embed', 'start': 20.762, 'weight': 0, 'content': [{'end': 22.683, 'text': 'very easily with respect to data science.', 'start': 20.762, 'duration': 1.921}, {'end': 28.307, 'text': 'Now, why NLP is important because NLP can be used along with machine learning.', 'start': 23.303, 'duration': 5.004}, {'end': 30.148, 'text': 'It can be used along with deep learning.', 'start': 28.367, 'duration': 1.781}, {'end': 37.474, 'text': 'And from the recent survey, it has found out that, when compared to different, different things that we learn, like machine learning, deep learning,', 'start': 30.889, 'duration': 6.585}, {'end': 42.938, 'text': 'computer vision, right? the task and or use case that involves nlp right.', 'start': 37.474, 'duration': 5.464}, {'end': 45.921, 'text': 'usually recruiters are actually looking for that, you know.', 'start': 42.938, 'duration': 2.983}, {'end': 50.364, 'text': 'so there is a whole new demand with respect to natural language processing.', 'start': 45.921, 'duration': 4.443}, {'end': 53.066, 'text': "so in this video i'll be showing you the path.", 'start': 50.364, 'duration': 2.702}, {'end': 59.612, 'text': "apart from that, i'll also be showing you the playlist from where you actually have to learn natural language processing.", 'start': 53.066, 'duration': 6.546}, {'end': 62.154, 'text': 'So every things will be getting covered.', 'start': 60.092, 'duration': 2.062}], 'summary': 'Nlp is in high demand, recruiters seek it, as per recent survey.', 'duration': 41.392, 'max_score': 20.762, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak20762.jpg'}, {'end': 185.315, 'src': 'heatmap', 'start': 93.038, 'weight': 2, 'content': [{'end': 100.046, 'text': "So I've created this diagram, guys, and in this particular diagram you actually have to follow bottom to top approach, okay?", 'start': 93.038, 'duration': 7.008}, {'end': 105.507, 'text': "So from the bottom I've actually written over here text pre-processing level one.", 'start': 100.666, 'duration': 4.841}, {'end': 107.968, 'text': 'So what is natural language processing?', 'start': 106.107, 'duration': 1.861}, {'end': 108.428, 'text': 'in short?', 'start': 107.968, 'duration': 0.46}, {'end': 114.509, 'text': 'Whenever your data is having words, sentences you know or paragraphs at that time,', 'start': 108.508, 'duration': 6.001}, {'end': 121.191, 'text': 'usually machine learning model will not be machine learning model or deep learning model will just not be able to understand those text.', 'start': 114.509, 'duration': 6.682}, {'end': 125.472, 'text': 'Right So we need to convert those text into vectors.', 'start': 121.731, 'duration': 3.741}, {'end': 131.64, 'text': 'And there are a lot of process, different ways to actually convert words into vectors.', 'start': 126.132, 'duration': 5.508}, {'end': 140.073, 'text': "So with respect to this, initially, whenever you're starting with NLP, first of all, I like to specify all the libraries that you can actually use.", 'start': 132.121, 'duration': 7.952}, {'end': 147.139, 'text': 'With respect to machine learning, Very good libraries like Spacey and Natural Language Analysis with NLTK.', 'start': 140.393, 'duration': 6.746}, {'end': 149.321, 'text': 'So NLTK library is also there.', 'start': 147.46, 'duration': 1.861}, {'end': 150.722, 'text': 'You can also use this.', 'start': 149.701, 'duration': 1.021}, {'end': 151.842, 'text': 'This is by Stanford.', 'start': 150.762, 'duration': 1.08}, {'end': 156.466, 'text': "And Spacey also is another library where you'll be able to do many, many tasks.", 'start': 152.223, 'duration': 4.243}, {'end': 162.71, 'text': 'In deep learning sections, you definitely know the libraries that we can use like PyTorch, Keras, TensorFlow.', 'start': 157.026, 'duration': 5.684}, {'end': 170.255, 'text': "Apart from that, if you have Transformers right or BERT, whenever you're implementing something with respect to Transformers and BERT,", 'start': 163.19, 'duration': 7.065}, {'end': 174.08, 'text': 'At that time you have something called as Hugging Face Library, okay?', 'start': 170.655, 'duration': 3.425}, {'end': 182.832, 'text': 'And similarly, with respect to most of the transformer tasks or BERT, you know sentence classifications and Spam Ham, different type of use cases.', 'start': 174.641, 'duration': 8.191}, {'end': 185.315, 'text': 'there