English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 283 Lessons (26h 56m) | 5.50 GB
Develop job-ready gen AI skills employers need. Build highly sought-after gen AI engineering skills and practical experience in just 6 months. No prior experience required.
What you’ll learn
- Job-ready skills employers are crying out for in gen AI, machine learning, deep learning, NLP apps, and large language models in just 6 months.
- Build and deploy generative AI applications, agents and chatbots using Python libraries like Flask, SciPy and ScikitLearn, Keras, and PyTorch.
- Key gen AI architectures and NLP models, and how to apply techniques like prompt engineering, model training, and fine-tuning.
- Apply transformers like BERT and LLMs like GPT for NLP tasks, with frameworks like RAG and LangChain.
Skills you’ll gain
- PyTorch (Machine Learning Library)
- Hugging Face (NLP Framework)
- RAG
- LangChain
- Large Language Models
The generative AI market is expected to grow over 46% CAGR to 2030 (Statista). The demand for tech professionals with gen AI engineering skills is exploding!
The IBM Generative AI Engineering Professional Certificate gives aspiring gen AI engineers, AI developers, data scientists, machine learning engineers, and AI research engineers the essential skills in gen AI, large language models (LLMs), and natural language processing (NLP) required to catch the eye of an employer.
A gen AI engineer designs AI systems that produce new data—like images, text, audio, and video—using transformers and LLMs. In this program, you’ll dive into AI, gen AI, and prompt engineering, along with data analysis, machine learning, and deep learning using Python. You’ll work with libraries like SciPy and scikit-learn and build apps using frameworks and models such as BERT, GPT, and LLaMA. You’ll use Hugging Face Transformers, PyTorch, RAG, and LangChain for developing and deploying LLM NLP-based apps, while exploring tokenization, language models, and transformer techniques.
You’ll also get plenty of practical experience in hands-on labs and projects that you can talk about in interviews. Plus, you’ll complete a significant guided project where you’ll create your own real-world gen AI application.
If you’re keen to stand out from the crowd with gen AI skills employers desperately need, ENROLL TODAY and transform your career opportunities in less than 6 months.
Applied Learning Project
Practical Experience Employers Look For
Practical experience speaks volumes in a job interview. This Professional Certificate gives you valuable hands-on experience that confirms to employers you’ve got what it takes!
The hands-on work includes:
- Generating text, images, and code through gen AI
- Applying prompt engineering techniques and best practices
- Creating multiple gen AI-powered applications with Python and deploying them using Flask
- Creating an NLP data loader
- Developing and training a simple language model with a neural network
- Applying transformers for classification, and building and evaluating a translation model
- Performing prompt engineering and in-context learning
- Fine-tuning models to improve performance
- Using LangChain tools and components for different applications
- Building AI agents and applications with RAG and LangChain in a significant guided project.
Table of Contents
building-gen-ai-powered-applications
image-captioning-with-generative-ai
welcome-to-the-course
1 course-introduction
2 course-syllabus-and-prerequisites_instructions
3 generative-ai-models
4 foundation-models
captioning-photos-with-generative-ai
5 project-overview-image-captioning-with-generative-ai
6 hugging-face
module-summary-and-assessment
7 module-summary-image-captioning-with-generative-ai_instructions
create-your-own-chatgpt-like-website
create-chatbot-with-open-source-llms
8 project-overview-create-your-own-chatgpt-like-website
module-summary-and-assessment
9 module-summary-create-your-own-chatgpt-like-website_instructions
create-a-voice-assistant
create-a-voice-assistant-with-openai-s-gpt-3-and-ibm-watson
10 project-overview-create-a-voice-assistant
11 introduction-to-docker
module-summary-and-assessment
12 module-summary-create-a-voice-assistant_instructions
generative-ai-powered-meeting-assistant
create-an-app-for-summarizing-meetings
13 project-overview-generative-ai-powered-meeting-assistant
14 ibm-watsonx-ai
