Mastering AI: Neural Nets, Vision System, Speech Recognition Specialization

ai-applications-computer-vision-and-speech-analysis

computer-vision-with-opencv

evolution-of-ai-and-computer-vision
1 welcome-to-ai-applications-computer-vision-and-speech-recognition_instructions
2 course-introduction
3 industrial-breakthrough-in-audio-and-speech-recognition
4 speech-recognition-technology
5 computer-vision-application
6 computer-vision-applications-medical-and-plant-disease
7 ai-responsibility-pyramid
8 evolution-of-computer-vision-and-speech-analysis
9 evolution-of-speech-analysis
10 what-is-opencv
11 exploring-technologies-for-computer-vision_instructions

setting-up-environment
12 installing-opencv-on-windows
13 installing-opencv-on-windows-handling-libraries-in-jupyter
14 installing-integrated-libraries-numpy-matplotlib-scipy-and-pillow
15 installing-integrated-libraries-dlib-scikit-and-pytorch
16 ethical-considerations-in-computer-vision_instructions

image-processing
17 operations-on-opencv
18 demonstration-loading-the-image-and-encoding-image-to-rgb
19 demonstration-resizing-rotating-and-flipping-the-image
20 demonstration-gaussian-blur
21 demonstration-edge-detection-and-conversion
22 demonstration-image-thresholding-binary-image
23 demonstration-different-methods-of-thresholding
24 demonstration-practical-use-cases
25 what-is-adaptive-thresholding
26 demonstration-of-global-adaptive-threshold
27 demonstration-implementing-adaptive-thresholding-methods
28 lbph-algorithm-local-binary-patterns-histogram_instructions

morphological-operations
29 morphological-operations
30 morphological-operations-in-opencv
31 demonstration-opening-dilation-and-erosion
32 demonstration-closing-and-morphological-gradient
33 blackhat-and-whitehat-transformations
34 demonstration-whitehat-tophat
35 demonstration-blackhat
36 watershed-algorithm-for-image-processing_instructions

module-wrap-up-and-assessment
37 summary-of-computer-vision-with-opencv

video-processing-using-opencv

video-processing-operations
38 video-processing
39 demonstration-implementing-frame-by-frame-video-processing
40 demonstration-exiting-the-processing-operation
41 demonstration-initializing-the-video-frames
42 demonstration-saving-the-frames
43 demonstration-loading-the-data
44 demonstration-reading-and-writing-operations
45 demonstration-histogram-matching
46 demonstration-matching-source-and-reference-images
47 demonstration-cumulative-distribution-function
48 demonstration-differences-in-images
49 marker-based-augmented-reality-ar_instructions

exploring-various-techniques-for-face-detection-and-recognition
50 haar-cascade
51 haar-cascade-algorithm-overview
52 haar-cascade-application-and-limitation
53 demonstration-implementation-of-haar-cascade-algorithm
54 demonstration-face-detection-code-for-static-image
55 demonstration-implementing-boundary-box-for-face-detection
56 demonstration-applying-face-detection-on-images
57 introduction-to-face-recognition
58 demonstration-setting-up-pre-requisite-libraries-and-loading-the-image
59 demonstration-face-recognition-and-detection
60 demonstration-facial-landmark-detection-with-file-loading-and-library-setup
61 demonstration-adjusting-video-details-through-opencv
62 demonstration-face-recognition
63 demonstration-encoding-facial-landmarks
64 demonstration-implementing-facial-landmarks-on-images
65 demonstration-implementing-facial-landmarks-on-videos
66 pros-and-cons-of-opencv-s-haar-cascade-face-detector_instructions

module-wrap-up-and-assessment
67 summary-of-video-processing-with-opencv

speech-recognition-and-audio-analysis

speech-and-its-variation
68 introduction-to-speech-audio-data
69 introduction-to-speech-human-computer-interaction-and-applications
70 processing-speech
71 speech-production
72 difficulties-in-analyzing-speech
73 working-of-sound-waves
74 adc-and-sample-rate-bit-rate
75 conversion-of-adc-analog-to-digital-converter-to-dac-digital-to-analog-converter
76 demonstration-generating-sound
77 demonstration-spectrogram
78 demonstration-signal-frequencies-over-time
79 summary-of-audio-file-analysis
80 demonstration-converting-a-sound-file-into-waveform
81 speech-analysis-in-cyber-security_instructions

digitizing-and-analyzing-speech
82 human-speech
83 speech-waveform
84 digital-signal-processing
85 mfcc-mel-frequency-cepstral-coefficient
86 windowing-formula-and-cepstrum
87 demonstration-computing-the-spectrogram
88 demonstration-digitizing-the-audio-data
89 demonstration-converting-fragmented-parts-of-audio-file-for-speech-recognition
90 voice-onset-voice-offset-tremor-and-noise-detection
91 understanding-the-concepts-of-voice-onset-and-offset
92 tremor-detection
93 demonstration-zcr-pitch-detection-voice-activity-detection
94 demonstration-tremor-detection
95 speech-processing-interactive-creation-and-evaluation-spice-toolkit_instructions

module-warp-up-and-assessment
96 summary-of-speech-recognition-and-audio-analysis

course-wrap-up-and-assessment

untitled-lesson
97 summary-for-ai-applications-computer-vision-and-speech-recognition
98 practice-project-vehicle-tracking-and-detection_instructions

