title
Complete Roadmap To Follow To Prepare Machine Learning With All Videos And Materials

description
In this video I will how you the complete roadmap to follow to prepare machine learning with all videos and materials Register now for the upcoming DLCVNLP course in collaboration with Ineuron Mentors: Krish And Sudhanhu Register now:- https://ineuron1.viewpage.co/Deep-learning-with-computer-vision-and-advance-NLP-ineuron All the details are given in the above link All Playlist In My channel Complete ML Playlist :https://www.youtube.com/playlist?list=PLZoTAELRMXVPBTrWtJkn3wWQxZkmTXGwe Complete NLP Playlist:https://www.youtube.com/playlist?list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm Docker End To End Implementation: https://www.youtube.com/playlist?list=PLZoTAELRMXVNKtpy0U_Mx9N26w8n0hIbs Live stream Playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVNxYFq_9MuiUdn2YnlFqmMK Machine Learning Pipelines: https://www.youtube.com/playlist?list=PLZoTAELRMXVNKtpy0U_Mx9N26w8n0hIbs Pytorch Playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVNxYFq_9MuiUdn2YnlFqmMK Feature Engineering :https://www.youtube.com/playlist?list=PLZoTAELRMXVPwYGE2PXD3x0bfKnR0cJjN Live Projects :https://www.youtube.com/playlist?list=PLZoTAELRMXVOFnfSwkB_uyr4FT-327noK Kaggle competition :https://www.youtube.com/playlist?list=PLZoTAELRMXVPiKOxbwaniXjHJ02bdkLWy Mongodb with Python :https://www.youtube.com/playlist?list=PLZoTAELRMXVN_8zzsevm1bm6G-plsiO1I MySQL With Python :https://www.youtube.com/playlist?list=PLZoTAELRMXVMd3RF7p-u7ezEysGaG9JmO Deployment Architectures:https://www.youtube.com/playlist?list=PLZoTAELRMXVOPzVJiSJAn9Ly27Fi1-8ac Amazon sagemaker :https://www.youtube.com/playlist?list=PLZoTAELRMXVONh5mHrXowH6-dgyWoC_Ew 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 #machinelearning #machinelearningengineer

detail
{'title': 'Complete Roadmap To Follow To Prepare Machine Learning With All Videos And Materials', 'heatmap': [{'end': 605.164, 'start': 562.23, 'weight': 0.701}, {'end': 843.499, 'start': 817.63, 'weight': 0.844}, {'end': 876.474, 'start': 860.446, 'weight': 0.749}], 'summary': 'Provides a comprehensive roadmap for learning machine learning, covering the significance of machine learning in data science, the essential steps of a machine learning project, including data analysis, feature engineering, and model deployment using docker and kubernetes, along with a 45-day blueprint for learning data science and securing jobs.', 'chapters': [{'end': 88.216, 'segs': [{'end': 88.216, 'src': 'embed', 'start': 35.904, 'weight': 0, 'content': [{'end': 38.486, 'text': 'If you try to follow this particular path, it will be very, very good to you.', 'start': 35.904, 'duration': 2.582}, {'end': 42.309, 'text': "And apart from this, I'll also show you from where you can actually learn all these things.", 'start': 38.686, 'duration': 3.623}, {'end': 44.991, 'text': "I've created some amazing playlists on machine learning.", 'start': 42.829, 'duration': 2.162}, {'end': 47.973, 'text': "I've created so many videos on machine learning also.", 'start': 45.011, 'duration': 2.962}, {'end': 50.294, 'text': "So I'll tell you what playlists you have to follow.", 'start': 48.493, 'duration': 1.801}, {'end': 54.817, 'text': 'And please make sure that you follow those playlists to be very, very good at machine learning itself.', 'start': 51.275, 'duration': 3.542}, {'end': 57.078, 'text': 'So let me just go and show you the path.', 'start': 55.217, 'duration': 1.861}, {'end': 66.383, 'text': "So this is an amazing diagram that I've actually created based on my experience, like what should be the path that you should take.", 'start': 57.818, 'duration': 8.565}, {'end': 69.045, 'text': "I've also mentioned about various things.", 'start': 66.964, 'duration': 2.081}, {'end': 70.546, 'text': "So let's go from the bottom.", 'start': 69.125, 'duration': 1.421}, {'end': 75.168, 'text': "So in the bottom, you'll be able to see that we have programming languages like Python and R.", 'start': 71.006, 'duration': 4.162}, {'end': 78.47, 'text': 'So these programming languages are pretty much important.', 'start': 76.229, 'duration': 2.241}, {'end': 80.451, 'text': 'You can either select Python and R.', 'start': 78.67, 'duration': 1.781}, {'end': 81.912, 'text': 'But if you really want to go,', 'start': 80.451, 'duration': 1.461}, {'end': 88.216, 