module-summary-and-assessment
15 module-summary-generative-ai-powered-meeting-assistant_instructions
summarize-your-private-data-with-generative-ai-and-rag
create-an-app-for-summarizing-your-private-data
16 project-overview-summarize-your-private-data-with-generative-ai-rag
17 introduction-to-langchain
18 enhancing-llm-accuracy-with-rag
module-summary-and-assessment
19 module-summary-summarize-your-private-data-with-generative-ai_instructions
babel-fish-universal-language-translator-with-llm-and-stt-tts
create-a-translation-assistant
20 introduction-to-project-babel-fish-with-llm-and-stt-tts
module-summary-and-assessment
21 module-summary-babel-fish-with-llm-and-stt-tts_instructions
course-wrap-up
22 congratulations-and-next-steps_instructions
23 thanks-from-the-course-team_instructions
bonus-module-7-build-an-ai-career-coach
create-a-personalized-job-application-coach
24 introduction-to-project-build-an-ai-career-coach
module-summary-and-assessment
25 module-summary-build-an-ai-career-coach_instructions
data-analysis-with-python
importing-data-sets
importing-data-sets
26 course-introduction
27 understanding-the-data
28 python-packages-for-data-science
29 importing-and-exporting-data-in-python
30 getting-started-analyzing-data-in-python
31 accessing-databases-with-python
32 lesson-summary_instructions
data-wrangling
data-wrangling
33 pre-processing-data-in-python
34 dealing-with-missing-values-in-python
35 data-formatting-in-python
36 data-normalization-in-python
37 binning-in-python
38 turning-categorical-variables-into-quantitative-variables-in-python
39 lesson-summary_instructions
exploratory-data-analysis
exploratory-data-analysis
40 exploratory-data-analysis
41 descriptive-statistics
42 groupby-in-python
43 correlation
44 correlation-statistics
45 lesson-summary_instructions
model-development
model-development
46 model-development
47 linear-regression-and-multiple-linear-regression
48 model-evaluation-using-visualization
49 polynomial-regression-and-pipelines
50 measures-for-in-sample-evaluation
51 prediction-and-decision-making
52 lesson-summary_instructions
model-evaluation-and-refinement
model-evaluation-and-refinement
53 model-evaluation-and-refinement
54 overfitting-underfitting-and-model-selection
55 ridge-regression
56 grid-search
57 lesson-summary_instructions
final-assignment
final-project
58 final-project-scenario_instructions
final-exam
59 cheat-sheet-data-analysis-for-python_instructions
digital-badge
60 ibm-digital-badge_instructions
acknowledgments
61 congratulations-and-next-steps_instructions
62 thanks-from-the-course-team_instructions
fundamentals-of-ai-agents-using-rag-and-langchain
rag-framework
welcome-to-the-course
63 course-introduction
64 course-overview_instructions
65 specialization-overview_instructions
introduction-to-rag
66 rag
67 rag-encoders-and-faiss
68 reading-summary-and-highlights_instructions
prompt-engineering-and-langchain
prompt-engineering-and-langchain
69 introduction-to-langchain
70 introduction-to-prompt-engineering-and-in-context-learning
71 advanced-methods-of-prompt-engineering
72 langchain-core-concepts
73 langchain-documents-for-building-rag-applications
74 langchain-chains-and-agents-for-building-applications
75 summary-and-highlights_instructions
course-wrap-up
76 course-conclusion_instructions
77 congratulations-and-next-steps_instructions
78 thanks-from-the-course-team_instructions
gen-ai-foundational-models-for-nlp-and-language-understanding
fundamentals-of-language-understanding
welcome
79 course-introduction
80 course-overview_instructions
81 specialization-overview_instructions
language-understanding-with-neural-networks
82 converting-words-to-features
83 document-categorization-prediction-with-torchtext
84 document-categorization-training-with-torchtext
85 training-the-model-in-pytorch
86 summary-and-highlights_instructions
n-gram-model
87 language-modeling-with-n-grams
88 n-grams-as-neural-networks-with-pytorch
89 summary-and-highlights_instructions
word2vec-and-sequence-to-sequence-models
word2vec-sequence-to-sequence-models-and-evaluation
90 word2vec-introduction-and-cbow-models
91 word2vec-skip-gram-and-pretrained-models
92 introduction-to-sequence-to-sequence-models-and-recurrent-neural-networks
93 encoder-decoder-rnn-models-training-and-inference
94 encoder-decoder-rnn-models-translation