Resources

module-1-resources
99 resources

module-2-resources
100 Traffic
101 resources

module-3-resources
102 resources

module-4-resources
103 highway
104 resources

applied-machine-learning-with-python

introduction-to-machine-learning

machine-learning-essentials
105 welcome-to-applied-machine-learning-with-python_instructions
106 course-introduction
107 machine-learning-in-industry
108 how-companies-use-machine-learning
109 how-companies-are-crafting-the-future_instructions

overview-of-machine-learning
110 machine-learning-process
111 steps-in-machine-learning
112 types-of-machine-learning
113 machine-learning-101_instructions

regression
114 introduction-to-linear-regression
115 real-life-examples
116 calculating-ols
117 equation-of-ols
118 assumptions-in-linear-regression
119 demonstration-setting-up-the-model
120 calculating-r-square-and-rmse
121 residual-plot-and-q-q-plot
122 cooks-distance
123 real-life-examples-of-logistic-regression
124 what-is-logistic-regression
125 cost-function
126 assumptions-in-logistic-regression
127 demonstration-of-logistic-regression-transforming-data
128 demonstration-of-logistic-regression-developing-the-model
129 regression-and-its-assumptions_instructions
130 role-of-regularization_instructions

evaluation-metrics
131 confusion-matrix
132 example-for-calculating-confusion-matrix
133 conditions-for-over-fitting-and-under-fitting
134 overfitting-and-underfitting
135 performance-metrics-mse-rmse-mae-mape
136 r-square-rmsle-and-adjusted-r-square
137 working-of-r-square
138 significance-of-r-square
139 evaluation-of-all-things-predictive_instructions

module-wrap-up-and-assessment
140 summary-for-inception-of-machine-learning

machine-learning-algorithms

decision-tree-and-random-forest
141 classification-in-machine-learning
142 what-is-decision-tree
143 decision-tree-entropy-and-information-gain
144 step-by-step-building-of-decision-tree
145 pruning-in-decision-tree
146 demonstration-importing-data
147 demonstration-building-decision-tree-and-random-forest
148 demonstration-importance-of-features
149 demonstration-production-ready-random-forest
150 demonstration-hyperparameter-tuning
151 decision-trees-and-random-forests_instructions

svm-knn-and-naive-bayes-algorithms
152 what-is-svm
153 terminologies-in-svm
154 hinge-loss-function-and-other-parameters
155 demonstration-of-svm-exploring-the-data
156 demonstration-of-svm-setting-up-the-svm-classifier
157 what-is-naive-bayes
158 working-of-naive-bayes-bayes-theorem
159 example-of-naive-bayes-algorithm
160 demonstration-of-naive-bayes-code
161 working-of-knn
162 example-of-knn-algorithm
163 demonstration-of-knn-setting-up-the-model
164 demonstration-of-knn-transforming-and-scaling-data
165 demonstration-of-knn-creating-classifier
166 svm-knn-and-naive-bayes-when-to-use-which-algorithm_instructions

dimensionality-reduction
167 dimensionality-reduction
168 introduction-to-pca
169 applying-pca
170 eigen-values-and-eigen-vectors
171 demonstration-initializing-pca
172 demonstration-determining-optimal-number-of-components-through-pca
173 demonstration-implementing-optimal-pca
174 working-of-lda
175 demonstration-of-lda
176 best-practices-for-dimensionality-reduction-pca-vs-lda_instructions

module-wrap-up-and-assessment
177 summary-for-machine-learning-algorithms

association-rule-mining-and-recommendation-system

association-rules
178 what-are-association-rules
179 apriori-algorithm
180 demonstrating-apriori-algorithm
181 fp-growth-in-association-rule_instructions

recommendation-engines
182 what-are-recommendation-engine
183 cbf
184 demonstration-of-recommendation-engine-preparing-data
185 demonstration-testing-the-model
186 how-recommendation-engines-personalize-your-world_instructions

