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Beginner to Advanced - how to become a data scientist
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[TutsNode.net] - Beginner to Advanced - how to become a data scientist (Size: 1.59 GB) (Files: 202)
[TutsNode.net] - Beginner to Advanced - how to become a data scientist
05 - Master Data Visualization with Seaborn
018 Bonus - How to use Transfer learning to predict ice cream.mp4
018 Bonus - How to use Transfer learning to predict ice cream_en.srt
011 52 how to master seaborn 10_en.srt
007 48 how to master seaborn 6_en.srt
004 45 how to master seaborn 3_en.srt
002 43 how to master seaborn 1_en.srt
009 50 how to master seaborn 8_en.srt
006 47 how to master seaborn 5_en.srt
010 51 how to master seaborn 9_en.srt
003 44 how to master seaborn 2_en.srt
005 46 how to master seaborn 4_en.srt
015 56 how to master seaborn 14_en.srt
014 55 how to master seaborn 13_en.srt
008 49 how to master seaborn 7_en.srt
013 54 how to master seaborn 12_en.srt
012 53 how to master seaborn 11_en.srt
001 42 Seaborn introduction_en.srt
016 57 The end of the road - What to do now_en.srt
019 If you like my teaching style and want to continue learning together.html
017 More learning resources for your AI learning journey.html
011 52 how to master seaborn 10.mp4
007 48 how to master seaborn 6.mp4
009 50 how to master seaborn 8.mp4
002 43 how to master seaborn 1.mp4
003 44 how to master seaborn 2.mp4
010 51 how to master seaborn 9.mp4
004 45 how to master seaborn 3.mp4
006 47 how to master seaborn 5.mp4
008 49 how to master seaborn 7.mp4
015 56 how to master seaborn 14.mp4
005 46 how to master seaborn 4.mp4
014 55 how to master seaborn 13.mp4
012 53 how to master seaborn 11.mp4
016 57 The end of the road - What to do now.mp4
013 54 how to master seaborn 12.mp4
001 42 Seaborn introduction.mp4
01 - Course introduction
005 matches.csv
001 Introduction - Why are you here and what we will accomplish here_en.srt
002 One important thing before you start.html
005 train.csv
005 sales2.csv
005 Download all the source files.html
005 baseball.csv
005 test.csv
005 Pokemon-Color-Palette.txt
005 titanicexcel.csv
005 pima-indians-diabetes.csv
005 Pokemon.csv
004 Check you system_en.srt
003 What are the prerequesits for data science and this course_en.srt
005 transit-segments.csv
004 Check you system.mp4
001 Introduction - Why are you here and what we will accomplish here.mp4
003 What are the prerequesits for data science and this course.mp4
03 - Introduction to numpy - what you need to know
005 38 Introduction to numpy 4_en.srt
003 36 Introduction to numpy 2_en.srt
004 37 Introduction to numpy 3_en.srt
001 34 What are Tensors_en.srt
002 35 Introduction to numpy 1_en.srt
005 38 Introduction to numpy 4.mp4
003 36 Introduction to numpy 2.mp4
004 37 Introduction to numpy 3.mp4
001 34 What are Tensors.mp4
002 35 Introduction to numpy 1.mp4
02 - pandas for data science
030 29 In depth combining dataframes.mp4
030 29 In depth combining dataframes_en.srt
015 14 A machine learning insight - a full case study_en.srt
029 28 Data science and Finance_en.srt
023 22 How to work with dates_en.srt
001 0 All you need to know about Series_en.srt
020 19 Slicing techniques 2_en.srt
028 27 The Multiindex_en.srt
027 26 Grouping in pandas beginner to advanced_en.srt
031 30 Useful ways to deal with strings (regex example)_en.srt
034 33 Bonus Tips and Tricks 3_en.srt
029 28 Data science and Finance.mp4
016 15 Master dates_en.srt
021 20 More data science techniques in pandas_en.srt
019 18 Slicing techniques_en.srt
008 7 The issue with missing values - a common problem in machine learning_en.srt
005 4 pandas for data scientists_en.srt
009 8 Dealing with missing values 2_en.srt
032 31 Bonus Tips and Tricks_en.srt
002 1 pandas for data scientists_en.srt
018 17 How to play with the Index_en.srt
033 32 Bonus Tips and Tricks 2_en.srt
006 5 Broadcasting operations_en.srt
004 3 pandas for data scientists_en.srt
022 21 Data querying in pandas_en.srt
012 11 How to slice your data 1_en.srt
017 16 How to deal with dublicates_en.srt
011 10 Sorting your data properly_en.srt
010 9 The right data in the right format_en.srt
003 2 pandas for data scientists_en.srt
007 6 Counting_en.srt
014 13 How to check for missing values_en.srt
013 12 How to slice your data 2_en.srt
025 24 How to work with dates 3_en.srt
024 23 How to work with dates 2_en.srt
026 25 How to work with dates 4_en.srt
020 19 Slicing techniques 2.mp4
028 27 The Multiindex.mp4
023 22 How to work with dates.mp4
015 14 A machine learning insight - a full case study.mp4
027 26 Grouping in pandas beginner to advanced.mp4
001 0 All you need to know about Series.mp4
031 30 Useful ways to deal with strings (regex example).mp4
016 15 Master dates.mp4
021 20 More data science techniques in pandas.mp4
034 33 Bonus Tips and Tricks 3.mp4
019 18 Slicing techniques.mp4
008 7 The issue with missing values - a common problem in machine learning.mp4
009 8 Dealing with missing values 2.mp4
017 16 How to deal with dublicates.mp4
005 4 pandas for data scientists.mp4
032 31 Bonus Tips and Tricks.mp4
018 17 How to play with the Index.mp4
010 9 The right data in the right format.mp4
002 1 pandas for data scientists.mp4
003 2 pandas for data scientists.mp4
033 32 Bonus Tips and Tricks 2.mp4
006 5 Broadcasting operations.mp4
007 6 Counting.mp4
022 21 Data querying in pandas.mp4
011 10 Sorting your data properly.mp4
004 3 pandas for data scientists.mp4
013 12 How to slice your data 2.mp4
012 11 How to slice your data 1.mp4
014 13 How to check for missing values.mp4
025 24 How to work with dates 3.mp4
024 23 How to work with dates 2.mp4
026 25 How to work with dates 4.mp4
04 - Data Visualization
003 41 Matplotlib - advanced_en.srt
002 40 Matplotlib - advanced_en.srt
001 39 Matplotlib - a how to guide_en.srt
003 41 Matplotlib - advanced.mp4
002 40 Matplotlib - advanced.mp4
001 39 Matplotlib - a how to guide.mp4
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