'text': 'as a data scientist itself and probably in the future you are planning to learn deep learning I would suggest go with Python programming language.', 'start': 81.912, 'duration': 6.304}], 'summary': 'Follow specific playlists to excel in machine learning. python recommended for data science and deep learning.', 'duration': 52.312, 'max_score': 35.904, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy035904.jpg'}], 'start': 0.069, 'title': 'Machine learning roadmap', 'summary': 'Covers the significance of machine learning in data science, the requirement for expertise in python and r, and offers a roadmap for learning machine learning concepts and resources.', 'chapters': [{'end': 88.216, 'start': 0.069, 'title': 'Machine learning roadmap', 'summary': 'Covers the importance of machine learning in data science, the need for proficiency in programming languages like python and r, and provides a comprehensive roadmap for learning machine learning concepts and resources.', 'duration': 88.147, 'highlights': ['The chapter emphasizes the significance of machine learning in becoming a data scientist and highlights the importance of proficiency in programming languages like Python and R.', 'The speaker provides a detailed roadmap for learning machine learning concepts, emphasizing the critical path to follow for success in the field.', 'The video content includes playlists and resources for learning machine learning, offering a comprehensive guide for individuals seeking proficiency in the subject.', 'The presenter advocates for Python as the programming language of choice for individuals aspiring to become data scientists.']}], 'duration': 88.147, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy069.jpg', 'highlights': ['The chapter emphasizes the significance of machine learning in becoming a data scientist and highlights the importance of proficiency in programming languages like Python and R.', 'The presenter advocates for Python as the programming language of choice for individuals aspiring to become data scientists.', 'The speaker provides a detailed roadmap for learning machine learning concepts, emphasizing the critical path to follow for success in the field.', 'The video content includes playlists and resources for learning machine learning, offering a comprehensive guide for individuals seeking proficiency in the subject.']}, {'end': 444.951, 'segs': [{'end': 152.403, 'src': 'embed', 'start': 126.449, 'weight': 0, 'content': [{'end': 132.632, 'text': 'Then the next step that we will be focusing on, we should focus on is basically the feature selection.', 'start': 126.449, 'duration': 6.183}, {'end': 137.215, 'text': "In feature selection, again, if you don't remember, guys, I've also started a playlist on feature selection.", 'start': 132.733, 'duration': 4.482}, {'end': 140.677, 'text': 'I already have a playlist on feature engineering, right?', 'start': 138.015, 'duration': 2.662}, {'end': 146.58, 'text': 'So in the feature selection you have methods like correlation forward elimination, backward elimination, univariate selections,', 'start': 141.057, 'duration': 5.523}, {'end': 147.741, 'text': 'random forest importance.', 'start': 146.58, 'duration': 1.161}, {'end': 152.403, 'text': 'feature selection with it should not be election, it should be selection with decision trees, okay?', 'start': 147.741, 'duration': 4.662}], 'summary': 'Focus on feature selection methods like correlation, forward and backward elimination, univariate selections, and random forest importance.', 'duration': 25.954, 'max_score': 126.449, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy0126449.jpg'}, {'end': 215.44, 'src': 'embed', 'start': 182.976, 'weight': 1, 'content': [{'end': 185.118, 'text': 'all these three steps, then.', 'start': 182.976, 'duration': 2.142}, {'end': 189.821, 'text': 'obviously, the next step is basically with respect to machine learning algorithms and, as you know,', 'start': 185.118, 'duration': 4.703}, {'end': 192.103, 'text': 'that whenever we discuss about machine learning algorithms,', 'start': 189.821, 'duration': 2.282}, {'end': 196.767, 'text': 'we are basically going to discuss about supervised and unsupervised machine learning algorithms.', 'start': 192.103, 'duration': 4.664}, {'end': 202.212, 'text': 'in supervised, you basically have regression and classification problem