2025 Data Science & AI Masters From Python To Gen AI ~ Udemy - Satyajit Pattnaik
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2025 Data Science & AI Masters From Python To Gen AI ~ Udemy - Satyajit Pattnaik
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2025 Data Science & AI Masters From Python To Gen AI ~ Udemy - Satyajit Pattnaik
02. - Python+Installation+Guide.pdf -
10 - Power+BI+Ebook.pdf -
10 - RAG+with+GrokAI.ipynb -
10 - RAG+with+Ollama.ipynb -
10 - RAGPaper (1).pdf -
10 - RAGPaper.pdf -
1 Welcome Page.mp4 -
10 Datatypes Operators.mp4 -
11 Lists.mp4 -
12 Tuples.mp4 -
13 Sets.mp4 -
14 Dictionary.mp4 -
15 Loops & Iterations.mp4 -
16 Functions.mp4 -
17 Map Reduce Filter.mp4 -
18 File Handling.mp4 -
19 Control Structures.mp4 -
20 OOPs.mp4 -
21 NumPy.mp4 -
22 Pandas.mp4 -
23 Data Visualization.mp4 -
24 Matplotlib.mp4 -
25 Seaborn.mp4 -
5 Let's install Python together!!.mp4 -
6 Google Colab, what's that.mp4 -
7 Let's leverage chatGPT for help!!.mp4 -
8 Introduction to Python.mp4 -
9 Variables & Keywords.mp4 -
27 Introduction.mp4 -
28 Types of Data (Agenda).mp4 -
29 Descriptive Stats.mp4 -
30 Inferential Stats.mp4 -
31 Qualitative Data.mp4 -
32 Quantitative Data.mp4 -
33 Sampling Techniques (Agenda).mp4 -
34 Population vs Sample.mp4 -
35 Why Sampling is important.mp4 -
36 Types of Sampling.mp4 -
37 Cluster Random Sampling.mp4 -
38 Probability Sampling.mp4 -
39 Non probability sampling.mp4 -
40 Population Sampling.mp4 -
41 Why n-1 and not n.mp4 -
42 Descriptive Analytics (Agenda).mp4 -
43 Measures of Central Tendency.mp4 -
44 Mean.mp4 -
45 Median.mp4 -
46 Mode.mp4 -
47 Measures of Dispersion.mp4 -
48 Range.mp4 -
49 IQR.mp4 -
50 Variance Standard Deviation.mp4 -
51 Mean Deviation.mp4 -
52 Probability (Agenda).mp4 -
53 Probability.mp4 -
54 Addition Rule.mp4 -
55 Independent Events.mp4 -
56 Cumulative Probability.mp4 -
57 Conditional Probability.mp4 -
58 Bayes Theorem 1.mp4 -
59 Bayes Theorem 2.mp4 -
60 Probability Distrubution (Agenda).mp4 -
61 Uniform Distribution.mp4 -
62 Binomial Distribution.mp4 -
63 Poisson Distribution.mp4 -
64 Normal Distribution Part 1.mp4 -
65 Normal Distribution Part 2.mp4 -
66 Skewness.mp4 -
67 Kurtosis.mp4 -
68 Calc Prob w Z-score - Normal Distrib Pt 1.mp4 -
69 Calc Prob w Z-score - Normal Distrib Pt 2.mp4 -
70 Calc Prob w Z-score - Normal Distrib Pt 3.mp4 -
71 Covariance & Correlation (Agenda).mp4 -
72 Covariance.mp4 -
73 Correlation.mp4 -
74 Covariance VS Correlation.mp4 -
75 Hypothesis Testing.mp4 -
76 Tailed Tests.mp4 -
77 p-value.mp4 -
78 Types of Test.mp4 -
79 T Test.mp4 -
80 Z Test.mp4 -
81 Chi Square Test.mp4 -
82 ANOVA.mp4 -
83 Correlation Test (Practicals).mp4 -
100 Types of Data.mp4 -
101 Types of Analysis.mp4 -
102 Univariate Analysis.mp4 -
103 Bivariate Analysis.mp4 -
104 Multivariate Analysis.mp4 -
105 Numerical Analysis.mp4 -
106 Analysis Practicals.mp4 -
107 Derived Metrics.mp4 -
108 Feature Binning (Theory).mp4 -
109 Feature Binning (Practicals).mp4 -
110 Feature Encoding (Theory).mp4 -
111 Feature Encoding (Practicals).mp4 -
112 Case Study.mp4 -
113 Data Exploration.mp4 -
114 Data Cleaning.mp4 -
115 Univariate Analysis.mp4 -
116 Bivariate Analysis Part 1.mp4 -
117 Bivariate Analysis Part 2.mp4 -
118 EDA Report.mp4 -
85 Agenda.mp4 -
86 DA,DS Processes.mp4 -
87 What is EDA.mp4 -
88 Visualization.mp4 -
89 Steps involved in EDA (Data Sourcing).mp4 -
90 Steps involved in EDA (Data Cleaning).mp4 -
91 Handle Missing Values (Theory).mp4 -
92 Handle Missing Values (Practicals).mp4 -
93 Feature Scaling (Theory).mp4 -
94 Standardization Example.mp4 -
95 Normalization Example.mp4 -
96 Feature Scaling (Practicals).mp4 -
97 Outlier Treatment (Theory).mp4 -
98 Outlier Treatment (Practicals).mp4 -
99 Invalid Data.mp4 -
120 Installation.mp4 -
121 Data Architect - File server vs client server.mp4 -
122 Introduction to SQL.mp4 -
123 Constraints in SQL.mp4 -
124 Table Basics - DDLs.mp4 -
125 Table Basics - DQLs.mp4 -
126 Table Basics - DMLs.mp4 -
127 Joins.mp4 -