95 ethical-implications-of-word-embeddings-and-language-models_instructions
96 metrics-for-evaluating-the-quality-of-generated-text
97 summary-and-highlights_instructions
course-wrap-up
98 course-conclusion_instructions
99 congratulations-and-next-steps_instructions
100 team-and-acknowledgements_instructions
generative-ai-advanced-fine-tuning-for-llms
different-approaches-to-fine-tuning
welcome-to-the-course
101 course-introduction
102 course-overview_instructions
103 specialization-overview_instructions
instruction-tuning-and-reward-modeling
104 basics-of-instruction-tuning
105 instruction-tuning-with-hugging-face
106 best-practices-for-instruction-tuning-large-language-models_instructions
107 reward-modeling-response-evaluation
108 reward-model-training
109 reward-modeling-with-hugging-face
110 summary-and-highlights_instructions
fine-tuning-causal-llms-with-human-feedback-and-direct-preference
ppo
111 large-language-models-llms-as-distributions
112 from-distributions-to-policies
113 reinforcement-learning-from-human-feedback-rlhf
114 proximal-policy-optimization-ppo
115 ppo-with-hugging-face
116 ppo-trainer
117 summary-and-highlights_instructions
dpo
118 dpo-partition-function
119 dpo-optimal-solution
120 from-optimal-policy-to-dpo
121 dpo-with-hugging-face
122 summary-and-highlights_instructions
course-wrap-up
123 course-conclusion_instructions
124 congratulations-and-next-steps_instructions
125 thanks-from-the-course-team_instructions
generative-ai-engineering-and-fine-tuning-transformers
transformers-and-fine-tuning
course-introduction
126 course-introduction
127 course-overview_instructions
128 specialization-overview_instructions
129 helpful-tips-for-course-completion_Helpful_Tips_IBM_SkillsNetwork
130 helpful-tips-for-course-completion_instructions
transfer-learning-in-nlp
131 hugging-face-vs-pytorch
132 using-pre-trained-transformers-and-fine-tuning
133 fine-tuning-with-pytorch
134 fine-tuning-with-hugging-face
135 reading-summary-and-highlights_instructions
parameter-efficient-fine-tuning-peft
parameter-efficient-fine-tuning-peft
136 video-introduction-to-peft
137 low-rank-adaptation-lora
138 lora-with-hugging-face-and-pytorch
139 from-quantization-to-qlora
140 ethical-considerations-in-fine-tuning-large-language-models_instructions
141 summary-and-highlights_instructions
course-wrap-up
142 course-conclusion_instructions
143 congratulations-and-next-steps_instructions
144 thanks-from-the-course-team_instructions
generative-ai-introduction-and-applications
introduction-and-capabilities-of-generative-ai
welcome-to-the-course
145 course-introduction
146 why-learn-generative-ai-with-ibm
147 specialization-overview_instructions
148 generative-ai-fundamentals-specialization-introduction
149 course-overview_instructions
150 helpful-tips-for-course-completion_Helpful_Tips_IBM_SkillsNetwork
151 helpful-tips-for-course-completion_instructions
152 expert-viewpoints-sme-introductions
generative-ai-and-its-capabilities
153 introduction-to-generative-ai
154 capabilities-of-generative-ai
155 expert-viewpoints-generative-ai-capabilities
156 expert-viewpoints-exploring-the-evolution-of-generative-ai
157 lesson-summary-generative-ai-and-its-capabilities_instructions
applications-and-tools-of-generative-ai
generative-ai-applications-and-tools
158 applications-of-generative-ai
159 tools-for-text-generation
160 tools-for-image-generation
161 tools-for-audio-and-video-generation
162 tools-for-code-generation
163 expert-viewpoints-leveraging-generative-ai-tools
164 expert-viewpoints-exploring-generative-ai-applications-across-domains
165 generative-versus-agentic-ai
166 reading-lesson-summary-applications-and-tools-of-generative-ai_instructions
course-quiz-project-and-wrap-up
graded-quiz-and-wrap-up
generative-ai-language-modeling-with-transformers
fundamental-concepts-of-transformer-architecture
welcome
167 course-introduction
168 course-overview_instructions
169 specialization-overview_instructions
positional-encoding-attention-and-application-in-classification
170 positional-encoding
171 attention-mechanism
172 self-attention-mechanism
173 from-attention-to-transformers
174 transformers-for-classification-encoder
175 optimization-techniques-for-efficient-transformer-training_instructions
176 summary-and-highlights_instructions
advanced-concepts-of-transformer-architecture
decoder-models
177 language-modeling-with-the-decoders-and-gpt-like-models