statements.', 'start': 196.767, 'duration': 5.445}, {'end': 205.735, 'text': "then I'd suggest that you go with clustering algorithms.", 'start': 202.212, 'duration': 3.523}, {'end': 208.337, 'text': 'clustering is basically the example of unsupervised machine learning.', 'start': 205.735, 'duration': 2.602}, {'end': 215.44, 'text': "There is also a concept of reinforcement learning, but if you are really a starter right now, don't focus on reinforcement,", 'start': 208.877, 'duration': 6.563}], 'summary': 'Focus on supervised (regression, classification) and unsupervised (clustering) machine learning algorithms, avoid reinforcement learning for beginners.', 'duration': 32.464, 'max_score': 182.976, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy0182976.jpg'}, {'end': 370.253, 'src': 'embed', 'start': 334.648, 'weight': 2, 'content': [{'end': 341.775, 'text': 'Now in hyperparameter tuning you have various techniques like grid search, CV, randomized search, CV, hyperopt, optuna, genetics algorithm.', 'start': 334.648, 'duration': 7.127}, {'end': 345.458, 'text': 'In deep learning also you have something called as Keras tuner and many more things.', 'start': 342.155, 'duration': 3.303}, {'end': 351.944, 'text': 'Now, once you go through all these things, this whole steps from machine learning algorithms to grid search,', 'start': 346.039, 'duration': 5.905}, {'end': 356.088, 'text': 'it will be probably taking 10% of your overall machine learning project time.', 'start': 351.944, 'duration': 4.144}, {'end': 359.469, 'text': 'okay, only 10%, guys, nothing more than that.', 'start': 356.528, 'duration': 2.941}, {'end': 365.351, 'text': 'but again to create the pipelines and all that will be taking some more 5% of the overall time.', 'start': 359.469, 'duration': 5.882}, {'end': 370.253, 'text': "so overall I'll consider 10 to 15 percent of the overall time of the machine learning project.", 'start': 365.351, 'duration': 4.902}], 'summary': 'Hyperparameter tuning takes 10-15% of ml project time.', 'duration': 35.605, 'max_score': 334.648, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy0334648.jpg'}, {'end': 425.618, 'src': 'embed', 'start': 392.121, 'weight': 4, 'content': [{'end': 394.602, 'text': 'At the end of the day, when you complete all these things.', 'start': 392.121, 'duration': 2.481}, {'end': 397.163, 'text': 'and guys, for all these things I have created playlist.', 'start': 394.602, 'duration': 2.561}, {'end': 399.704, 'text': 'okay?. For all these things model deployment I have a separate playlist.', 'start': 397.163, 'duration': 2.541}, {'end': 401.444, 'text': 'Docker Kubernetes, I have a separate playlist.', 'start': 399.744, 'duration': 1.7}, {'end': 405.766, 'text': 'You know, hyperparameter tuning, feature engineering, feature selection, I have a separate playlist.', 'start': 401.944, 'duration': 3.822}, {'end': 410.828, 'text': "All the machine learning algorithms, I have actually created as a separate playlist, you know? So everything I've done.", 'start': 406.146, 'duration': 4.682}, {'end': 416.572, 'text': "Once you're very good at this, try to just implement end-to-end machine learning projects as much as you can.", 'start': 411.588, 'duration': 4.984}, {'end': 425.618, 'text': "And again, for that, I've taken one month continuously live streams in my YouTube channel where I've implemented end-to-end machine learning projects.", 'start': 417.833, 'duration': 7.785}], 'summary': 'Created separate playlists for various topics and implemented end-to-end machine learning projects in one month of continuous live streams on youtube.', 'duration': 33.497, 'max_score': 392.121, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy0392121.jpg'}], 'start': 88.876, 'title': 'Data analysis, feature engineering, and machine learning project journey', 'summary': 'Covers the importance of exploratory data analysis, feature engineering, and feature selection in data preprocessing, including steps like handling missing values, outliers, category encoding, normalization, standardization, and various techniques for feature selection. it also discusses essential steps of a machine learning project, including the allocation of project time, the importance of supervised and unsupervised learning algorithms, hyperparameter tuning, and model deployment using docker and kubernetes, with an emphasis on following