128 Data Import Export.mp4 -
129 Aggregation Functions.mp4 -
130 String functions.mp4 -
131 Date Time Functions.mp4 -
132 Regular Expressions.mp4 -
133 Nested Queries.mp4 -
134 Views.mp4 -
135 Stored Procedures.mp4 -
136 Windows Function.mp4 -
137 SQL Python connectivity.mp4 -
138 Agenda.mp4 -
139 Introduction to ML.mp4 -
140 Types of ML.mp4 -
141 Use Cases Part 1.mp4 -
142 Use Cases Part 2.mp4 -
143 Pre-Requisites Features.mp4 -
144 Pre-Requisites Train-Test Split.mp4 -
145 Pre-Requisites Feature Scaling.mp4 -
146 Pre-Requisites Standardization Example.mp4 -
147 Pre-Requisites Normalization Example.mp4 -
148 Pre-Requisites Feature Encoding.mp4 -
149 Pre-Req Feature Encoding (Practicals).mp4 -
150 Regression Intro to Regression Models.mp4 -
151 Regression Regression Metrics.mp4 -
152 Regression Regression Metrics (Practicals).mp4 -
153 Regression Simple Linear Regression.mp4 -
154 Regression Multiple Linear Regression.mp4 -
155 Regression Linear Regression (Practicals).mp4 -
156 Regress Multi Linear Regress (Practicals).mp4 -
157 Regression Polynomial Regression.mp4 -
158 Regression Polynomial Regress (Practicals).mp4 -
159 Regression Bias Variance Tradeoff.mp4 -
160 Regression Ridge Regression.mp4 -
161 Regression Lasso Regression.mp4 -
162 Regress Lasso, Ridge Regress (Practicals).mp4 -
163 Classification Intro to Classification.mp4 -
164 Classification Types of Classification.mp4 -
165 Classification Log Loss.mp4 -
166 Classification Confusion Matrix.mp4 -
167 Classification AUC ROC Curve.mp4 -
168 Classification Classification Report.mp4 -
169 Classification kNN Classifier.mp4 -
170 Classification kNN Classifier Example.mp4 -
171 Classification Practicals Part 1.mp4 -
172 Classification kNN Classifier (Practicals).mp4 -
173 Classification Decision Tree.mp4 -
174 Class.. Decision Tree (Entropy based).mp4 -
175 Classification Decision Tree (gini based).mp4 -
176 Classification Decision Tree (Practicals).mp4 -
177 Classification Decision Tree (Visualizing).mp4 -
178 Classification Random Forest Classifier.mp4 -
179 Class.. Random Forest Classifier (Practs).mp4 -
180 Classification Naive Bayes Classifier.mp4 -
181 Classification SVM Classifier Part 1.mp4 -
182 Classification SVM Classifier Part 2.mp4 -
183 Classification Logistic Regression.mp4 -
184 Classification Practicals so far.mp4 -
185 Class.. Issues in Classification (Part 1).mp4 -
186 Class.. Issues in Classification (Part 2).mp4 -
187 Classification Project.mp4 -
188 Ensemble Intro to Ensemble Learning.mp4 -
189 Ensemble Bagging.mp4 -
190 Ensemble Bagging vs Random Forest.mp4 -
191 Ensemble Bagging (Practicals #1).mp4 -
192 Ensemble Bagging (Practicals #2).mp4 -
193 Ensemble Boosting.mp4 -
194 Ensemble Ada Boost.mp4 -
195 Ensemble Gradient Boost.mp4 -
196 Ensemble CF vs LF.mp4 -
197 Ensemble Cross Entropy.mp4 -
198 Ensemble Xtreme Gradient Boosting (XGB).mp4 -
199 Ensemble Project.mp4 -
200 Clustering Introduction to Clustering.mp4 -
201 Clustering kMeans Clustering.mp4 -
202 Clustering kMeans Clustering (Practicals).mp4 -
203 Clustering Hierarchical Clustering.mp4 -
204 Clustering Hierarchy Cluster (Practicals).mp4 -
205 Clustering Mean Shift Clustering.mp4 -
206 Feature Engineering Introduction.mp4 -
207 Feature Engineering RFE and SFS.mp4 -
208 Feature Engineering RFE (Practicals).mp4 -
209 Feature Eng.. Successive Feature Selection.mp4 -
210 Feature Engineering Chi-Square.mp4 -
211 Feature Eng.. Chi-Square (Practicals).mp4 -
212 Feat Eng Principal Component Analysis.mp4 -
213 Feat Eng Principal Component Analy (Practls).mp4 -
214 Feat Eng Linear Discriminant Analysis.mp4 -
215 Feat Eng Linear Discriminant Analysis (Practls).mp4 -
216 Feature Engineering kPCA & QDA.mp4 -
217 Feature Engineering kPCA & QDA (Practicals).mp4 -
218 Hyper Parameter Optimization (HPO) Basics.mp4 -
219 Hyper Parameter Optimization Manual HPO.mp4 -
220 HPO GridSearch vs RandomizedSearch.mp4 -
221 HPO Manual HPO (Practicals).mp4 -
222 HPO RandomizedSearchCV (Practicals).mp4 -