reinforcement-learning-and-boosting
187 elements-for-reinforcement-learning
188 demonstration-of-boosting-explaining-the-dataset
189 demonstration-of-boosting-cleaning-and-transforming-dataset
190 demonstration-of-boosting-factors-affecting-promotion
191 demonstration-of-boosting-total-score-and-service-affecting-promotion
192 demonstration-of-boosting-age-previous-year-rating-influencing-promotion
193 demonstration-of-boosting-department-influencing-promotion
194 demonstration-of-boosting-education-affecting-promotion-and-summarization
195 demonstration-of-boosting-modeling-the-data
196 demonstration-of-boosting-building-a-model
197 working-of-k-means-algorithm
198 demonstration-of-k-means-clustering
199 training-models-to-get-better-with-experience_instructions

module-wrap-up-and-assessment
200 summary-for-association-rule-mining-and-recommendation-system

course-wrap-up-and-assessment

untitled-lesson
201 course-summary-for-applied-machine-learning-with-python
202 final-project-cab-booking-demand-analysis_instructions

Resources

module-4-resources
203 resources

module-3-resources
204 resources

module-2-resources
205 resources

module-1-resources
206 resources

practical-deep-learning-with-python

deep-learning-components

environment-set-up-and-configuration
207 welcome-to-practical-deep-learning-with-python_instructions
208 course-introduction
209 environment-configuration
210 system-requirements-and-pre-requisite-for-studying-deep-learning_instructions

essentials-of-deep-learning
211 machine-learning-vs-deep-learning
212 what-is-deep-learning
213 neural-networks
214 artificial-neural-network-ann
215 ann-types-and-applications
216 forward-propagation
217 perceptron
218 learning-rate
219 what-is-activation-function
220 activation-function-and-its-types
221 importance-of-epoch
222 single-layer-perceptron-define-sigmoid-function
223 single-layer-perceptron-decision-boundary
224 learning-rate-in-deep-learning_instructions

building-perceptron-and-its-working
225 limitations-of-single-layered-perceptron
226 multi-layered-perceptron
227 what-is-backpropagation
228 backpropagation
229 demonstration-building-a-simple-neural-network
230 demonstration-understanding-how-backpropagation-has-worked
231 demonstration-handwritten-digits-classification-data-preprocessing
232 demonstration-handwritten-digits-classification-designing-the-model
233 demonstration-handwritten-digits-classification-optimizing-the-model
234 hebbian-learning-algorithm_instructions

module-wrap-up-and-assessment
235 summary-of-deep-learning-components

deep-learning-with-cnn-rcnn-and-faster-rcnn

convolutional-neural-network
236 limitations-of-mlp
237 mlp-limitations-resolving-the-issue-with-cnn
238 visual-cortex-and-cnn
239 convolutional-layer
240 working-of-convolutional-layer
241 demonstration-load-and-preprocess-the-data
242 demonstration-designing-the-model
243 demonstration-building-the-cnn-model
244 demonstration-model-accuracy
245 demonstration-adding-more-layers
246 demonstration-building-basic-cnn-model-with-new-parameters
247 demonstration-pre-trained-model
248 why-convolutions-are-important_instructions

tensorflow-hub-for-object-detection-using-faster-rcnn
249 classification-and-object-detection
250 introduction-to-rcnn
251 r-cnn-bounding-box-regression
252 pre-trained-model
253 fast-regional-cnn
254 demonstration-creating-base-variables-and-loading-the-model
255 demonstration-training-the-model-and-visualizing-the-predictions
256 demonstration-svm-as-a-classifier
257 svm-classifier-in-object-detection_instructions

faster-rcnn-recurrent-convolutional-neural-network
258 fast-rcnn-limitations
259 advent-of-faster-r-cnn
260 tensorflow-hub
261 demonstration-object-detection-with-faster-rcnn-pretrained-model-setup
262 demonstration-object-detection-with-faster-rcnn-building-the-model
263 faster-r-cnn-architecture_instructions

module-wrap-up-and-assessment
264 summary-of-cnn-in-deep-learning
265 summary-of-faster-rcnn

deep-learning-with-rnn-lstm-and-model-optimization

working-of-recurrent-neural-networks-rnn
266 rnn-fundamentals
267 rnn-architecture
268 rnn-architecture-workflow
269 implementing-rnn
270 demonstration-rnn-dataset-preparation
271 demonstration-rnn-building-the-model
272 recurrent-neural-networks-rnns-in-deep-learning_instructions

lstm-architecture
273 basics-of-lstm
274 lstm-structure
275 forget-gate-and-input-gate
276 output-gate
277 importance-of-lstm-architecture
278 types-of-lstm
279 demonstration-next-word-prediction-processing-the-corpus
280 demonstration-next-word-prediction-layers
281 demonstration-next-word-prediction-model-compilation-and-prediction
282 attention-based-lstm-long-short-term-memory_instructions
283 capsule-networks-in-deep-learning_instructions

module-optimization-and-compilation
284 improving-a-model
285 model-optimization
286 using-adam-optimizer
287 model-compilation
288 model-compilation-with-popular-frameworks
289 demonstration-model-compilation-preparing-the-dataset
290 demonstration-building-and-compiling-model
291 demonstration-from-rmsprop-to-adam
292 model-optimizers-beyond-adam_instructions

module-wrap-up-and-assessment
293 summary-of-deep-learning-with-rnn-and-lstm-with-model-optimization

course-wrap-up-and-assessment

untitled-lesson
294 course-summary-for-practical-deep-learning-with-python
295 practice-project-mnist-fashion-dataset-analysis_instructions