structured learning playlists and continuous practice.', 'chapters': [{'end': 162.448, 'start': 88.876, 'title': 'Data analysis and feature engineering', 'summary': 'Covers the importance of exploratory data analysis, feature engineering, and feature selection in data preprocessing, including steps like handling missing values, outliers, category encoding, normalization, standardization, and various techniques for feature selection.', 'duration': 73.572, 'highlights': ['The importance of exploratory data analysis, feature engineering, and feature selection in data preprocessing, including steps like handling missing values, outliers, category encoding, normalization, standardization, and various techniques for feature selection.', 'In the feature engineering section, steps like exploratory data analysis, handling missing values, handling outliers, category encoding, normalization, and standardization are mentioned as crucial.', 'Methods for feature selection include correlation, forward elimination, backward elimination, univariate selections, random forest importance, and feature selection with decision trees.']}, {'end': 444.951, 'start': 162.448, 'title': 'Machine learning project journey', 'summary': 'Covers the essential steps of a machine learning project, including the allocation of project time, the importance of supervised and unsupervised learning algorithms, hyperparameter tuning, and model deployment using docker and kubernetes, with an emphasis on following structured learning playlists and continuous practice.', 'duration': 282.503, 'highlights': ['The importance of supervised and unsupervised learning algorithms in machine learning projects, with a focus on regression, classification, and clustering algorithms. Understanding the significance of various machine learning algorithms, including regression, classification, and clustering, in the context of project implementation.', 'The significance of hyperparameter tuning in machine learning algorithms, including techniques like grid search, CV, randomized search, CV, hyperopt, optuna, genetics algorithm, and Keras tuner. Highlighting the importance of hyperparameter tuning techniques such as grid search, CV, randomized search, CV, hyperopt, optuna, genetics algorithm, and Keras tuner in improving model performance.', 'The allocation of project time, with machine learning algorithms and hyperparameter tuning accounting for 10% and model deployment taking 5% of the overall project time. Explaining the distribution of project time, with machine learning algorithms and hyperparameter tuning consuming 10% and model deployment requiring 5% of the total project duration.', 'The recommendation to focus on model deployment using Docker and Kubernetes, along with the suggestion to follow structured learning playlists for comprehensive understanding and continuous practice. Emphasizing the importance of model deployment using Docker and Kubernetes, as well as the value of following structured learning playlists for thorough comprehension and consistent skill development.']}], 'duration': 356.075, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy088876.jpg', 'highlights': ['Various techniques for feature selection include correlation, forward elimination, backward elimination, univariate selections, random forest importance, and feature selection with decision trees.', 'Understanding the significance of various machine learning algorithms, including regression, classification, and clustering, in the context of project implementation.', 'Highlighting the importance of hyperparameter tuning techniques such as grid search, CV, randomized search, CV, hyperopt, optuna, genetics algorithm, and Keras tuner in improving model performance.', 'Explaining the distribution of project time, with machine learning algorithms and hyperparameter tuning consuming 10% and model deployment requiring 5% of the total project duration.', 'Emphasizing the importance of model deployment using Docker and Kubernetes, as well as the value of following structured learning playlists for thorough comprehension and consistent skill development.']}, {'end': 1134.591, 'segs': [{'end': 505.742, 'src': 'embed', 'start': 467.388, 'weight': 0, 'content': [{'end': 474.253, 'text': 'Sklearn is one of the most important library because all the pre-processing techniques, all the machine learning algorithms,', 'start': 467.388, 'duration': 6.865}, {'end': 479.437, 'text': 'some of the hyperparameter tuning like grid search, randomized search, will be present in Sklearn right?', 'start': 474.253, 'duration': 5.184}, {'end': 482.359, 'text': 'So this Sklearn library is pretty much important.', 'start': 479.857, 'duration': 2.502}, {'end': 487.465, 'text': "I hope everybody's familiar with that, right? Now, this is the path that you should follow.", 'start': 482.399, 'duration': 5.066}, {'end': 494.555, 'text': "Now, this is completely based on my experience, guys, and based on this only, I've uploaded all the videos in my YouTube channel.", 'start': 488.066, 'duration': 6.489}, {'end': 500.459, 'text': 'See, if you are trying to enter into data science industry, first of all, you need to know the programming knowledge.', 'start': 495.737, 'duration': 4.722}, {'end': 503.741, 'text': 'Then you have to do a lot of EDA with respect to the data that you have.', 'start': 500.66, 'duration': 3.081}, {'end': 505.742, 'text': 'Then you have to go ahead with feature engineering.', 'start': 504.121, 'duration': 1.621}], 'summary': 'Sklearn is crucial for pre-processing, ml algorithms, and hyperparameter tuning. programming knowledge, eda, and feature engineering are essential for entering data science.', 'duration': 38.354, 'max_score': 467.388, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy0467388.jpg'}, {'end': 605.164, 'src': 'heatmap', 'start': 546.515, 'weight': 1, 'content': [{'end': 553.222, 'text': 'Two things that I missed over here is about some of the web frameworks also you should try to learn like Flask, Django, Streamlit.', 'start': 546.515, 'duration': 6.707}, {'end': 556.425, 'text': 'So those kind of videos has been also uploaded in my video.', 'start': 553.502, 'duration': 2.923}, {'end': 561.549, 'text': 'Now one important thing that I missed from this whole diagram is basically about the NLP.', 'start': 556.985, 'duration': 4.564}, {'end': 568.713, 'text': "So guys, NLP, when I talk about machine learning algorithms here, you'll also suppose if your input data is in the form of text.", 'start': 562.23, 'duration': 6.483}, {'end': 573.634, 'text': 'So at that time, you will also be considering doing it with the help of natural language processing.', 'start': 569.313, 'duration': 4.321}, {'end': 578.996, 'text': 'Whenever I talk about text preprocessing, whenever I talk about name bias algorithm,', 'start': 574.095, 'duration': 4.901}, {'end': 583.298, 'text': 'whenever I talk about some of the algorithms that are suitable for this NLP techniques, right?', 'start': 578.996, 'duration': 4.302}, {'end': 585.379, 'text': "For that I've created a separate pipeline.", 'start': 583.618, 'duration': 1.761}, {'end': 588.5, 'text': 'In this pipeline, you have text pre-processing techniques.', 'start': 585.979, 'duration': 2.521}, {'end': 592.601, 'text': 'In this, you will be doing tokenization, lemmatization, stop words, POS.', 'start': 588.58, 'duration': 4.021}, {'end': 597.902, 'text': 'In that level 2, you will be doing bag of words, TF-IDF, unigrams, bigrams, engrams.', 'start': 593.321, 'duration': 4.581}, {'end': 599.582, 'text': 'You have to learn in this specific pattern.', 'start': 597.922, 'duration': 1.66}, {'end': 605.164, 'text': 'And then you also have some advanced text pre-processing like gen sim, word2vec, average word2vec.', 'start': 600.103, 'duration': 5.061}], 'summary': 'The transcript covers web frameworks like flask, django, streamlit, and nlp techniques including text preprocessing and algorithms.', 'duration': 32.481, 'max_score': 546.515, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy0546515.jpg'}, {'end': 632.459, 'src': 'embed', 'start': 597.922, 'weight': 6, 'content': [{'end': 599.582, 'text': 'You have to learn in this specific pattern.', 'start': 597.922, 'duration': 1.66}, {'end': 605.164, 'text': 'And then you also have some advanced text pre-processing like gen sim, word2vec, average word2vec.', 'start': 600.103, 'duration': 5.061}, {'end': 609.925, 'text': 'And then you really have to solve many machine learning use cases and finally do the model deployment.', 'start': 605.624, 'duration': 4.301}, {'end': 616.089, 'text': 'The two libraries whenever you are preparing machine learning with NLP that you should focus on is NLTK and spaCy.', 'start': 610.445, 'duration': 