178 training-decoder-models
179 decoder-models-pytorch-implementation-causal-lm
180 decoder-models-pytorch-implementation-using-training-and-inference
181 summary-and-highlights_instructions
encoder-models
182 encoder-models-with-bert-pretraining-using-mlm
183 encoder-models-with-bert-pretraining-using-nsp
184 data-preparation-for-bert-with-pytorch
185 pretraining-bert-models-with-pytorch
186 summary-and-highlights_instructions
application-of-transformers-for-translation
187 transformer-architecture-for-language-translation
188 transformer-architecture-for-translation-pytorch-implementation
189 summary-and-highlights_instructions
course-wrap-up
190 course-conclusion_instructions
191 thanks-from-the-course-team_instructions
192 congratulations-and-next-steps_instructions
generative-ai-llm-architecture-data-preparation
generative-ai-architecture
welcome
193 overview-of-ai-engineering-with-llms
194 course-introduction
195 course-overview_instructions
generative-ai-overview-and-architecture
196 significance-of-generative-ai
197 generative-ai-architectures-and-models
198 generative-ai-for-nlp
199 summary-and-highlights_instructions
data-preparation-for-llms
preparing-data
200 tokenization
201 overview-of-data-loaders
202 data-quality-and-diversity-for-effective-llm-training_instructions
203 summary-and-highlights_instructions
course-wrap-up
204 course-conclusion_instructions
205 congratulations-and-next-steps_instructions
206 team-and-acknowledgments_instructions
generative-ai-prompt-engineering-for-everyone
prompt-engineering-for-generative-ai
course-introduction
207 course-introduction
208 course-overview_instructions
209 specialization-overview_instructions
210 helpful-tips-for-course-completion_Helpful_Tips_IBM_SkillsNetwork
211 helpful-tips-for-course-completion_instructions
concept-of-prompt-engineering
212 what-is-a-prompt
213 what-is-prompt-engineering
214 best-practices-for-prompt-creation
215 common-prompt-engineering-tools
216 expert-viewpoints-best-practices-for-effective-prompts
217 expert-viewpoints-exploring-prompt-engineering-in-generative-ai
218 reading-lesson-summary-concept-of-prompt-engineering_instructions
prompt-engineering-techniques-and-approaches
techniques-and-approaches-for-writing-effective-prompts
219 text-to-text-prompt-techniques
220 interview-pattern-approach
221 chain-of-thought-approach
222 tree-of-thought-approach
223 expert-viewpoints-key-considerations-in-choosing-prompt-engineering-approach
224 reading-lesson-summary-key-techniques-of-prompt-engineering_instructions
course-quiz-project-and-wrap-up
text-to-image-prompting
225 text-to-image-prompts-techniques
glossary-and-final-project
course-wrap-up
226 course-wrap-up
introduction-to-ai
introduction-and-applications-of-ai
welcome
227 course-introduction
228 course-overview_instructions
introduction-to-ai
229 introducing-ai
230 artificial-intelligence-vs-augmented-intelligence
231 introducing-generative-ai-and-its-use-cases
232 the-evolution-of-ai-traditional-ai-vs-generative-ai
233 artificial-intelligence-are-we-there-yet
impact-and-applications-of-ai
234 ai-in-daily-life
235 ai-chatbots-and-smart-assistants
236 what-is-a-chatbot
237 applications-of-ai-in-different-industries
238 generative-ai-tools-and-applications
239 ten-everyday-ai-and-machine-learning-use-cases
module-1-module-summary-and-graded-quiz
ai-concepts-terminology-and-application-domains
fundamental-concepts-of-ai
240 cognitive-computing
241 terminologies-and-related-concepts-of-ai
242 machine-learning
243 machine-learning-techniques-and-training
244 deep-learning
245 neural-networks
246 machine-learning-vs-deep-learning
247 generative-ai-models
248 large-language-models
249 machine-learning-vs-deep-learning-vs-foundation-models
domains-of-ai
250 natural-language-processing-speech-and-computer-vision
251 what-is-nlp-natural-language-processing
252 self-driving-cars
253 ai-and-cloud-computing-edge-computing-and-iot
module-2-summary-and-graded-quiz
business-and-career-transformation-through-ai
ai-domains-for-industries
254 ai-agents
255 what-are-ai-agents
256 robotics-and-automation
ai-for-businesses
257 transforming-businesses-through-ai
258 the-rise-of-generative-ai-for-business
259 become-a-value-creator-with-generative-ai
260 what-is-retrieval-augmented-generation-rag
261 adopting-ai-in-your-business
262 frameworks-for-ai-adoption