223 HPO GridSearchCV (Practicals).mp4 -
224 Introduction to TSA.mp4 -
225 Time Series vs Regression.mp4 -
226 Time Series Analysis.mp4 -
227 Anomaly Detection.mp4 -
228 Components of Time Series.mp4 -
229 Decomposition.mp4 -
230 Decomposition (Practicals).mp4 -
231 AdditiveMultiplicative Decomp.mp4 -
232 Stationarity.mp4 -
233 Testing TS Stationarity.mp4 -
234 Transformation.mp4 -
235 Introduction to Pre-Processing.mp4 -
236 Handle Missing Value.mp4 -
237 Handle Missing Value (Practicals).mp4 -
238 Outlier Treatment.mp4 -
239 3-Sigma Technique.mp4 -
240 Feature Scaling.mp4 -
241 Feature Scaling Standardization.mp4 -
242 Feature Scaling Normalization.mp4 -
243 Feature Scaling (Practicals).mp4 -
244 Feature Encoding.mp4 -
245 Feature Encoding (Practicals).mp4 -
246 Models - Algorithms.mp4 -
247 Models - ARIMA Part 1.mp4 -
248 Models - ARIMA Part 2.mp4 -
249 Models - AR Theory.mp4 -
250 Models - MA Theory.mp4 -
251 Models - ACFPACF Plots.mp4 -
252 Models - Find p,d,q in ARIMA.mp4 -
253 Models - ARIMA (Practicals Part 1).mp4 -
254 Models - ARIMA (Practicals Part 2).mp4 -
255 Models - ARIMA (Final).mp4 -
256 Models - Decomposition.mp4 -
257 Models - ACFPACF.mp4 -
258 Models - Best Transformation.mp4 -
259 Models - Grid Search (Part 1).mp4 -
260 Models - Grid Search (Part 2).mp4 -
261 Models - Final Model Building.mp4 -
262 Models - Facebook Prophet (Part 1).mp4 -
263 Models - Facebook Prophet (Part 2).mp4 -
264 Models - Facebook Prophet (Part 3).mp4 -
265 Mods - Multi Variate Time Series Analy.mp4 -
266 Mods - Facebook Prophet Uni v Multi.mp4 -
267 Introduction to Metrics.mp4 -
268 Forecasting Evaluation Metrics.mp4 -
269 Mean Squarred Error.mp4 -
270 Root Mean Squarred Error.mp4 -
271 Mean Absolute Percentage Error.mp4 -
272 Proj 1 - Energy Forecasting Part 1.mp4 -
273 Proj 1 - Energy Forecasting Part 2.mp4 -
274 Proj 1 - Energy Forecasting Part 3.mp4 -
275 Proj 2 - Stock Market Prediction Pt 1.mp4 -
276 Proj 2 - Stock Market Prediction Pt 2.mp4 -
277 Proj 2 - Stock Market Prediction Pt 3.mp4 -
278 Proj 3 - Demand Forecasting Part 1.mp4 -
279 Proj 3 - Demand Forecasting Part 2.mp4 -
280 Proj 3 - Demand Forecasting Part 3.mp4 -
281 Proj 3 - Demand Forecasting Part 4.mp4 -
282 Proj 3 - Demand Forecasting Part 5.mp4 -
283 Proj 3 - Demand Forecasting Part 6.mp4 -
285 Introduction to Deep Learning.mp4 -
286 Understanding Deep Learning.mp4 -
287 What is a Neuron.mp4 -
288 Activation Functions.mp4 -
289 Activation Function Step Function.mp4 -
290 Activation Function Linear Function.mp4 -
291 Activation Function Sigmoid Function.mp4 -
292 Activation Function TanH Function.mp4 -
293 Activation Function ReLu Function.mp4 -
294 Backpropagation & Forward Pass.mp4 -
295 Gradient Descent.mp4 -
296 Artificial Neural Networks Intuition.mp4 -
297 Artificial Neural Networks Practicals.mp4 -
298 Artificial NN Hyper Param Optimize.mp4 -
299 Convolutional Neural Networks (CNN).mp4 -
300 CNN Steps in CNN.mp4 -
301 CNN Architecture Explained.mp4 -
302 CNN Image Augmentation.mp4 -
303 CNN Batch size vs iterations vs epochs.mp4 -
304 CNN Practicals.mp4 -
305 CNN Model Summary & Parameters.mp4 -
306 CNN Project (X-Ray detection).mp4 -
307 Recurrent Neural Networks (RNN) Basics.mp4 -
308 RNN Types of RNN.mp4 -
309 RNN Vanishing, Exploding Gradient Prob.mp4 -
310 RNN LSTMs.mp4 -
311 RNN LSTMs (Practicals).mp4 -
312 Pre-Trained Models.mp4 -
313 Pre-Trained Models (Practicals).mp4 -
314 Pre-Trained Models VGG16.mp4 -
315 Pre-Trained Models MobileNet.mp4 -
316 Transfer Learning.mp4 -
317 Proj Pneumonia Detection X-Ray Img.mp4 -
319 Intro to NLP Introduction.mp4 -
320 Intro to NLP Introduction continued.mp4 -
321 Intro to NLP Key Challenges.mp4 -
322 Intro to NLP Linguistics.mp4 -
323 NLP Basics Case Folding.mp4 -
324 NLP Basics SCR.mp4 -
325 NLP Basics Handling Contractions.mp4 -
326 NLP Basics Tokenization.mp4 -
327 NLP Basics Stop Word Removal.mp4 -
328 NLP Basics nGrams.mp4 -
329 NLP Basics Vectorization.mp4 -
330 NLP Basics Word Embeddings.mp4 -
331 NLP Basics Bag of Words.mp4 -
332 NLP Basics Bag of Words (Practicals).mp4 -
333 NLP Basics TF-IDF.mp4 -
334 NLP Basics TF-IDF (Practicals).mp4 -
335 NLP Part of Speech Tag, Named Entity Recog.mp4 -
336 NLP Basics NER (Practicals).mp4 -
337 Word Embeddings Word2Vec Introduction.mp4 -
338 Word Embeddings Word2Vec Part 2.mp4 -
339 Word Embeddings Pre-Trained Word2Vec.mp4 -
340 Word Embeddings Word2Vec Intuition.mp4 -
341 Word Embed Word2Vec - Check X Features.mp4 -
342 Word Embeddings Word2Vec CBOW.mp4 -
343 Word Embed Word2Vec Skip Grams.mp4 -
344 Word Embeddings GloVe.mp4 -
345 Word Embeddings FastText.mp4 -
346 Word Embeddings Cosine Similarity.mp4 -
347 Neural Networks (NN) LSTMs Part 1.mp4 -
348 NN LSTMs Part 2 (Architecture).mp4 -
349 NN LSTMs Part 3 (Deep Dive).mp4 -
350 NN LSTMs Part 4 Pointwise Operation.mp4 -
351 NN LSTMs Part 5 (forget gate).mp4 -
352 NN LSTMs Part 6 (inpute gate).mp4 -
353 NN LSTMs Part 7 (output gate).mp4 -
354 NN LSTMs Part 8 (Practicals #1).mp4 -
355 NN LSTMs Part 9 (Practicals #2).mp4 -
356 NN LSTMs Part 10 (Practicals #3).mp4 -
357 NN GRU Part 1.mp4 -
358 NN GRU Part 2.mp4 -
359 NN GRU Part 3 (reset gate).mp4 -
360 NN GRU Part 4 (update gate).mp4 -
361 NN GRU Part 5 (Practicals).mp4 -
362 NN Bi-Directional LSTMs.mp4 -
364 Transformer Types.mp4 -
365 Introduction to Transformers.mp4 -
366 Self Attention.mp4 -
367 Encoder Architecture.mp4 -
368 Contextual Embeddings.mp4 -
369 Decoder Architecture.mp4 -
370 Introduction to BERT.mp4 -
371 Configurations of BERT.mp4 -
372 BERT Fine Tuning.mp4 -
373 BERT Pre Tuning (Masked LM).mp4 -
374 BERT Input Embeddings.mp4 -
375 ARLM vs AELM.mp4 -
376 RoBERTa.mp4 -
377 DistilBERT.mp4 -
378 AlBERT.mp4 -
379 Introduction to GPT (Decoder Only).mp4 -
380 GPT Architecture.mp4 -
381 GPT Masked Multi Head Attention.mp4 -
382 GPT Blocks.mp4 -
383 GPT Training.mp4 -
385 LLM Basics Context Window.mp4 -
386 LLM Basics Prompt.mp4 -
387 LLM Basics Prompt Engineering.mp4 -
388 LLM Basics Prompt Tuning.mp4 -
389 LLM Basics Prompt Structures.mp4 -
390 RAGs Introduction to RAG.mp4 -
391 RAGs What and Why.mp4 -
392 RAGs Use Cases.mp4 -
393 RAGs Paper Explanation.mp4 -
394 RAGs Architecture Explanation.mp4 -
395 RAGs Detailed Architect Walk-thru.mp4 -
396 RAGs Practical Use Cases.mp4 -
397 LangChain.mp4 -
398 Intro Prompt Engineering.mp4 -
399 Types of Prompting.mp4 -
400 Few Shot Limitations.mp4 -
401 Chain of Thoughts Prompting.mp4 -
402 Vector Databases.mp4 -
403 Vector Database vs Vector Index.mp4 -
404 How Vector Databases works.mp4 -
405 Vector Database (Practicals).mp4 -
406 LSH.mp4 -
407 Model Overview Ollama.mp4 -
408 Getting Started Ollama.mp4 -
409 Model Testing Ollama.mp4 -
410 Python Implementation Ollama.mp4 -
411 RAG Systems Ollama.mp4 -
412 RAG Systems (Practicals) Ollama.mp4 -
413 Model Overview LLM APIs.mp4 -
414 RAG Systems with xAI LLM APIs.mp4 -
415 RAG Sys w xAI (Practicals) LLM APIs.mp4 -
416 Deployment Basics.mp4 -
417 Introduction to Flask.mp4 -
418 Flask Basic App.mp4 -
419 Model Building (Breast Cancer Predict).mp4 -
420 Flask App (Breast Cancer Prediction).mp4 -
421 AWS.mp4 -
422 AWS Deploy (Breast Cancer Predict).mp4 -
423 Introduction to Data Engineering.mp4 -
424 What is ETL.mp4 -
425 ETL Tools.mp4 -
426 What is Data Warehouse.mp4 -
427 Benefits of Data Warehouse.mp4 -
428 Data Warehouse Structure.mp4 -
429 Why do we need Staging.mp4 -
430 What are Data Marts.mp4 -
431 Data Lake.mp4 -
432 Data lake vs Data Warehouse.mp4 -
433 Elements of Datalake.mp4 -
434 ChatScholar (EdTech Project).mp4 -
435 Research RAG Chatbot.mp4 -
436 Auto AI Claims Processing - Gen AI.mp4 -
437 PDF RAG(s) Chatbot Web Scrape Data.mp4 -
438 AI Career Coach Part 1.mp4 -
439 AI Career Coach Part 2.mp4 -
440 AI Career Coach Part 3.mp4 -
441 Sustainability Chatbot (GROK AI).mp4 -
442 ML Interview Prep.mp4 -
443 ML Interview #1.mp4 -
444 ML Interview #2.mp4 -
445 ML Interview #3.mp4 -
446 ML Interview #4.mp4 -
447 ML Interview #5.mp4 -
448 ML Interview #6.mp4 -
449 ML Interview #7.mp4 -
450 ML Interview #8.mp4 -
451 ML Interview #9.mp4 -
452 ML Interview #10.mp4 -
453 DL Interview #1.mp4 -
454 DL Interview #2.mp4 -
455 DL Interview #3.mp4 -
456 DL Interview #4.mp4 -
457 DL Interview #5.mp4 -
458 DL Interview #6.mp4 -
459 DL Interview #7.mp4 -
460 DL Interview #8.mp4 -
461 DL Interview #9.mp4 -
462 DL Interview #10.mp4 -
463 Gen AI Interview #1.mp4 -
464 Gen AI Interview #2.mp4 -
465 Gen AI Interview #3.mp4 -
466 Gen AI Interview #4.mp4 -
467 Gen AI Interview #5.mp4 -
468 Gen AI Interview #6.mp4 -
469 Gen AI Interview #7.mp4 -
470 Gen AI Interview #8.mp4 -
471 Gen AI Interview #9.mp4 -
472 Gen AI Interview #10.mp4 -
473 Gen AI Interview #2.mp4 -
474 Gen AI Interview #3.mp4 -
475 Gen AI Interview #4.mp4 -
476 Gen AI Interview #5.mp4 -
477 Gen AI Interview #6.mp4 -
478 Gen AI Interview #7.mp4 -
479 Gen AI Interview #8.mp4 -
480 Gen AI Interview #9.mp4 -
481 Gen AI Interview #10.mp4 -
2025 Data Science & AI Masters From Python To Gen AI ~ Udemy - Satyajit Pattnaiko.txt -
Please login or create a FREE account to post comments
02. - Python+Installation+Guide.pdf -
842.0 KB
10 - Power+BI+Ebook.pdf -
14.4 MB
10 - RAG+with+GrokAI.ipynb -
20.0 KB
10 - RAG+with+Ollama.ipynb -
10.0 KB
10 - RAGPaper (1).pdf -
864.6 KB
10 - RAGPaper.pdf -
864.6 KB
1 Welcome Page.mp4 -
51.5 MB
10 Datatypes Operators.mp4 -
364.2 MB
11 Lists.mp4 -
465.6 MB
12 Tuples.mp4 -
424.2 MB
13 Sets.mp4 -
220.6 MB
14 Dictionary.mp4 -
297.3 MB
15 Loops & Iterations.mp4 -
336.2 MB
16 Functions.mp4 -
393.9 MB
17 Map Reduce Filter.mp4 -
514.8 MB
18 File Handling.mp4 -
327.3 MB
19 Control Structures.mp4 -
171.9 MB
20 OOPs.mp4 -
335.2 MB
21 NumPy.mp4 -
485.4 MB
22 Pandas.mp4 -
567.4 MB
23 Data Visualization.mp4 -
113.4 MB
24 Matplotlib.mp4 -
449.7 MB
25 Seaborn.mp4 -
325.2 MB
5 Let's install Python together!!.mp4 -
273.2 MB
6 Google Colab, what's that.mp4 -
51.4 MB
7 Let's leverage chatGPT for help!!.mp4 -
70.8 MB
8 Introduction to Python.mp4 -
94.2 MB
9 Variables & Keywords.mp4 -
352.3 MB
27 Introduction.mp4 -
42.3 MB
28 Types of Data (Agenda).mp4 -
3.2 MB
29 Descriptive Stats.mp4 -
79.8 MB
30 Inferential Stats.mp4 -
13.4 MB
31 Qualitative Data.mp4 -
50.0 MB
32 Quantitative Data.mp4 -
20.5 MB
33 Sampling Techniques (Agenda).mp4 -
7.6 MB
34 Population vs Sample.mp4 -
17.9 MB
35 Why Sampling is important.mp4 -
17.0 MB
36 Types of Sampling.mp4 -
20.7 MB
37 Cluster Random Sampling.mp4 -
30.6 MB
38 Probability Sampling.mp4 -
40.7 MB
39 Non probability sampling.mp4 -
31.4 MB
40 Population Sampling.mp4 -
56.4 MB
41 Why n-1 and not n.mp4 -
32.8 MB
42 Descriptive Analytics (Agenda).mp4 -
5.0 MB
43 Measures of Central Tendency.mp4 -
9.1 MB
44 Mean.mp4 -
27.0 MB
45 Median.mp4 -
36.5 MB
46 Mode.mp4 -
28.1 MB
47 Measures of Dispersion.mp4 -
21.6 MB
48 Range.mp4 -
7.3 MB
49 IQR.mp4 -
19.5 MB
50 Variance Standard Deviation.mp4 -
56.4 MB
51 Mean Deviation.mp4 -
18.9 MB
52 Probability (Agenda).mp4 -
4.7 MB
53 Probability.mp4 -
42.0 MB
54 Addition Rule.mp4 -
45.4 MB
55 Independent Events.mp4 -
26.0 MB
56 Cumulative Probability.mp4 -
29.4 MB
57 Conditional Probability.mp4 -
57.9 MB
58 Bayes Theorem 1.mp4 -
9.5 MB
59 Bayes Theorem 2.mp4 -
25.0 MB
60 Probability Distrubution (Agenda).mp4 -
10.4 MB
61 Uniform Distribution.mp4 -
44.1 MB
62 Binomial Distribution.mp4 -
70.8 MB
63 Poisson Distribution.mp4 -
18.8 MB
64 Normal Distribution Part 1.mp4 -
77.5 MB
65 Normal Distribution Part 2.mp4 -
34.3 MB
66 Skewness.mp4 -
25.2 MB
67 Kurtosis.mp4 -
14.1 MB
68 Calc Prob w Z-score - Normal Distrib Pt 1.mp4 -
49.6 MB
69 Calc Prob w Z-score - Normal Distrib Pt 2.mp4 -
47.0 MB
70 Calc Prob w Z-score - Normal Distrib Pt 3.mp4 -
27.0 MB
71 Covariance & Correlation (Agenda).mp4 -
2.7 MB
72 Covariance.mp4 -
54.4 MB
73 Correlation.mp4 -
86.6 MB
74 Covariance VS Correlation.mp4 -
26.6 MB
75 Hypothesis Testing.mp4 -
52.9 MB
76 Tailed Tests.mp4 -
16.8 MB
77 p-value.mp4 -
32.0 MB
78 Types of Test.mp4 -
26.9 MB
79 T Test.mp4 -
51.3 MB
80 Z Test.mp4 -
67.4 MB
81 Chi Square Test.mp4 -
67.3 MB
82 ANOVA.mp4 -
68.9 MB
83 Correlation Test (Practicals).mp4 -
44.6 MB
100 Types of Data.mp4 -
11.0 MB
101 Types of Analysis.mp4 -
12.0 MB
102 Univariate Analysis.mp4 -
54.1 MB
103 Bivariate Analysis.mp4 -
35.7 MB
104 Multivariate Analysis.mp4 -
5.2 MB
105 Numerical Analysis.mp4 -
19.4 MB
106 Analysis Practicals.mp4 -
211.5 MB
107 Derived Metrics.mp4 -
26.5 MB
108 Feature Binning (Theory).mp4 -
44.2 MB
109 Feature Binning (Practicals).mp4 -
71.0 MB
110 Feature Encoding (Theory).mp4 -
83.8 MB
111 Feature Encoding (Practicals).mp4 -
169.8 MB
112 Case Study.mp4 -
79.6 MB
113 Data Exploration.mp4 -
151.4 MB
114 Data Cleaning.mp4 -
73.5 MB
115 Univariate Analysis.mp4 -
97.6 MB
116 Bivariate Analysis Part 1.mp4 -
129.3 MB
117 Bivariate Analysis Part 2.mp4 -
54.5 MB
118 EDA Report.mp4 -
35.8 MB
85 Agenda.mp4 -
15.2 MB
86 DA,DS Processes.mp4 -
24.3 MB
87 What is EDA.mp4 -
27.1 MB
88 Visualization.mp4 -
30.5 MB
89 Steps involved in EDA (Data Sourcing).mp4 -
28.8 MB
90 Steps involved in EDA (Data Cleaning).mp4 -
30.4 MB
91 Handle Missing Values (Theory).mp4 -
58.5 MB
92 Handle Missing Values (Practicals).mp4 -
92.1 MB
93 Feature Scaling (Theory).mp4 -
74.2 MB
94 Standardization Example.mp4 -
22.7 MB
95 Normalization Example.mp4 -
13.9 MB
96 Feature Scaling (Practicals).mp4 -
111.6 MB
97 Outlier Treatment (Theory).mp4 -
59.8 MB
98 Outlier Treatment (Practicals).mp4 -
102.7 MB
99 Invalid Data.mp4 -
42.5 MB
120 Installation.mp4 -
59.1 MB
121 Data Architect - File server vs client server.mp4 -
119.3 MB
122 Introduction to SQL.mp4 -
164.5 MB
123 Constraints in SQL.mp4 -
289.4 MB
124 Table Basics - DDLs.mp4 -
396.5 MB
125 Table Basics - DQLs.mp4 -
290.0 MB
126 Table Basics - DMLs.mp4 -
461.6 MB
127 Joins.mp4 -
448.4 MB
128 Data Import Export.mp4 -
545.6 MB
129 Aggregation Functions.mp4 -
211.2 MB
130 String functions.mp4 -
287.9 MB
131 Date Time Functions.mp4 -
233.9 MB
132 Regular Expressions.mp4 -
160.7 MB
133 Nested Queries.mp4 -
263.5 MB
134 Views.mp4 -
222.8 MB
135 Stored Procedures.mp4 -
439.1 MB
136 Windows Function.mp4 -
365.8 MB
137 SQL Python connectivity.mp4 -
341.3 MB
138 Agenda.mp4 -
13.7 MB
139 Introduction to ML.mp4 -
32.2 MB
140 Types of ML.mp4 -
104.9 MB
141 Use Cases Part 1.mp4 -
19.9 MB
142 Use Cases Part 2.mp4 -
8.0 MB
143 Pre-Requisites Features.mp4 -
88.7 MB
144 Pre-Requisites Train-Test Split.mp4 -
115.8 MB
145 Pre-Requisites Feature Scaling.mp4 -
74.2 MB
146 Pre-Requisites Standardization Example.mp4 -
22.7 MB
147 Pre-Requisites Normalization Example.mp4 -
13.9 MB
148 Pre-Requisites Feature Encoding.mp4 -
83.8 MB
149 Pre-Req Feature Encoding (Practicals).mp4 -
78.8 MB
150 Regression Intro to Regression Models.mp4 -
38.9 MB
151 Regression Regression Metrics.mp4 -
151.4 MB
152 Regression Regression Metrics (Practicals).mp4 -
102.6 MB
153 Regression Simple Linear Regression.mp4 -
55.9 MB
154 Regression Multiple Linear Regression.mp4 -
51.5 MB
155 Regression Linear Regression (Practicals).mp4 -
210.8 MB
156 Regress Multi Linear Regress (Practicals).mp4 -
96.2 MB
157 Regression Polynomial Regression.mp4 -
39.2 MB
158 Regression Polynomial Regress (Practicals).mp4 -
176.6 MB
159 Regression Bias Variance Tradeoff.mp4 -
31.0 MB
160 Regression Ridge Regression.mp4 -
55.2 MB
161 Regression Lasso Regression.mp4 -
43.9 MB
162 Regress Lasso, Ridge Regress (Practicals).mp4 -
335.4 MB
163 Classification Intro to Classification.mp4 -
41.3 MB
164 Classification Types of Classification.mp4 -
25.7 MB
165 Classification Log Loss.mp4 -
63.6 MB
166 Classification Confusion Matrix.mp4 -
72.8 MB
167 Classification AUC ROC Curve.mp4 -
48.6 MB
168 Classification Classification Report.mp4 -
47.2 MB
169 Classification kNN Classifier.mp4 -
80.8 MB
170 Classification kNN Classifier Example.mp4 -
78.0 MB
171 Classification Practicals Part 1.mp4 -
100.5 MB
172 Classification kNN Classifier (Practicals).mp4 -
115.4 MB
173 Classification Decision Tree.mp4 -
73.0 MB
174 Class.. Decision Tree (Entropy based).mp4 -
112.5 MB
175 Classification Decision Tree (gini based).mp4 -
104.2 MB
176 Classification Decision Tree (Practicals).mp4 -
66.5 MB
177 Classification Decision Tree (Visualizing).mp4 -
160.3 MB
178 Classification Random Forest Classifier.mp4 -
40.9 MB
179 Class.. Random Forest Classifier (Practs).mp4 -
46.7 MB
180 Classification Naive Bayes Classifier.mp4 -
89.9 MB
181 Classification SVM Classifier Part 1.mp4 -
71.8 MB
182 Classification SVM Classifier Part 2.mp4 -
60.2 MB
183 Classification Logistic Regression.mp4 -
119.2 MB
184 Classification Practicals so far.mp4 -
218.7 MB
185 Class.. Issues in Classification (Part 1).mp4 -
48.4 MB
186 Class.. Issues in Classification (Part 2).mp4 -
80.2 MB
187 Classification Project.mp4 -
308.9 MB
188 Ensemble Intro to Ensemble Learning.mp4 -
117.9 MB
189 Ensemble Bagging.mp4 -
50.6 MB
190 Ensemble Bagging vs Random Forest.mp4 -
91.1 MB
191 Ensemble Bagging (Practicals #1).mp4 -
241.2 MB
192 Ensemble Bagging (Practicals #2).mp4 -
178.1 MB
193 Ensemble Boosting.mp4 -
41.9 MB
194 Ensemble Ada Boost.mp4 -
97.9 MB
195 Ensemble Gradient Boost.mp4 -
20.8 MB
196 Ensemble CF vs LF.mp4 -
47.1 MB
197 Ensemble Cross Entropy.mp4 -
22.1 MB
198 Ensemble Xtreme Gradient Boosting (XGB).mp4 -
94.4 MB
199 Ensemble Project.mp4 -
210.6 MB
200 Clustering Introduction to Clustering.mp4 -
104.0 MB
201 Clustering kMeans Clustering.mp4 -
121.0 MB
202 Clustering kMeans Clustering (Practicals).mp4 -
133.4 MB
203 Clustering Hierarchical Clustering.mp4 -
81.9 MB
204 Clustering Hierarchy Cluster (Practicals).mp4 -
106.5 MB
205 Clustering Mean Shift Clustering.mp4 -
73.3 MB
206 Feature Engineering Introduction.mp4 -
87.4 MB
207 Feature Engineering RFE and SFS.mp4 -
29.0 MB
208 Feature Engineering RFE (Practicals).mp4 -
190.9 MB
209 Feature Eng.. Successive Feature Selection.mp4 -
180.1 MB
210 Feature Engineering Chi-Square.mp4 -
31.7 MB
211 Feature Eng.. Chi-Square (Practicals).mp4 -
54.6 MB
212 Feat Eng Principal Component Analysis.mp4 -
258.2 MB
213 Feat Eng Principal Component Analy (Practls).mp4 -
79.7 MB
214 Feat Eng Linear Discriminant Analysis.mp4 -
54.2 MB
215 Feat Eng Linear Discriminant Analysis (Practls).mp4 -
84.9 MB
216 Feature Engineering kPCA & QDA.mp4 -
53.5 MB
217 Feature Engineering kPCA & QDA (Practicals).mp4 -
50.7 MB
218 Hyper Parameter Optimization (HPO) Basics.mp4 -
76.0 MB
219 Hyper Parameter Optimization Manual HPO.mp4 -
31.9 MB
220 HPO GridSearch vs RandomizedSearch.mp4 -
70.8 MB
221 HPO Manual HPO (Practicals).mp4 -
164.8 MB
222 HPO RandomizedSearchCV (Practicals).mp4 -
138.8 MB
223 HPO GridSearchCV (Practicals).mp4 -
60.4 MB
224 Introduction to TSA.mp4 -
23.3 MB
225 Time Series vs Regression.mp4 -
77.1 MB
226 Time Series Analysis.mp4 -
14.6 MB
227 Anomaly Detection.mp4 -
29.9 MB
228 Components of Time Series.mp4 -
46.3 MB
229 Decomposition.mp4 -
6.5 MB
230 Decomposition (Practicals).mp4 -
46.5 MB
231 AdditiveMultiplicative Decomp.mp4 -
38.9 MB
232 Stationarity.mp4 -
28.5 MB
233 Testing TS Stationarity.mp4 -
43.6 MB
234 Transformation.mp4 -
21.4 MB
235 Introduction to Pre-Processing.mp4 -
17.8 MB
236 Handle Missing Value.mp4 -
58.5 MB
237 Handle Missing Value (Practicals).mp4 -
92.1 MB
238 Outlier Treatment.mp4 -
59.8 MB
239 3-Sigma Technique.mp4 -
102.7 MB
240 Feature Scaling.mp4 -
74.2 MB
241 Feature Scaling Standardization.mp4 -
22.7 MB
242 Feature Scaling Normalization.mp4 -
14.0 MB
243 Feature Scaling (Practicals).mp4 -
111.6 MB
244 Feature Encoding.mp4 -
83.8 MB
245 Feature Encoding (Practicals).mp4 -
78.8 MB
246 Models - Algorithms.mp4 -
5.8 MB
247 Models - ARIMA Part 1.mp4 -
11.4 MB
248 Models - ARIMA Part 2.mp4 -
32.1 MB
249 Models - AR Theory.mp4 -
41.7 MB
250 Models - MA Theory.mp4 -
46.0 MB
251 Models - ACFPACF Plots.mp4 -
45.4 MB
252 Models - Find p,d,q in ARIMA.mp4 -
11.9 MB
253 Models - ARIMA (Practicals Part 1).mp4 -
90.9 MB
254 Models - ARIMA (Practicals Part 2).mp4 -
85.7 MB
255 Models - ARIMA (Final).mp4 -
70.7 MB
256 Models - Decomposition.mp4 -
31.8 MB
257 Models - ACFPACF.mp4 -
21.6 MB
258 Models - Best Transformation.mp4 -
72.3 MB
259 Models - Grid Search (Part 1).mp4 -
90.1 MB
260 Models - Grid Search (Part 2).mp4 -
16.9 MB
261 Models - Final Model Building.mp4 -
83.6 MB
262 Models - Facebook Prophet (Part 1).mp4 -
52.0 MB
263 Models - Facebook Prophet (Part 2).mp4 -
84.7 MB
264 Models - Facebook Prophet (Part 3).mp4 -
51.9 MB
265 Mods - Multi Variate Time Series Analy.mp4 -
42.6 MB
266 Mods - Facebook Prophet Uni v Multi.mp4 -
118.2 MB
267 Introduction to Metrics.mp4 -
30.0 MB
268 Forecasting Evaluation Metrics.mp4 -
6.7 MB
269 Mean Squarred Error.mp4 -
7.0 MB
270 Root Mean Squarred Error.mp4 -
7.1 MB
271 Mean Absolute Percentage Error.mp4 -
16.3 MB
272 Proj 1 - Energy Forecasting Part 1.mp4 -
25.6 MB
273 Proj 1 - Energy Forecasting Part 2.mp4 -
53.2 MB
274 Proj 1 - Energy Forecasting Part 3.mp4 -
77.7 MB
275 Proj 2 - Stock Market Prediction Pt 1.mp4 -
30.6 MB
276 Proj 2 - Stock Market Prediction Pt 2.mp4 -
37.7 MB
277 Proj 2 - Stock Market Prediction Pt 3.mp4 -
152.5 MB
278 Proj 3 - Demand Forecasting Part 1.mp4 -
24.1 MB
279 Proj 3 - Demand Forecasting Part 2.mp4 -
113.4 MB
280 Proj 3 - Demand Forecasting Part 3.mp4 -
94.1 MB
281 Proj 3 - Demand Forecasting Part 4.mp4 -
10.8 MB
282 Proj 3 - Demand Forecasting Part 5.mp4 -
141.1 MB
283 Proj 3 - Demand Forecasting Part 6.mp4 -
79.8 MB
285 Introduction to Deep Learning.mp4 -
10.6 MB
286 Understanding Deep Learning.mp4 -
92.8 MB
287 What is a Neuron.mp4 -
132.7 MB
288 Activation Functions.mp4 -
70.4 MB
289 Activation Function Step Function.mp4 -
91.4 MB
290 Activation Function Linear Function.mp4 -
171.0 MB
291 Activation Function Sigmoid Function.mp4 -
93.5 MB
292 Activation Function TanH Function.mp4 -
45.9 MB
293 Activation Function ReLu Function.mp4 -
148.4 MB
294 Backpropagation & Forward Pass.mp4 -
212.4 MB
295 Gradient Descent.mp4 -
107.4 MB
296 Artificial Neural Networks Intuition.mp4 -
28.2 MB
297 Artificial Neural Networks Practicals.mp4 -
140.8 MB
298 Artificial NN Hyper Param Optimize.mp4 -
101.9 MB
299 Convolutional Neural Networks (CNN).mp4 -
123.1 MB
300 CNN Steps in CNN.mp4 -
176.2 MB
301 CNN Architecture Explained.mp4 -
253.1 MB
302 CNN Image Augmentation.mp4 -
205.7 MB
303 CNN Batch size vs iterations vs epochs.mp4 -
120.9 MB
304 CNN Practicals.mp4 -
308.3 MB
305 CNN Model Summary & Parameters.mp4 -
113.5 MB
306 CNN Project (X-Ray detection).mp4 -
260.9 MB
307 Recurrent Neural Networks (RNN) Basics.mp4 -
35.2 MB
308 RNN Types of RNN.mp4 -
19.0 MB
309 RNN Vanishing, Exploding Gradient Prob.mp4 -
94.6 MB
310 RNN LSTMs.mp4 -
36.2 MB
311 RNN LSTMs (Practicals).mp4 -
89.0 MB
312 Pre-Trained Models.mp4 -
172.0 MB
313 Pre-Trained Models (Practicals).mp4 -
214.2 MB
314 Pre-Trained Models VGG16.mp4 -
75.9 MB
315 Pre-Trained Models MobileNet.mp4 -
46.8 MB
316 Transfer Learning.mp4 -
39.2 MB
317 Proj Pneumonia Detection X-Ray Img.mp4 -
124.3 MB
319 Intro to NLP Introduction.mp4 -
59.1 MB
320 Intro to NLP Introduction continued.mp4 -
46.9 MB
321 Intro to NLP Key Challenges.mp4 -
67.5 MB
322 Intro to NLP Linguistics.mp4 -
31.1 MB
323 NLP Basics Case Folding.mp4 -
28.0 MB
324 NLP Basics SCR.mp4 -
89.8 MB
325 NLP Basics Handling Contractions.mp4 -
64.6 MB
326 NLP Basics Tokenization.mp4 -
38.9 MB
327 NLP Basics Stop Word Removal.mp4 -
40.6 MB
328 NLP Basics nGrams.mp4 -
52.3 MB
329 NLP Basics Vectorization.mp4 -
24.4 MB
330 NLP Basics Word Embeddings.mp4 -
14.5 MB
331 NLP Basics Bag of Words.mp4 -
50.7 MB
332 NLP Basics Bag of Words (Practicals).mp4 -
154.6 MB
333 NLP Basics TF-IDF.mp4 -
68.6 MB
334 NLP Basics TF-IDF (Practicals).mp4 -
150.4 MB
335 NLP Part of Speech Tag, Named Entity Recog.mp4 -
57.3 MB
336 NLP Basics NER (Practicals).mp4 -
96.9 MB
337 Word Embeddings Word2Vec Introduction.mp4 -
23.7 MB
338 Word Embeddings Word2Vec Part 2.mp4 -
13.6 MB
339 Word Embeddings Pre-Trained Word2Vec.mp4 -
62.3 MB
340 Word Embeddings Word2Vec Intuition.mp4 -
37.5 MB
341 Word Embed Word2Vec - