NLP-Natural Language Processing in Python(Theory & Projects)

Focused View

AI Sciences,AI Sciences Team

23:31:16

337 View

1 - Introduction

1 - Introduction to Course.mp4

00:55

2 - Introduction to Instructor.mp4

02:19

3 - Introduction to CoInstructor.mp4

01:30

4 - Course Introduction.mp4

11:16

5 - Request for Your Honest Review.mp4

01:18

6 - Links for the Courses Materials and Codes.html

2 - IntroductionRegular Expressions

7 - Links for the Courses Materials and Codes.html

8 - What Is Regular Expression.mp4

05:56

9 - Why Regular Expression.mp4

06:31

10 - ELIZA Chatbot.mp4

04:23

11 - Python Regular Expression Package.mp4

04:24

3 - Meta CharactersRegular Expressions

12 - Links for the Courses Materials and Codes.html

13 - Meta Characters.mp4

02:27

14 - Meta Characters Bigbrackets Exercise.mp4

04:19

15 - Meta Characters Bigbrackets Exercise Solution.mp4

03:59

16 - Meta Characters Bigbrackets Exercise 2.mp4

02:11

17 - Meta Characters Bigbrackets Exercise 2 Solution.mp4

04:37

18 - Meta Characters Cap.mp4

03:18

19 - Meta Characters Cap Exercise 3.mp4

02:03

20 - Meta Characters Cap Exercise 3 Solution.mp4

04:44

21 - Backslash.mp4

05:02

22 - Backslash Continued.mp4

07:59

23 - Backslash Continued 01.mp4

03:51

24 - Backslash Squared Brackets Exercise.mp4

01:38

25 - Backslash Squared Brackets Exercise Solution.mp4

03:59

26 - Backslash Squared Brackets Exercise Another Solution.mp4

04:43

27 - Backslash Exercise.mp4

01:43

28 - Backslash Exercise Solution And Special Sequences Exercise.mp4

05:12

29 - Solution And Special Sequences Exercise Solution.mp4

04:57

30 - Meta Character Asterisk.mp4

05:18

31 - Meta Character Asterisk Exercise.mp4

04:54

32 - Meta Character Asterisk Exercise Solution.mp4

05:04

33 - Meta Character Asterisk Homework.mp4

04:28

34 - Meta Character Asterisk Greedymatching.mp4

05:54

35 - Meta Character Plus And Questionmark.mp4

05:47

36 - Meta Character Curly Brackets Exercise.mp4

03:25

37 - Meta Character Curly Brackets Exercise Solution.mp4

05:18

4 - Pattern ObjectsRegular Expressions

38 - Links for the Courses Materials and Codes.html

39 - Pattern Objects.mp4

05:05

40 - Pattern Objects Match Method Exersize.mp4

02:43

41 - Pattern Objects Match Method Exersize Solution.mp4

07:11

42 - Pattern Objects Match Method Vs Search Method.mp4

05:18

43 - Pattern Objects Finditer Method.mp4

03:20

44 - Pattern Objects Finditer Method Exersize Solution.mp4

06:22

5 - More Meta CharactersRegular Expressions

45 - Links for the Courses Materials and Codes.html

46 - Meta Characters Logical Or.mp4

06:29

47 - Meta Characters Beginning And End Patterns.mp4

04:57

48 - Meta Characters Paranthesis.mp4

07:03

6 - String ModificationRegular Expressions

49 - Links for the Courses Materials and Codes.html

50 - String Modification.mp4

03:49

51 - Word Tokenizer Using Split Method.mp4

04:34

52 - Sub Method Exercise.mp4

04:44

53 - Sub Method Exercise Solution.mp4

04:30

7 - Words and TokensText Preprocessing

54 - Links for the Courses Materials and Codes.html

55 - What Is A Word.mp4

04:41

56 - Definition Of Word Is Task Dependent.mp4

05:20

57 - Vocabulary And Corpus.mp4

05:38

58 - Tokens.mp4

03:14

59 - Tokenization In Spacy.mp4

09:10

8 - Sentiment ClassificationText Preprocessing

60 - Links for the Courses Materials and Codes.html

61 - Yelp Reviews Classification Mini Project Introduction.mp4

05:35

62 - Yelp Reviews Classification Mini Project Vocabulary Initialization.mp4

06:48

63 - Yelp Reviews Classification Mini Project Adding Tokens To Vocabulary.mp4

04:54

64 - Yelp Reviews Classification Mini Project Look Up Functions In Vocabulary.mp4