ai-for-your-work-and-career
263 transforming-your-work-through-ai-tools
264 career-opportunities-with-ai
265 humans-vs-ai-who-should-make-the-decision
module-3-summary-and-graded-quiz
issues-concerns-and-ethical-considerations
ai-concerns-and-ethical-considerations
266 ethical-considerations-and-responsible-use-of-ai
267 considerations-around-generative-ai
268 why-large-language-models-hallucinate
269 perspective-of-key-players-around-ai-ethics
270 the-importance-of-ai-governance
271 how-to-implement-ai-ethics
272 lesson-summary_instructions
graded-assignment-and-wrap-up
273 course-wrap-up
274 congratulations-and-next-steps_instructions
275 thanks-from-the-course-team_instructions
introduction-to-deep-learning-with-keras
introduction-to-deep-learning-and-neural-networks
welcome
276 course-introduction
277 course-overview_instructions
introduction-to-deep-learning-neurons-and-artificial-neural-networks
278 introduction-to-deep-learning
279 neurons-and-neural-networks
280 artificial-neural-networks
module-1-summary-and-evaluation
281 module-summary-introduction-to-neural-networks-and-deep-learning_instructions
basics-of-deep-learning
deep-learning-fundamentals
282 gradient-descent
283 backpropagation
284 vanishing-gradient
285 activation-functions
module-2-summary-and-evaluation
286 module-2-summary-basics-of-deep-learning_instructions
keras-and-deep-learning-libraries
modeling-with-keras
287 deep-learning-libraries
288 regression-models-with-keras
289 classification-models-with-keras
module-3-summary-and-evaluation
290 module-3-summary-keras-and-deep-learning-libraries_instructions
deep-learning-models
supervised-and-unsupervised-neural-networks
291 shallow-versus-deep-neural-networks
292 convolutional-neural-networks
293 recurrent-neural-networks
294 transformers
295 autoencoders
296 using-pre-trained-models
module-4-summary-and-evaluation
297 module-4-summary-deep-learning-models_instructions
final-project-and-course-wrap-up
course-wrap-up
298 course-wrap-up
299 congratulations-and-next-steps_instructions
300 team-and-acknowledgments_instructions
machine-learning-with-python
introduction-to-machine-learning
welcome-to-the-course
301 course-introduction
302 course-overview_instructions
303 ibm-ai-engineering-pc-overview
machine-learning-in-action
304 an-overview-of-machine-learning
305 machine-learning-model-lifecycle
306 a-day-in-the-life-of-a-machine-learning-engineer
307 data-scientist-vs-ai-engineer
308 tools-for-machine-learning
309 scikit-learn-machine-learning-ecosystem
module-summary-evaluation
310 module-1-summary-and-highlights_instructions
linear-and-logistic-regression
linear-regression
311 introduction-to-regression
312 introduction-to-simple-linear-regression
313 multiple-linear-regression
314 polynomial-and-non-linear-regression
logistic-regression
315 introduction-to-logistic-regression
316 training-a-logistic-regression-model
module-summary-cheat-sheet-evaluation
317 module-2-summary-and-highlights_instructions
building-supervised-learning-models
classification-and-regression
318 classification
319 decision-trees
320 regression-trees
other-supervised-learning-models
321 supervised-learning-with-svms
322 supervised-learning-with-knn
323 bias-variance-and-ensemble-models
module-summary-cheat-sheet-evaluation
324 module-3-summary-and-highlights_instructions
building-unsupervised-learning-models
clustering
325 clustering-strategies-and-real-world-applications
326 k-means-and-more-on-k-means
327 dbscan-and-hdbscan-clustering
dimension-reduction-feature-engineering
328 clustering-dimension-reduction-and-feature-engineering
329 dimension-reduction-algorithms
module-summary-cheat-sheet-evaluation
330 module-4-summary-and-highlights_instructions
evaluating-and-validating-machine-learning-models
evaluating-machine-learning-models
331 classification-metrics-and-evaluation-techniques
332 regression-metrics-and-evaluation-techniques
333 evaluating-unsupervised-learning-models-heuristics-and-techniques
best-practices-for-ensuring-model-generalizability
334 cross-validation-and-advanced-model-validation-techniques
335 regularization-in-regression-and-classification
336 data-leakage-and-other-pitfalls
module-summary-cheat-sheet-evaluation
337 module-5-summary-and-highlights_instructions
final-project-and-exam
final-project
338 final-project-scenario_instructions
course-summary-and-final-exam