is also a library which is called as K-Train.', 'start': 182.832, 'duration': 2.483}], 'summary': 'Nlp involves converting text into vectors using libraries like nltk, spacey, pytorch, keras, tensorflow, bert, and hugging face.', 'duration': 92.277, 'max_score': 93.038, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak93038.jpg'}, {'end': 182.832, 'src': 'embed', 'start': 152.223, 'weight': 3, 'content': [{'end': 156.466, 'text': "And Spacey also is another library where you'll be able to do many, many tasks.", 'start': 152.223, 'duration': 4.243}, {'end': 162.71, 'text': 'In deep learning sections, you definitely know the libraries that we can use like PyTorch, Keras, TensorFlow.', 'start': 157.026, 'duration': 5.684}, {'end': 170.255, 'text': "Apart from that, if you have Transformers right or BERT, whenever you're implementing something with respect to Transformers and BERT,", 'start': 163.19, 'duration': 7.065}, {'end': 174.08, 'text': 'At that time you have something called as Hugging Face Library, okay?', 'start': 170.655, 'duration': 3.425}, {'end': 182.832, 'text': 'And similarly, with respect to most of the transformer tasks or BERT, you know sentence classifications and Spam Ham, different type of use cases.', 'start': 174.641, 'duration': 8.191}], 'summary': 'Spacey library enables various tasks, including deep learning with pytorch, keras, tensorflow, transformers, bert, and hugging face library for sentence classification and use cases.', 'duration': 30.609, 'max_score': 152.223, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak152223.jpg'}, {'end': 221.178, 'src': 'embed', 'start': 197.324, 'weight': 4, 'content': [{'end': 206.812, 'text': 'Now, the next part that is in this coming week, I will be actually covering both BERT and Transformers theoretical part and also the practical part.', 'start': 197.324, 'duration': 9.488}, {'end': 210.553, 'text': 'Theoretical part of transformer is already completed.', 'start': 207.552, 'duration': 3.001}, {'end': 216.396, 'text': "For the BERT, I'm just preparing the material so that I'll be able to showcase all the things that we can actually do in BERT.", 'start': 210.994, 'duration': 5.402}, {'end': 221.178, 'text': "So probably in another one week, I'll be able to complete BERT also.", 'start': 216.836, 'duration': 4.342}], 'summary': 'Covering theoretical and practical aspects of bert and transformers in the coming week, with bert expected to be completed in one week.', 'duration': 23.854, 'max_score': 197.324, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak197324.jpg'}], 'start': 0.029, 'title': 'Nlp essentials', 'summary': "Covers nlp roadmap, emphasizing its importance in data science interviews and increasing demand for nlp skills. it also discusses text pre-processing's significance in nlp and the use of nlp libraries, including upcoming coverage of bert and transformers.", 'chapters': [{'end': 92.478, 'start': 0.029, 'title': 'Nlp roadmap & importance', 'summary': "Discusses the roadmap for preparing for nlp, emphasizing its importance in data science interviews and the increasing demand for nlp skills, as evidenced by recent surveys and the speaker's personal experience in mnc interviews.", 'duration': 92.449, 'highlights': ["NLP skills are in high demand, as evidenced by recent surveys and the speaker's personal experience in MNC interviews.", "The roadmap for preparing for NLP is crucial for clearing data science interviews, as per the speaker's recommendation.", 'Recruiters are increasingly looking for NLP skills when compared to other skills like machine learning, deep learning, and computer vision, as mentioned in a recent survey.', 'The speaker emphasizes the importance of NLP in data science interviews, as it has been a frequently asked topic in various MNC interviews.']}, {'end': 131.64, 'start': 93.038, 'title': 'Nlp text pre-processing', 'summary': 'Discusses the importance of text pre-processing in natural language processing, emphasizing the need to convert text into vectors for machine learning models to understand, with different approaches for the conversion.', 'duration': 38.602, 'highlights': ['The necessity of converting text into vectors for machine learning model comprehension.', 'Emphasizing the importance of text pre-processing in natural language processing.', 'Different ways to convert words into vectors for machine learning models.']