5.644}, {'end': 619.731, 'text': 'If you are good at these two libraries, probably with respect to machine learning,', 'start': 616.729, 'duration': 3.002}, {'end': 623.834, 'text': 'any question that comes up with NLP you will be able to answer with all these things.', 'start': 619.731, 'duration': 4.103}, {'end': 632.459, 'text': 'Again, model deployment is the same deployment techniques that we will be using over here with the help of Flask or Streamlit or Django.', 'start': 624.354, 'duration': 8.105}], 'summary': 'Learn specific pattern, use nltk and spacy, solve ml use cases, deploy model', 'duration': 34.537, 'max_score': 597.922, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy0597922.jpg'}, {'end': 846.6, 'src': 'heatmap', 'start': 817.63, 'weight': 0.844, 'content': [{'end': 819.151, 'text': 'Again, there are so many videos on this.', 'start': 817.63, 'duration': 1.521}, {'end': 824.153, 'text': 'The next thing that you also have to focus on is basically my feature engineering playlist.', 'start': 820.051, 'duration': 4.102}, {'end': 828.994, 'text': 'So in this feature engineering playlist, I have uploaded all the different techniques of feature engineering.', 'start': 824.533, 'duration': 4.461}, {'end': 834.896, 'text': 'This will be pretty much handy for you, because this is the second step that we really want to focus on, right?', 'start': 829.454, 'duration': 5.442}, {'end': 843.499, 'text': 'And if you remember the diagram that I showed you just a while back over there, all the techniques of feature engineering has been included,', 'start': 835.516, 'duration': 7.983}, {'end': 846.6, 'text': 'should be is included in this specific playlist itself, okay?', 'start': 843.499, 'duration': 3.101}], 'summary': 'Feature engineering playlist covers various techniques, essential for focusing on the second step.', 'duration': 28.97, 'max_score': 817.63, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy0817630.jpg'}, {'end': 886.46, 'src': 'heatmap', 'start': 860.446, 'weight': 0.749, 'content': [{'end': 865.928, 'text': "I've just uploaded two videos, some random videos I've uploaded with respect to feature selection with some of the techniques.", 'start': 860.446, 'duration': 5.482}, {'end': 867.029, 'text': 'You can also go through this.', 'start': 865.948, 'duration': 1.081}, {'end': 876.474, 'text': "Apart from that, guys, you'll also be seeing that I have created some SQL database playlist with Python and MongoDB playlist with Python also.", 'start': 867.689, 'duration': 8.785}, {'end': 879.596, 'text': 'So this link will also be given in the description of this particular video.', 'start': 876.534, 'duration': 3.062}, {'end': 883.458, 'text': 'Some important thing is with respect to Dockers end-to-end.', 'start': 880.197, 'duration': 3.261}, {'end': 886.46, 'text': 'Here we have created some machine learning application.', 'start': 883.879, 'duration': 2.581}], 'summary': 'Uploaded 2 feature selection videos, sql and mongodb playlist with python, and docker end-to-end machine learning application.', 'duration': 26.014, 'max_score': 860.446, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy0860446.jpg'}, {'end': 1067.319, 'src': 'embed', 'start': 1038.814, 'weight': 4, 'content': [{'end': 1041.994, 'text': "you know I've asked a lot of questions with respect to that over there itself.", 'start': 1038.814, 'duration': 3.18}, {'end': 1045.438, 'text': 'Okay, so please make sure that you follow these live virtual interviews.', 'start': 1042.474, 'duration': 2.964}, {'end': 1048.442, 'text': 'that will give you a confidence, like how you should answer the questions and all.', 'start': 1045.438, 'duration': 3.004}, {'end': 1054.869, 'text': 'Finally, for the members, I have also created a lot of data science projects, machine learning projects.', 'start': 1049.123, 'duration': 5.746}, {'end': 1058.213, 'text': 'So here you can see there are more than 72 videos in this project.', 'start': 1055.269, 'duration': 2.944}, {'end': 1062.776, 'text': "And yes, I'll be uploading more membership for membership projects.", 'start': 1058.733, 'duration': 4.043}, {'end': 1067.319, 'text': "Probably today I'm also planning to upload one separate project for membership.", 'start': 1063.236, 'duration': 4.083}], 'summary': 'The speaker emphasizes the value of following live virtual interviews to gain confidence and