339 course-wrap-up
course-wrap-up
340 congratulations-and-next-steps_instructions
341 thanks-from-the-course-team_instructions
project-generative-ai-applications-with-rag-and-langchain
document-loader-using-langchain
welcome-to-the-course
342 course-introduction
343 course-overview_instructions
344 specialization-overview_instructions
different-document-loaders-from-langchain
345 load-your-document-from-different-sources
346 best-practices-for-loading-documents-in-langchain-applications_instructions
text-splitter
347 strategies-for-splitting-text-for-optimal-processing
module-summary
348 reading-summary-and-highlights_instructions
rag-using-langchain
embedding-the-document
349 introduction-to-vector-databases-for-storing-embeddings
retriever
350 explore-advanced-retrievers-in-langchain-part-1
351 explore-advanced-retrievers-in-langchain-part-2
rag-using-langchain-summary
352 module-summary-rag-using-langchain_instructions
create-a-qa-bot-to-read-your-document
introduction-to-gradio
353 getting-started-with-gradio
build-a-qa-bot-web-app-summary
354 module-summary-create-a-qa-bot-to-read-your-document_instructions
course-wrap-up
355 course-conclusion_instructions
356 congratulations-and-next-steps_instructions
357 thanks-from-the-course-team_instructions
python-for-applied-data-science-ai
python-basics
about-the-course
358 course-introduction
359 about-this-course_instructions
360 course-overview_instructions
361 helpful-tips-for-course-completion_Helpful_Tips_IBM_SkillsNetwork
362 helpful-tips-for-course-completion_instructions
getting-started-with-python-and-jupyter
363 introduction-to-python
364 introduction-to-jupyter_instructions
365 getting-started-with-jupyter
types
366 types
expressions-and-variables
367 expressions-and-variables
string-operations
368 string-operations
module-1-summary-cheatsheet-graded-quiz-and-glossary
python-data-structures
lists-and-tuples
369 lists-and-tuples
dictionaries
370 dictionaries
sets
371 sets
module-2-summary-cheatsheet-graded-quiz-and-glossary
python-programming-fundamentals
conditions-and-branching
372 conditions-and-branching
loops
373 loops
functions
374 functions
exception-handling
375 exception-handling
objects-and-classes
376 objects-and-classes
module-3-summary-cheatsheet-graded-quiz-and-glossary
working-with-data-in-python
reading-and-writing-files-with-open
377 reading-files-with-open
378 writing-files-with-open
pandas
379 pandas-loading-data
380 pandas-working-with-and-saving-data
numpy-in-python
381 one-dimensional-numpy
382 two-dimensional-numpy
module-4-summary-cheatsheet-graded-quiz-and-glossary
apis-and-data-collection
simple-apis
383 application-program-interface
rest-apis-web-scraping-and-working-with-files
384 rest-apis-http-requests-part-1
385 rest-apis-http-requests-part-2
386 optional-html-for-web-scraping
387 optional-web-scraping
388 working-with-different-file-formats
module-5-summary-cheatsheet-graded-quiz-and-glossary
course-wrap-up
389 congratulations-and-next-steps_instructions
390 python-cheat-sheet-the-basics_instructions
Resources
python-cheat-sheet-the-basics
391 resources
python-project-for-ai-application-development
python-coding-practices-and-packaging-concepts
welcome
392 introduction-to-the-course
393 helpful-tips-for-completing-this-course_instructions
394 prerequisites-and-course-syllabus_instructions
application-development-and-packaging-using-python
395 application-development-lifecycle
396 introduction-to-web-applications-and-apis
397 demo-working-with-an-ide
398 python-style-guide-and-coding-practices
399 unit-testing
400 packaging
401 module-1-summary-python-coding-practices-and-packaging-concepts_instructions
web-app-deployment-using-flask
web-application-deployment-using-flask
402 python-libraries-and-frameworks-for-application-development
403 introduction-to-flask
404 flask-basic-applications-and-routes
405 request-and-response-objects-using-get-and-post-modes
406 dynamic-routes
407 error-handling
408 deploying-web-apps-using-flask
409 module-2-lesson-summary-web-app-deployment-using-flask_instructions
creating-ai-application-and-deploy-using-flask
graded-final-project
410 module-3-summary-creating-ai-application-and-deploy-using-flask_instructions
course-wrap-up
411 congratulations-next-steps_instructions
412 thanks-from-the-course-team_instructions
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