}, {'end': 238.886, 'start': 132.121, 'title': 'Nlp libraries and future coverage', 'summary': 'Discusses nlp libraries like spacey, nltk, pytorch, keras, tensorflow, bert, hugging face, and k-train, and outlines the upcoming coverage of bert and transformers.', 'duration': 106.765, 'highlights': ['The chapter discusses NLP libraries like Spacey, NLTK, PyTorch, Keras, TensorFlow, BERT, Hugging Face, and K-Train.', 'The upcoming coverage includes theoretical and practical parts of BERT and Transformers.', 'The chapter emphasizes a bottom-top approach for beginners in NLP and data science.']}], 'duration': 238.857, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak29.jpg', 'highlights': ['NLP skills are crucial for clearing data science interviews.', 'Recruiters increasingly seek NLP skills over other skills like ML and DL.', 'Text pre-processing is vital in natural language processing.', 'The chapter discusses NLP libraries like Spacey, NLTK, PyTorch, Keras, TensorFlow, BERT, Hugging Face, and K-Train.', 'The upcoming coverage includes theoretical and practical parts of BERT and Transformers.']}, {'end': 437.486, 'segs': [{'end': 284.571, 'src': 'embed', 'start': 258.933, 'weight': 0, 'content': [{'end': 265.476, 'text': 'So what is the exact difference between stemming and lemmatization? What is stop words? What is POS? and many more things.', 'start': 258.933, 'duration': 6.543}, {'end': 274.023, 'text': "So it's this simple text pre-processing that we usually do as soon as we get some kind of data sets which involve text data.", 'start': 265.516, 'duration': 8.507}, {'end': 278.586, 'text': "So this is what I've actually written with respect to text pre-processing level one.", 'start': 274.423, 'duration': 4.163}, {'end': 284.571, 'text': 'And please make sure that you understand all these things in this specific way, because in this specific way only,', 'start': 279.046, 'duration': 5.525}], 'summary': 'Text preprocessing involves stemming, lemmatization, stop words, and pos tagging.', 'duration': 25.638, 'max_score': 258.933, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak258933.jpg'}, {'end': 323.182, 'src': 'embed', 'start': 300.183, 'weight': 1, 'content': [{'end': 307.87, 'text': 'So in the next step, that is text processing level two, we usually focus how we can actually convert words into vectors.', 'start': 300.183, 'duration': 7.687}, {'end': 316.477, 'text': 'There are various, various ways, guys, and probably you need to know the maths behind like how we actually converting word into vectors.', 'start': 308.47, 'duration': 8.007}, {'end': 319.499, 'text': 'OK, so there are a lot of techniques like one hot encoding.', 'start': 316.817, 'duration': 2.682}, {'end': 321.901, 'text': 'You can actually use something called a bag of words.', 'start': 319.539, 'duration': 2.362}, {'end': 323.182, 'text': "I've written it over here.", 'start': 322.221, 'duration': 0.961}], 'summary': 'Text processing level two focuses on converting words into vectors using techniques like one hot encoding and bag of words.', 'duration': 22.999, 'max_score': 300.183, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak300183.jpg'}], 'start': 238.886, 'title': 'Text pre-processing and word vector conversion in nlp', 'summary': 'Covers text preprocessing level one, including tokenization, lemmatization, stemming, stop words, and pos, and also discusses converting words into vectors in nlp using techniques like one hot encoding, bag of words, tfidf, gen sim, word2vec, and average word2vec, essential for interview preparation and improving performance and accuracy.', 'chapters': [{'end': 278.586, 'start': 238.886, 'title': 'Text pre-processing level one', 'summary': 'Covers text preprocessing level one, focusing on tokenization, lemmatization, stemming, stop words, and pos, essential for interview preparation and working with text data.', 'duration': 39.7, 'highlights': ['The chapter covers text preprocessing level one, focusing on tokenization, lemmatization, stemming, stop words, and POS, essential for interview preparation and working with text data.', 'It explains the exact difference between stemming and lemmatization, and the concept of stop words and POS, which are essential for text data processing.', 'Text pre-processing level one is crucial for interview preparation and working with text data, aiding in understanding the concepts of tokenization, lemmatization, stemming, stop words, and POS.']}, {'end': 437.486, 'start': 279.046, 'title': 'Converting words to vectors in nlp', 'summary': 'Discusses the process of converting words into vectors in natural language processing, including techniques like one hot encoding, bag of words, tfidf, unigrams, bigrams, engrams, and advanced methods like gen sim, word2vec, and average word2vec, which are crucial for improving performance and accuracy in nlp.', 'duration': 158.44, 'highlights': ['The chapter emphasizes the importance of understanding the process of converting words into vectors, which is crucial for machine learning algorithms to comprehend the context of sentences and improve accuracy.', 'Various techniques for converting words into vectors are discussed, including one hot encoding, bag of words, TFIDF, unigrams, bigrams, engrams, gen sim, Word2vec, and Average Word2vec, with an emphasis on the improved performance of the advanced methods over bag of words and TFIDF.', 'The speaker refers to the relevance of the discussed techniques by highlighting that they have been uploaded in the natural language processing playlist and are crucial for interviews, as advanced techniques like gen sim, Word2vec, and Average Word2vec outperform bag of words and TFIDF.']}], 'duration': 198.6, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak238886.jpg', 'highlights': ['The chapter covers text preprocessing level one, focusing on tokenization, lemmatization, stemming, stop words, and POS, essential for interview preparation and working with text data.', 'Various techniques for converting words into vectors are discussed, including one hot encoding, bag of words, TFIDF, unigrams, bigrams, engrams, gen sim, Word2vec, and Average Word2vec, with an emphasis on the improved performance of the advanced methods over bag of words and TFIDF.']}, {'end': 970.178, 'segs': [{'end': 480.387, 'src': 'embed', 'start': 438.066, 'weight': 0, 'content': [{'end': 446.47, 'text': 'Then, after you complete this, and remember, in the initial stage, I told you, right, NLP can be used in both machine learning and deep learning.', 'start': 438.066, 'duration': 8.404}, {'end': 450.792, 'text': 'So after you complete this, you have to solve some of the machine learning use cases.', 'start': 446.49, 'duration': 4.302}, {'end': 455.354, 'text': 'In that use cases, suppose if I take as an example sentiment classifier.', 'start': 451.292, 'duration': 4.062}, {'end': 458.816, 'text': 'spam ham classifier, you know, documents classifier.', 'start': 455.814, 'duration': 3.002}, {'end': 464.679, 'text': 'So all this kind of simple, simple use cases can be solved with the help of machine learning algorithms.', 'start': 459.136, 'duration': 5.543}, {'end': 473.923, 'text': 'And the algorithms that we basically use are like nape bias classifier and other kind of multinomial nape bias classifier for multi-class classification.', 'start': 465.019, 'duration': 8.904}, {'end': 480.387, 'text': 'And again, those practical videos also I have uploaded with respect to in my natural language play.', 'start': 474.324, 'duration': 6.063}], 'summary': 'Nlp can be applied in machine learning and deep learning for solving use cases like sentiment classifier, spam ham classifier, and document classifier using algorithms like nape bias classifier and multinomial nape bias classifier.', 'duration': 42.321, 'max_score': 438.066, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak438066.jpg'}, {'end': 607.871, 'src': 'embed', 'start': 579.878, 'weight': 3, 'content': [{'end': 586.72, 'text': 'because whenever we have sentences of words or words of sentences right at that time, those are coming in sequences right.', 'start': 579.878, 'duration': 6.842}, {'end': 592.142, 'text': 'so machine learning, translation you know, language translation can be actually done very, very easily.', 'start': 586.72, 'duration': 5.422}, {'end': 598.285, 'text': 'some of the use cases are chat bots, question answering session, the question answering application.', 'start': 592.142, 'duration': 6.143}, {'end': 601.606, 'text': 'all those things can be easily solved with the help of recurrent neural network.', 'start': 598.285, 'duration': 3.321}, {'end': 607.871, 'text': 'right. so, in order