offers over 72 data science and machine learning projects for members.', 'duration': 28.505, 'max_score': 1038.814, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy01038814.jpg'}, {'end': 1134.591, 'src': 'embed', 'start': 1109.314, 'weight': 5, 'content': [{'end': 1111.216, 'text': 'It will definitely be very, very useful for you.', 'start': 1109.314, 'duration': 1.902}, {'end': 1113.218, 'text': 'It will definitely be very, very helpful for you.', 'start': 1111.416, 'duration': 1.802}, {'end': 1116.861, 'text': 'The same thing has been followed by many of my subscribers.', 'start': 1114.279, 'duration': 2.582}, {'end': 1121.085, 'text': 'They are able to get many jobs as such in the data science industry itself.', 'start': 1116.881, 'duration': 4.204}, {'end': 1124.908, 'text': 'This thing will also happen with you if you are pretty much studious,', 'start': 1121.685, 'duration': 3.223}, {'end': 1127.831, 'text': "if you are pretty much following all the things that I'm actually mentioning you.", 'start': 1124.908, 'duration': 2.923}, {'end': 1129.897, 'text': 'so i hope you like this particular video.', 'start': 1128.292, 'duration': 1.605}, {'end': 1130.98, 'text': 'please do subscribe the channel.', 'start': 1129.897, 'duration': 1.083}, {'end': 1132.806, 'text': "if you have not already subscribed, i'll see you in the next video.", 'start': 1130.98, 'duration': 1.826}, {'end': 1133.809, 'text': 'have a great day.', 'start': 1132.806, 'duration': 1.003}, {'end': 1134.591, 'text': 'thank you and all bye.', 'start': 1133.809, 'duration': 0.782}], 'summary': 'Many subscribers have found jobs in data science industry. follow advice and be studious for success.', 'duration': 25.277, 'max_score': 1109.314, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy01109314.jpg'}], 'start': 444.971, 'title': 'Libraries and learning blueprint in data science', 'summary': 'Emphasizes the significance of sklearn, nltk, and spacy libraries in data science, and outlines essential steps and techniques for entering the industry. it also presents a 45-day blueprint for learning data science, covering machine learning algorithms, feature engineering, sql databases, docker, kaggle, nlp, data science interview questions, live virtual interviews, and data science projects, offering a high probability of securing jobs.', 'chapters': [{'end': 651.051, 'start': 444.971, 'title': 'Importance of libraries in data science', 'summary': 'Emphasizes the importance of libraries such as sklearn, nltk, and spacy in data science, along with the essential steps and techniques including pre-processing, feature engineering, and model deployment, necessary for entering the data science industry.', 'duration': 206.08, 'highlights': ['The significance of Sklearn as a library for pre-processing techniques, machine learning algorithms, and hyperparameter tuning is emphasized, which is crucial for entering the data science industry.', 'The importance of NLP techniques in machine learning algorithms is highlighted, with a focus on text pre-processing, tokenization, lemmatization, and advanced techniques like gen sim and word2vec, necessary for solving machine learning use cases.', 'The essential steps for entering the data science industry, including programming knowledge, EDA, feature engineering, feature selection, and practical implementation of machine learning algorithms, are outlined as crucial for aspiring data scientists.', 'The significance of learning web frameworks such as Flask, Django, and Streamlit, along with the emphasis on solving end-to-end projects, is stressed as essential for individuals aiming to enter the data science industry.']}, {'end': 1134.591, 'start': 652.192, 'title': 'Data science learning blueprint', 'summary': 'Illustrates a detailed blueprint for learning data science, including machine learning algorithms, playlists for feature engineering, sql databases, docker, kaggle, nlp, data science interview questions, live virtual interviews, and data science projects, which, if followed diligently, can be completed in 45 days, leading to a high probability of securing jobs in the data science industry.', 'duration': 482.399, 'highlights': ['The chapter provides a comprehensive blueprint for learning data science, covering machine learning algorithms like linear regression, logistic regression, decision trees, k-nearest neighbor, random