05:57

65 - Yelp Reviews Classification Mini Project Building Vocabulary From Data.mp4

09:17

66 - Yelp Reviews Classification Mini Project One Hot Encoding.mp4

06:51

67 - Yelp Reviews Classification Mini Project One Hot Encoding Implementation.mp4

09:47

68 - Yelp Reviews Classification Mini Project Encoding Documents.mp4

06:50

69 - Yelp Reviews Classification Mini Project Encoding Documents Implementation.mp4

06:28

70 - Yelp Reviews Classification Mini Project Train Test Splits.mp4

04:20

71 - Yelp Reviews Classification Mini Project Featurecomputation.mp4

06:08

72 - Yelp Reviews Classification Mini Project Classification.mp4

10:45

9 - Language Independent TokenizationText Preprocessing

73 - Links for the Courses Materials and Codes.html

74 - Tokenization In Detial Introduction.mp4

04:28

75 - Tokenization Is Hard.mp4

05:16

76 - Tokenization Byte Pair Encoding.mp4

06:08

77 - Tokenization Byte Pair Encoding Example.mp4

08:33

78 - Tokenization Byte Pair Encoding On Test Data.mp4

07:14

79 - Tokenization Byte Pair Encoding Implementation Getpaircounts.mp4

08:11

80 - Tokenization Byte Pair Encoding Implementation Mergeincorpus.mp4

07:49

81 - Tokenization Byte Pair Encoding Implementation BFE Training.mp4

06:10

82 - Tokenization Byte Pair Encoding Implementation BFE Encoding.mp4

06:25

83 - Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair.mp4

09:11

84 - Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair 1.mp4

07:53

10 - Text NomalizationText Preprocessing

85 - Links for the Courses Materials and Codes.html

86 - Word Normalization Case Folding.mp4

04:43

87 - Word Normalization Lematization.mp4

06:48

88 - Word Normalization Stemming.mp4

02:31

89 - Word Normalization Sentence Segmentation.mp4

06:27

11 - String Matching and Spelling CorrectionText Preprocessing

90 - Links for the Courses Materials and Codes.html

91 - Spelling Correction Minimum Edit Distance Intro.mp4

07:59

92 - Spelling Correction Minimum Edit Distance Example.mp4

09:04

93 - Spelling Correction Minimum Edit Distance Table Filling.mp4

09:10

94 - Spelling Correction Minimum Edit Distance Dynamic Programming.mp4

07:17

95 - Spelling Correction Minimum Edit Distance Psudocode.mp4

03:55

96 - Spelling Correction Minimum Edit Distance Implementation.mp4

06:57

97 - Spelling Correction Minimum Edit Distance Implementation Bugfixing.mp4

02:40

98 - Spelling Correction Implementation.mp4

07:42

12 - Language Modeling

99 - Links for the Courses Materials and Codes.html

100 - What Is A Language Model.mp4

05:50

101 - Language Model Formal Definition.mp4

06:31

102 - Language Model Curse Of Dimensionality.mp4

04:27

103 - Language Model Markov Assumption And NGrams.mp4

07:06

104 - Language Model Implementation Setup.mp4

04:24

105 - Language Model Implementation Ngrams Function.mp4

08:42

106 - Language Model Implementation Update Counts Function.mp4

05:47

107 - Language Model Implementation Probability Model Funciton.mp4

06:35

108 - Language Model Implementation Reading Corpus.mp4

12:17

109 - Language Model Implementation Sampling Text.mp4

18:17

13 - Topic Modelling with Word and Document Representations

110 - Links for the Courses Materials and Codes.html

111 - One Hot Vectors.mp4

04:10

112 - One Hot Vectors Implementaton.mp4

05:42

113 - One Hot Vectors Limitations.mp4

04:45

114 - One Hot Vectors Uses As Target Labeling.mp4

03:42

115 - Term Frequency For Document Representations.mp4

03:12

116 - Term Frequency For Document Representations Implementations.mp4

05:31

117 - Term Frequency For Word Representations.mp4

05:17

118 - TFIDF For Document Representations.mp4

05:01

119 - TFIDF For Document Representations Implementation Reading Corpus.mp4

04:24

120 - TFIDF For Document Representations Implementation Computing Document Frequenc.mp4