Resources

module-3-datasets
296 resources

module-2-datasets
297 resources

python-and-statistics-foundations

python-essentials

programming-with-python
298 specialization-introduction
299 welcome-to-python-and-statistics-foundations-course_instructions
300 course-introduction
301 unlocking-python-the-language-for-every-developer_instructions
302 python-installation_instructions
303 python-installation_miniconda
304 programming-language-and-myths
305 python-for-ai-ml-code-simplicity
306 python-for-ai-ml-ease-of-learning
307 python-tokens-types
308 literals
309 operators-basic-operators
310 operators-membership-and-identity-operators

data-types
311 explaining-data-types-in-python
312 demonstration-of-data-types-numeric-sequence-and-mapping
313 python-variables_instructions
314 python-data-types-numerical-and-strings_instructions
315 python-data-types-tuples-sets-and-dictionaries_instructions

conditional-statements
316 executing-conditional-statement
317 demonstration-of-if-else-statement
318 executing-while-loop
319 demonstration-of-for-loop
320 looping-multiple-conditions

advanced-functions-and-file-handling
321 file-handling
322 file-handling-basics_instructions
323 manipulating-files
324 user-defined-functions
325 variable-argument-and-variable-keyword-argument
326 lambda-functions
327 more-on-functions-and-arguments
328 modules-in-python
329 demonstration-of-modules

module-wrap-up-and-assessment
330 summary-of-python-essentials

exploring-data-with-numpy-and-pandas

numpy-with-python
331 memory-and-performance-comparison-between-python-lists-and-numpy-arrays_instructions
332 exploring-numpy
333 numpy-operation
334 working-with-numpy

data-manipulation-using-pandas
335 data-manipulation-and-analysis-with-python-pandas_instructions
336 pandas-framework
337 pandas-dataframe-operation
338 working-with-dataframes-in-pandas
339 dataframes-with-pandas-operations-and-insights
340 creating-a-series-in-pandas
341 working-with-pandas-series

dataframes-in-action
342 dataframe-data-maniplulation
343 dataframe-joining
344 dataframe-grouping-the-data
345 dataframe-data-cleaning
346 dataframe-data-adjusting

data-visualization
347 data-visualization-fundamentals_instructions
348 introduction-to-matplotlib_instructions
349 matplotlib-library
350 plotting-charts-with-matplotlib
351 plotting-histogram-and-box-plot
352 plotting-multiple-charts
353 introduction-to-seaborn-scatter-plot
354 basic-charts-in-seaborn
355 seaborn-heatmap-manipulation
356 seaborn-flights-dataset
357 seaborn-gaining-insights-in-flight-data
358 visualizing-charts-with-plotly
359 customizing-different-charts-in-plotly

module-wrap-up-and-assessment
360 summary-exploring-numpy-and-pandas-in-python

statistics-in-python

comprehending-statistics
361 the-importance-of-statistics-in-data-interpretation_instructions
362 what-is-statistics
363 measures-of-central-tendency
364 managing-statistical-data
365 exploring-and-analyzing-data

statistical-dispersion
366 measures-of-dispersion
367 dispersion-demonstration
368 application-of-dispersion-in-large-data

probability-foundation
369 introduction-to-probability_instructions
370 probability-sample-space-and-events
371 need-for-probability
372 types-of-probability
373 marginal-and-joint-probability-demonstration
374 conditional-probability-demonstration

hypothesis-testing
375 introduction-to-hypothesis-testing_instructions
376 what-is-hypothesis-testing
377 steps-for-hypothesis-testing
378 null-and-alternate-hypothesis
379 statistical-test-interpretation
380 one-tailed-and-two-tailed-test
381 hypothesis-testing-demonstration
382 margin-of-error-and-confidence-interval-demonstration

module-wrap-up-and-assessment
383 summary-statistical-analysis-with-python

course-wrap-up-and-assessment

untitled-lesson
384 course-summary-python-and-statistics-foundations
385 practice-project-travel-aggregator-analysis_instructions

Resources

module-4-dataset
386 resources

module-3-dataset
387 resources

module-2-datasets
388 resources