forest, support vector machines, unsupervised machine learning techniques, NLP techniques using SK Learn and NLTK, totaling to over 72 videos in the project playlist, which, if followed diligently, can be completed in 45 days, leading to a high probability of securing jobs in the data science industry.', 'The chapter emphasizes the importance of following the provided playlists for feature engineering, SQL databases, Docker, Kaggle, NLP, data science interview questions, live virtual interviews, and data science projects, as these playlists, if followed diligently, can be completed in 45 days, leading to a high probability of securing jobs in the data science industry.', 'The chapter highlights the availability of more than 50 videos for data science interview questions, over 72 videos for data science projects, and live virtual interviews covering machine learning and deep learning questions, which, if followed diligently, can be completed in 45 days, leading to a high probability of securing jobs in the data science industry.', 'The chapter emphasizes the completion of the comprehensive learning blueprint within 45 days, which, if followed diligently along with practice, can lead to a high probability of securing jobs in the data science industry, as attested by many subscribers.', 'The chapter stresses the significance of the roadmap for learning natural language processing and machine learning techniques, such as Kaggle competition using NLP techniques with SK Learn and NLTK, and the availability of subscription-based projects, which, if followed diligently, can be completed in 45 days, leading to a high probability of securing jobs in the data science industry.']}], 'duration': 689.62, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOpETRQGXy0/pics/VOpETRQGXy0444971.jpg', 'highlights': ['The significance of Sklearn as a library for pre-processing techniques, machine learning algorithms, and hyperparameter tuning is crucial for entering the data science industry.', 'The importance of NLP techniques in machine learning algorithms is necessary for solving machine learning use cases.', 'The essential steps for entering the data science industry, including programming knowledge, EDA, feature engineering, and practical implementation of machine learning algorithms, are crucial for aspiring data scientists.', 'The significance of learning web frameworks such as Flask, Django, and Streamlit, along with solving end-to-end projects, is essential for individuals aiming to enter the data science industry.', 'The chapter provides a comprehensive blueprint for learning data science, covering machine learning algorithms, NLP techniques, SQL databases, Docker, Kaggle, data science interview questions, live virtual interviews, and data science projects, offering a high probability of securing jobs.', 'The completion of the comprehensive learning blueprint within 45 days, if followed diligently along with practice, can lead to a high probability of securing jobs in the data science industry, as attested by many subscribers.', 'The roadmap for learning natural language processing and machine learning techniques, such as Kaggle competition using NLP techniques with SK Learn and NLTK, can be completed in 45 days, leading to a high probability of securing jobs in the data science industry.']}], 'highlights': ['The completion of the comprehensive learning blueprint within 45 days, if followed diligently along with practice, can lead to a high probability of securing jobs in the data science industry, as attested by many subscribers.', 'The roadmap for learning natural language processing and machine learning techniques, such as Kaggle competition using NLP techniques with SK Learn and NLTK, can be completed in 45 days, leading to a high probability of securing jobs in the data science industry.', 'Understanding the significance of various machine learning algorithms, including regression, classification, and clustering, in the context of project implementation.', 'The importance of hyperparameter tuning techniques such as grid search, CV, randomized search, CV, hyperopt, optuna, genetics algorithm, and Keras tuner in improving model performance.', 'The significance of Sklearn as a library for pre-processing techniques, machine learning algorithms, and hyperparameter tuning is crucial for entering the data science industry.']}