05:00

121 - TFIDF For Document Representations Implementation Computing TFIDF.mp4

07:22

122 - Topic Modeling With TFIDF 1.mp4

04:20

123 - Topic Modeling With TFIDF 3.mp4

04:27

124 - Topic Modeling With TFIDF 4.mp4

04:52

125 - Topic Modeling With TFIDF 5.mp4

04:40

126 - Topic Modeling With Gensim.mp4

13:26

14 - Word Embeddings LSI

127 - Links for the Courses Materials and Codes.html

128 - Word Cooccurrence Matrix.mp4

06:25

129 - Word Cooccurrence Matrix vs Documentterm Matrix.mp4

05:47

130 - Word Cooccurrence Matrix Implementation Preparing Data.mp4

05:08

131 - Word Cooccurrence Matrix Implementation Preparing Data 2.mp4

04:13

132 - Word Cooccurrence Matrix Implementation Preparing Data Getting Vocabulary.mp4

04:26

133 - Word Cooccurrence Matrix Implementation Final Function.mp4

11:37

134 - Word Cooccurrence Matrix Implementation Handling Memory Issues On Large Corp.mp4

09:19

135 - Word Cooccurrence Matrix Sparsity.mp4

05:19

136 - Word Cooccurrence Matrix Positive Point Wise Mutual Information PPMI.mp4

07:58

137 - PCA For Dense Embeddings.mp4

05:31

138 - Latent Semantic Analysis.mp4

04:25

139 - Latent Semantic Analysis Implementation.mp4

06:50

15 - Word Semantics

140 - Links for the Courses Materials and Codes.html

141 - Cosine Similarity.mp4

07:05

142 - Cosine Similarity Geting Norms Of Vectors.mp4

09:18

143 - Cosine Similarity Normalizing Vectors.mp4

06:36

144 - Cosine Similarity With More Than One Vectors.mp4

11:00

145 - Cosine Similarity Getting Most Similar Words In The Vocabulary.mp4

10:05

146 - Cosine Similarity Getting Most Similar Words In The Vocabulary Fixingbug Of D.mp4