to understand that you have the basic strong with respect to deep learning.', 'start': 602.026, 'duration': 5.845}], 'summary': 'Machine learning enables easy language translation for use cases like chat bots, question answering, and recurrent neural networks.', 'duration': 27.993, 'max_score': 579.878, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak579878.jpg'}, {'end': 753.68, 'src': 'embed', 'start': 726.202, 'weight': 1, 'content': [{'end': 729.124, 'text': 'So because of that, we use bidirectional LSTM-RNN.', 'start': 726.202, 'duration': 2.922}, {'end': 734.767, 'text': 'And then we also use something called a sequence-to-sequence neural networks like encoders and decoders.', 'start': 729.564, 'duration': 5.203}, {'end': 738.049, 'text': 'And there is also something called a self-attention models.', 'start': 735.147, 'duration': 2.902}, {'end': 742.672, 'text': 'everything has been covered in my playlist.', 'start': 738.869, 'duration': 3.803}, {'end': 749.937, 'text': 'with theoretical understanding, with practical intuition, trust me, you just try to follow in this specific manner,', 'start': 742.672, 'duration': 7.265}, {'end': 751.858, 'text': 'you will be able to understand everything.', 'start': 749.937, 'duration': 1.921}, {'end': 753.68, 'text': 'okay. so bidirectional LSTM.', 'start': 751.858, 'duration': 1.822}], 'summary': 'Utilize bidirectional lstm and sequence-to-sequence neural networks for comprehensive understanding.', 'duration': 27.478, 'max_score': 726.202, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak726202.jpg'}, {'end': 954.41, 'src': 'embed', 'start': 930.554, 'weight': 4, 'content': [{'end': 937.819, 'text': "Then again, BERT is again a simplified version of transformer, where you're solving some more extreme, complex problems right?", 'start': 930.554, 'duration': 7.265}, {'end': 942.302, 'text': 'And, as I said, the libraries that you can actually use is PyTorch, Keras and TensorFlow.', 'start': 938.319, 'duration': 3.983}, {'end': 944.503, 'text': 'You have spaCy and NLTK also.', 'start': 942.582, 'duration': 1.921}, {'end': 951.828, 'text': "In my playlist, I've actually completed with NLTK, with Keras and PyTorch playlist also, I'm actually preparing it.", 'start': 945.404, 'duration': 6.424}, {'end': 954.41, 'text': 'But at least Keras and TensorFlow is more than sufficient.', 'start': 952.068, 'duration': 2.342}], 'summary': 'Bert is a simplified version of transformer, used with pytorch, keras, tensorflow, spacy, and nltk for solving complex problems.', 'duration': 23.856, 'max_score': 930.554, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak930554.jpg'}], 'start': 438.066, 'title': 'Nlp and deep learning', 'summary': 'Explores the application of nlp in machine learning for sentiment classification, spam/ham classification, and document classification using naive bayes and multinomial naive bayes. it also emphasizes understanding deep learning techniques such as ann, cnn, rnn, lstm, gru, word embeddings, bidirectional lstm, encoders and decoders, self-attention models, transformers, and bert, with practical applications and recommended libraries including pytorch, keras, and tensorflow.', 'chapters': [{'end': 480.387, 'start': 438.066, 'title': 'Nlp in machine learning', 'summary': 'Discusses the application of nlp in machine learning, highlighting the use of nlp in solving machine learning use cases such as sentiment classification, spam/ham classification, and document classification using algorithms like naive bayes and multinomial naive bayes.', 'duration': 42.321, 'highlights': ['NLP can be used in both machine learning and deep learning, and it can be applied to solve use cases such as sentiment classification, spam/ham classification, and document classification.', 'Machine learning algorithms like Naive Bayes and multinomial Naive Bayes are used to solve simple use cases in NLP.', 'Practical videos related to the topic have been uploaded in the natural language play.']}, {'end': 970.178, 'start': 480.447, 'title': 'Deep learning techniques and applications', 'summary': 'Emphasizes the importance of understanding deep learning techniques such as ann, cnn, rnn, lstm, gru, word embeddings, bidirectional lstm, encoders and decoders, self-attention models, transformers, and bert, along with practical applications including machine learning use cases and language translation, recommending the use of pytorch, keras, and tensorflow libraries.', 'duration': 489.731, 'highlights': ['Understanding deep learning techniques such as ANN, CNN, RNN, LSTM, GRU, word embeddings, bidirectional LSTM, encoders and decoders, self-attention models, transformers, and BERT The chapter emphasizes the importance of understanding deep learning techniques, including various neural network architectures and models such as LSTM, GRU, word embeddings, bidirectional LSTM, encoders and decoders, self-attention models, transformers, and BERT.', 'Practical applications including machine learning use cases and language translation The transcript stresses the practical applications of deep learning techniques, including machine learning use cases and language translation, demonstrating the real-world relevance of the concepts being taught.', 'Recommendation of PyTorch, Keras, and TensorFlow libraries for implementation The chapter recommends the use of PyTorch, Keras, and TensorFlow libraries for implementation, highlighting their suitability for applying the discussed concepts in practice.']}], 'duration': 532.112, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak438066.jpg', 'highlights': ['NLP can be used in both machine learning and deep learning for sentiment classification, spam/ham classification, and document classification.', 'Understanding deep learning techniques such as ANN, CNN, RNN, LSTM, GRU, word embeddings, bidirectional LSTM, encoders and decoders, self-attention models, transformers, and BERT is emphasized.', 'Machine learning algorithms like Naive Bayes and multinomial Naive Bayes are used to solve simple use cases in NLP.', 'Practical applications including machine learning use cases and language translation are stressed.', 'The chapter recommends the use of PyTorch, Keras, and TensorFlow libraries for implementation.']}, {'end': 1252.766, 'segs': [{'end': 1010.227, 'src': 'embed', 'start': 970.758, 'weight': 0, 'content': [{'end': 974.181, 'text': 'Now coming to my playlist how you should follow these particular videos.', 'start': 970.758, 'duration': 3.423}, {'end': 977.463, 'text': "So what I've done is that guys, I've created two dedicated playlists.", 'start': 974.541, 'duration': 2.922}, {'end': 980.385, 'text': 'One is natural language processing and one is complete deep learning.', 'start': 977.563, 'duration': 2.822}, {'end': 981.626, 'text': 'So first of all, you see this.', 'start': 980.425, 'duration': 1.201}, {'end': 984.508, 'text': "I've started with tokenization, stemming, lemmatization.", 'start': 982.026, 'duration': 2.482}, {'end': 986.61, 'text': 'So stemming, practical intuition.', 'start': 984.969, 'duration': 1.641}, {'end': 988.651, 'text': 'This is lemmatization, practical intuition.', 'start': 986.65, 'duration': 2.001}, {'end': 990.613, 'text': 'This is theoretical intuition.', 'start': 988.711, 'duration': 1.902}, {'end': 994.215, 'text': 'So you can also see bag of words intuition.', 'start': 991.173, 'duration': 3.042}, {'end': 998.338, 'text': 'bag of words practical implementation TF-IDF intuition.', 'start': 994.215, 'duration': 4.123}, {'end': 1000.68, 'text': 'TF-IDF practical implementation.', 'start': 998.338, 'duration': 2.342}, {'end': 1003.822, 'text': 'then some machine learning use cases.', 'start': 1000.68, 'duration': 3.142}, {'end': 1005.284, 'text': 'then we went to word2vec.', 'start': 1003.822, 'duration': 1.462}, {'end': 1007.605, 'text': 'then again some machine learning use cases.', 'start': 1005.284, 'duration': 2.321}, {'end': 1009.146, 'text': 'here also machine learning use cases.', 'start': 1007.605, 'duration': 1.541}, {'end': 1010.227, 'text': 'machine learning use cases.', 'start': 1009.146, 'duration': 1.081}], 'summary': 'Created two dedicated playlists: natural language processing and complete deep learning, covering topics like tokenization, stemming, lemmatization, bag of words, tf-idf, word2vec, and machine learning use cases.', 'duration': 39.469, 'max_score': 970.758, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak970758.jpg'}, {'end': 1181.38, 'src': 'embed', 'start': 1157.161, 'weight': 3, 'content': [{'end': 1163.426, 'text': 'guys, and this videos, this videos that you will be seeing over here, whatever deep learning videos I have added over here,', 'start': 1157.161, 'duration': 6.265}, {'end': 1165.528, 'text': 'it is also present in this deep learning playlist.', 'start': 1163.426, 'duration': 2.102}, {'end': 1170.172, 'text': 'So here you can see problems with encoder, decoder and sequence to sequence learning.', 'start': 1165.588, 'duration': 4.584}, {'end': 1175.536, 'text': 'implement fake news classifier using bidirectional LSTM, bidirectional RNN intuition.', 'start': 1170.172, 'duration': 5.364}, {'end': 1181.38, 'text': "So what I'm going to do is that I'm just also going to add this in this particular playlist that is natural language processing.", 'start': 1175.836, 'duration': 5.544}], 'summary': 'Deep learning videos cover encoder, decoder, and sequence to sequence learning, including a fake news classifier using bidirectional lstm and rnn intuition.', 'duration': 24.219, 'max_score': 1157.161, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak1157161.jpg'}, {'end': 1229.249, 'src': 'embed', 'start': 1198.313, 'weight': 5, 'content': [{'end': 1201.095, 'text': 'Okay? So please make sure that you just follow these guys.', 'start': 1198.313, 'duration': 2.782}, {'end': 1205.279, 'text': "Trust me, I'll guarantee you, you will ace any data science interview.", 'start': 1201.796, 'duration': 3.483}, {'end': 1211.021, 'text': 'Now with respect to interview, natural language processing, many companies are actually working in it.', 'start': 1205.859, 'duration': 5.162}, {'end': 1219.685, 'text': 'If I talk about machine learning, deep learning, if I talk about computer vision, right? Natural languages will be always in the top priority.', 'start': 1211.301, 'duration': 8.384}, {'end': 1223.146, 'text': 'Then you have computer vision, then you have deep learning, then you have machine learning.', 'start': 1219.785, 'duration': 3.361}, {'end': 1224.467, 'text': 'this kind of techniques, okay?', 'start': 1223.146, 'duration': 1.321}, {'end': 1229.249, 'text': 'So please make sure that you start preparing for this and NLP can be used.', 'start': 1225.007, 'duration': 4.242}], 'summary': 'Prepare for data science interviews with focus on nlp, machine learning, and computer vision to ace interviews.', 'duration': 30.936, 'max_score': 1198.313, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak1198313.jpg'}], 'start': 970.758, 'title': 'Nlp and deep learning playlists', 'summary': 'Discusses the creation of dedicated playlists for nlp and complete deep learning, covering topics such as tokenization, stemming, lemmatization, bag of words, tf-idf, word2vec, and machine learning use cases.', 'chapters': [{'end': 1010.227, 'start': 970.758, 'title': 'Nlp and deep learning playlists', 'summary': 'Discusses the creation of two dedicated playlists for natural language processing and complete deep learning, covering topics such as tokenization, stemming, lemmatization, bag of words, tf-idf, word2vec, and machine learning use cases.', 'duration': 39.469, 'highlights': ['The chapter discusses the creation of two dedicated playlists for natural language processing and complete deep learning, with a focus on topics such as tokenization, stemming, lemmatization, bag of words, TF-IDF, word2vec, and machine learning use cases.', 'The playlists cover various topics including tokenization, stemming, lemmatization, bag of words, TF-IDF, word2vec, and machine learning use cases.', 'The chapter emphasizes practical and theoretical intuition for stemming, lemmatization, bag of words, and TF-IDF, providing a comprehensive understanding of these concepts.', 'It includes a progression from basic concepts like tokenization to more advanced topics like word2vec and machine learning use cases.']}, {'end': 1252.766, 'start': 1011.428, 'title': 'Deep learning and nlp overview', 'summary': 'Covers a comprehensive overview of deep learning and nlp, including tutorials on rnn, lstm, word embedding, encoder-decoder, attention models, and a practical use case of fake news classification using bidirectional lstm.', 'duration': 241.338, 'highlights': ['Practical use case of fake news classification using bidirectional LSTM The speaker provides a practical use case of fake news classification using bidirectional LSTM, demonstrating the practical application of the concept.', 'Tutorials on RNN, LSTM, word embedding, encoder-decoder, and attention models The chapter includes tutorials on various deep learning concepts such as RNN, LSTM, word embedding, encoder-decoder, and attention models, providing a comprehensive learning resource for the audience.', 'Importance of NLP in data science interviews and industry Emphasizing the importance of NLP in data science interviews and industry, the speaker highlights its relevance in the context of machine learning, deep learning, and computer vision.']}], 'duration': 282.008, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/fM4qTMfCoak/pics/fM4qTMfCoak970758.jpg', 'highlights': ['The chapter discusses the creation of two dedicated playlists for natural language processing and complete deep learning, covering topics such as tokenization, stemming, lemmatization, bag of words, TF-IDF, word2vec, and machine learning use cases.', 'The chapter emphasizes practical and theoretical intuition for stemming, lemmatization, bag of words, and TF-IDF, providing a comprehensive understanding of these concepts.', 'The playlists cover various topics including tokenization, stemming, lemmatization, bag of words, TF-IDF, word2vec, and machine learning use cases.', 'The chapter includes tutorials on various deep learning concepts such as RNN, LSTM, word embedding, encoder-decoder, and attention models, providing a comprehensive learning resource for the audience.', 'Practical use case of fake news classification using bidirectional LSTM, demonstrating the practical application of the concept.', 'Emphasizing the importance of NLP in data science interviews and industry, highlighting its relevance in the context of machine learning, deep learning, and computer vision.', 'It includes a progression from basic concepts like tokenization to more advanced topics like word2vec and machine learning use cases.']}], 'highlights': ['NLP skills are crucial for clearing data science interviews.', 'Recruiters increasingly seek NLP skills over other skills like ML and DL.', 'Text pre-processing is vital in natural language processing.', 'The chapter discusses NLP libraries like Spacey, NLTK, PyTorch, Keras, TensorFlow, BERT, Hugging Face, and K-Train.', 'The upcoming coverage includes theoretical and practical parts of BERT and Transformers.', 'Various techniques for converting words into vectors are discussed, including one hot encoding, bag of words, TFIDF, unigrams, bigrams, engrams, gen sim, Word2vec, and Average Word2vec, with an emphasis on the improved performance of the advanced methods over bag of words and TFIDF.', 'NLP can be used in both machine learning and deep learning for sentiment classification, spam/ham classification, and document classification.', 'Understanding deep learning techniques such as ANN, CNN, RNN, LSTM, GRU, word embeddings, bidirectional LSTM, encoders and decoders, self-attention models, transformers, and BERT is emphasized.', 'Machine learning algorithms like Naive Bayes and multinomial Naive Bayes are used to solve simple use cases in NLP.', 'Practical applications including machine learning use cases and language translation are stressed.', 'The chapter recommends the use of PyTorch, Keras, and TensorFlow libraries for implementation.', 'The chapter discusses the creation of two dedicated playlists for natural language processing and complete deep learning, covering topics such as tokenization, stemming, lemmatization, bag of words, TF-IDF, word2vec, and machine learning use cases.', 'The chapter emphasizes practical and theoretical intuition for stemming, lemmatization, bag of words, and TF-IDF, providing a comprehensive understanding of these concepts.', 'The playlists cover various topics including tokenization, stemming, lemmatization, bag of words, TF-IDF, word2vec, and machine learning use cases.', 'The chapter includes tutorials on various deep learning concepts such as RNN, LSTM, word embedding, encoder-decoder, and attention models, providing a comprehensive learning resource for the audience.', 'Practical use case of fake news classification using bidirectional LSTM, demonstrating the practical application of the concept.', 'Emphasizing the importance of NLP in data science interviews and industry, highlighting its relevance in the context of machine learning, deep learning, and computer vision.', 'It includes a progression from basic concepts like tokenization to more advanced topics like word2vec and machine learning use cases.']}