06:23

147 - Cosine Similarity Word2Vec Embeddings.mp4

08:21

148 - Words Analogies.mp4

04:50

149 - Words Analogies Implemenation 1.mp4

05:55

150 - Words Analogies Implemenation 2.mp4

06:13

151 - Words Visualizations.mp4

02:37

152 - Words Visualizations Implementaion.mp4

04:28

153 - Words Visualizations Implementaion 2.mp4

06:36

16 - Word2vecOptional

154 - Links for the Courses Materials and Codes.html

155 - Static And Dynamic Embeddings.mp4

06:17

156 - Self Supervision.mp4

04:23

157 - Word2Vec Algorithm Abstract.mp4

05:21

158 - Word2Vec Why Negative Sampling.mp4

03:49

159 - Word2Vec What Is Skip Gram.mp4

04:43

160 - Word2Vec How To Define Probability Law.mp4

04:00

161 - Word2Vec Sigmoid.mp4

05:21

162 - Word2Vec Formalizing Loss Function.mp4

05:49

163 - Word2Vec Loss Function.mp4

03:25

164 - Word2Vec Gradient Descent Step.mp4

04:21

165 - Word2Vec Implemenation Preparing Data.mp4

10:37

166 - Word2Vec Implemenation Gradient Step.mp4

07:18

167 - Word2Vec Implemenation Driver Function.mp4

13:38

17 - Need of Deep Learning for NLPNLP with Deep Learning DNN

168 - Links for the Courses Materials and Codes.html

169 - Why RNNs For NLP.mp4

13:25

170 - Pytorch Installation And Tensors Introduction.mp4

10:32

171 - Automatic Diffrenciation Pytorch.mp4

08:26

18 - IntroductionNLP with Deep Learning DNN

172 - Links for the Courses Materials and Codes.html

173 - Why DNNs In Machine Learning.mp4

04:13

174 - Representational Power And Data Utilization Capacity Of DNN.mp4

07:13

175 - Perceptron.mp4

05:08

176 - Perceptron Implementation.mp4

07:26

177 - DNN Architecture.mp4

03:52

178 - DNN Forwardstep Implementation.mp4

08:21

179 - DNN Why Activation Function Is Required.mp4

04:47

180 - DNN Properties Of Activation Function.mp4

06:04

181 - DNN Activation Functions In Pytorch.mp4

03:49

182 - DNN What Is Loss Function.mp4

07:10

183 - DNN Loss Function In Pytorch.mp4

05:45

19 - TrainingNLP with Deep Learning DNN

184 - Links for the Courses Materials and Codes.html

185 - DNN Gradient Descent.mp4

05:58

186 - DNN Gradient Descent Implementation.mp4

06:51

187 - DNN Gradient Descent Stochastic Batch Minibatch.mp4

07:07

188 - DNN Gradient Descent Summary.mp4

02:38

189 - DNN Implemenation Gradient Step.mp4

04:02

190 - DNN Implemenation Stochastic Gradient Descent.mp4

13:53

191 - DNN Implemenation Batch Gradient Descent.mp4

06:46

192 - DNN Implemenation Minibatch Gradient Descent.mp4

09:04

193 - DNN Implemenation In Pytorch.mp4

15:19

20 - Hyper parametersNLP with Deep Learning DNN

194 - Links for the Courses Materials and Codes.html

195 - DNN Weights Initializations.mp4

04:35

196 - DNN Learning Rate.mp4

04:03

197 - DNN Batch Normalization.mp4

02:05

198 - DNN Batch Normalization Implementation.mp4

02:41

199 - DNN Optimizations.mp4

04:08

200 - DNN Dropout.mp4

03:58

201 - DNN Dropout In Pytorch.mp4

02:03

202 - DNN Early Stopping.mp4

03:34

203 - DNN Hyperparameters.mp4

03:33

204 - DNN Pytorch CIFAR10 Example.mp4

15:56

21 - IntroductionNLP with Deep Learning RNN

205 - Links for the Courses Materials and Codes.html

206 - What Is RNN.mp4

04:54

207 - Understanding RNN With A Simple Example.mp4

08:30

208 - RNN Applications Human Activity Recognition.mp4

02:53

209 - RNN Applications Image Captioning.mp4

02:37

210 - RNN Applications Machine Translation.mp4

03:16

211 - RNN Applications Speech Recognition Stock Price Prediction.mp4

04:05

212 - RNN Models.mp4

07:07

22 - Miniproject Language ModellingNLP with Deep Learning RNN

213 - Links for the Courses Materials and Codes.html

214 - Language Modeling Next Word Prediction.mp4

03:43

215 - Language Modeling Next Word Prediction Vocabulary Index.mp4

04:04

216 - Language Modeling Next Word Prediction Vocabulary Index Embeddings.mp4

03:16

217 - Language Modeling Next Word Prediction Rnn Architecture.mp4

04:09

218 - Language Modeling Next Word Prediction Python 1.mp4

07:12

219 - Language Modeling Next Word Prediction Python 2.mp4

09:02

220 - Language Modeling Next Word Prediction Python 3.mp4

07:27

221 - Language Modeling Next Word Prediction Python 4.mp4

05:33

222 - Language Modeling Next Word Prediction Python 5.mp4

04:35

223 - Language Modeling Next Word Prediction Python 6.mp4

13:34

23 - Miniproject Sentiment ClassificationNLP with Deep Learning RNN

224 - Links for the Courses Materials and Codes.html

225 - Vocabulary Implementation.mp4

09:46

226 - Vocabulary Implementation Helpers.mp4

05:50

227 - Vocabulary Implementation From File.mp4

06:26

228 - Vectorizer.mp4

05:17

229 - RNN Setup.mp4

07:20

230 - RNN Setup 1.mp4

21:23

24 - RNN in PyTorchNLP with Deep Learning RNN

231 - Links for the Courses Materials and Codes.html

232 - RNN In Pytorch Introduction.mp4

02:04

233 - RNN In Pytorch Embedding Layer.mp4

07:21

234 - RNN In Pytorch Nn Rnn.mp4

08:47

235 - RNN In Pytorch Output Shapes.mp4

04:11

236 - RNN In Pytorch Gatedunits.mp4

03:39

237 - RNN In Pytorch Gatedunits GRU LSTM.mp4

03:55

238 - RNN In Pytorch Bidirectional RNN.mp4

02:44

239 - RNN In Pytorch Bidirectional RNN Output Shapes.mp4

04:26

240 - RNN In Pytorch Bidirectional RNN Output Shapes Seperation.mp4

04:33

241 - RNN In Pytorch Example.mp4

09:48

25 - Advanced RNN modelsNLP with Deep Learning RNN

242 - Links for the Courses Materials and Codes.html

243 - RNN Encoder Decoder.mp4

03:01

244 - RNN Attention.mp4

03:28

26 - Neural Machine Translation

245 - Links for the Courses Materials and Codes.html

246 - Introduction To Dataset And Packages.mp4

05:10

247 - Implementing Language Class.mp4

05:22

248 - Testing Language Class And Implementing Normalization.mp4

09:44

249 - Reading Datafile.mp4

05:17

250 - Reading Building Vocabulary.mp4

07:56

251 - EncoderRNN.mp4

06:14

252 - DecoderRNN.mp4

06:13

253 - DecoderRNN Forward Step.mp4

12:08

254 - DecoderRNN Helper Functions.mp4

04:54

255 - Training Module.mp4

13:31

256 - Stochastic Gradient Descent.mp4

07:12

257 - NMT Training.mp4

05:06

258 - NMT Evaluation.mp4

11:13

Description

Mastering Natural Language Processing with Spacy, NLTK, PyTorch, NLP Techniques, Text Data Analysis, Hands-on Projects

What You'll Learn?

• The importance of Natural Language Processing (NLP) in Data Science.
• The reasons to move from classical sequence models to deep learning-based sequence models.
• The essential concepts from the absolute beginning with complete unraveling with examples in Python.
• Details of deep learning models for NLP with examples.
• A summary of the concepts of Deep Learning theory.
• Practical description and live coding with Python.
• Deep PyTorch (Deep learning framework by Facebook).
• The use and applications of state-of-the-art NLP models.
• Building your own applications for automatic text generation and language translators.
• And much more…

Who is this for?

• Complete beginners to Natural Language Processing.

• People who want to upgrade their Python programming skills for NLP.

• Individuals who are passionate about data science and machine learning.

• Data Scientists.

• Data Analysts.

• Machine Learning Practitioners.

What You Need to Know?

• No prior knowledge is required. You will start from the fundamental concepts and slowly build your knowledge of the subject.

• A willingness to learn and practice.

• Knowledge of Python will be a plus.

More details

Description
Master Natural Language Processing (NLP): Unleash the Power of AI in Language Understanding and Text Analysis

Are you ready to embark on an exciting journey into the world of Natural Language Processing (NLP)? This comprehensive course is your gateway to mastering the art of understanding human language and harnessing the incredible capabilities of AI for text analysis and language understanding. Whether you're a novice or an aspiring NLP practitioner, this course offers an extensive exploration of NLP theory and hands-on practice using Python.

Course Highlights:
In this enlightening course, you will:
1. Explore NLP Foundations: Gain a solid understanding of NLP concepts, its importance, and its applications in fields like speech recognition, sentiment analysis, language translation, and chatbots.

2. Harness Python's Power: Leverage Python's extensive libraries and tools for text analysis, text preprocessing, and data extraction. Python's versatility makes it the ideal language for NLP.

3. Master Text Preprocessing: Dive into the nitty-gritty of text preprocessing, including regular expressions, text normalization, tokenization, and more. Learn how to prepare text data for analysis effectively.

4. Decode Word Embeddings: Unlock the potential of word embeddings, from traditional methods like one-hot vectors to advanced techniques like Word2Vec, GloVe, and BERT. Understand how words are represented in vectors and their applications.

5. Grasp Deep Learning for NLP: Explore neural networks, recurrent neural networks (RNNs), their types (one to one, one to many, many to one, many to many), bi-directional RNNs, deep RNNs, and more. Understand how deep learning is revolutionizing NLP.

6. Real-World Projects: Apply your NLP skills to practical projects, including building a Neural Machine/Language Translator and developing a Chatbot. These projects will challenge you and reinforce your learning.

7. Extensive Learning Material: Access high-quality video lectures, assessments, course notes, and handouts to enhance your understanding. We provide comprehensive resources to support your learning journey.

8. Supportive Community: Reach out to our friendly team for prompt assistance with any course-related queries. We are here to help you succeed.

Course Modules:
Here's a glimpse of what you'll explore throughout this comprehensive course:
Introduction to NLP: Understand the essence of NLP, its significance, and its applications in various domains. Get an overview of essential software tools used in NLP.

Text Preprocessing: Dive into text preprocessing techniques, including regular expressions, text normalization, tokenization, and string matching. Learn how to clean and prepare text data for analysis.

Word Embeddings: Explore language models, vocabulary, N-Grams, one-hot vectors, and advanced word embeddings like Word2Vec, GloVe, and BERT. Understand the mathematical foundations and applications of word embeddings.

NLP with Deep Learning: Master neural networks, different RNN architectures (one to one, one to many, many to one, many to many), advanced RNN models for NLP (encoder-decoder models, attention mechanisms), and deep learning techniques. Discover how deep learning has transformed NLP.

Projects: Apply your newfound knowledge to real-world projects. Build a Neural Machine/Language Translator and create a Chatbot. These hands-on projects will allow you to demonstrate your skills and creativity in solving practical NLP problems.

Who Should Enroll:
This course is designed to cater to a wide audience, making it suitable for:
Beginners who are eager to venture into the fascinating world of Natural Language Processing
Python enthusiasts looking to enhance their programming skills for NLP applications
Data Scientists, Data Analysts, and Machine Learning Practitioners aiming to add NLP expertise to their skill set

Upon successful completion of this course, you'll be equipped with the knowledge and hands-on experience to confidently tackle NLP challenges, create AI-powered language understanding systems, and embark on exciting career opportunities in the field of Natural Language Processing.

Unlock the Potential of NLP and Transform Your Skill Set. Enroll Now and Harness the Power of AI in Language Understanding and Text Analysis!

Keywords:
Natural Language Processing (NLP)
Artificial Intelligence (AI)
Text Analysis
Language Understanding
Python Programming
Text Preprocessing
Word Embeddings
Word Vectors
Deep Learning for NLP
Neural Networks
Recurrent Neural Networks (RNNs)
Word2Vec
GloVe
BERT
Language Models
Chatbots
Sentiment Analysis
Speech Recognition
Machine Translation
Text Data Processing
Text Normalization
Tokenization
Regular Expressions
Data Extraction
Text Mining
NLP Applications
Natural Language Understanding
Language Processing Tools
NLP Projects
AI-powered Language Systems
Career Opportunities in NLP
NLP Certification
Master NLP with Python
Learn Text Analysis with NLP
Python for Natural Language Processing
Dive into Word Embeddings
Deep Learning Techniques for NLP
Hands-on NLP Projects
Build AI-driven Chatbots
Sentiment Analysis in Python
NLP Career Advancement
Language Understanding Systems
Natural Language Processing Course
NLP Training and Certification
AI in Text Data Analysis
Harnessing NLP in Python
Unlock the Power of NLP
Real-world NLP Applications
Who this course is for:
â€¢ Complete beginners to Natural Language Processing.
â€¢ People who want to upgrade their Python programming skills for NLP.
â€¢ Individuals who are passionate about data science and machine learning.
â€¢ Data Scientists.
â€¢ Data Analysts.
â€¢ Machine Learning Practitioners.

User Reviews

Rating

average 0

Total votes0

Focused display

We are a group of experts, PhDs and Practitioners of Artificial Intelligence, Computer Science, Machine Learning, and Statistics. Some of us work in big companies like Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.We decided to produce 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, our objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory and without a long reading. Today we also publish a more complete course on some topics for a wider audience.Our courses have had phenomenal success. Our Courses have helped more than 100,000 students to master AI and Data Science.

AI Sciences Team

Instructor's Courses

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.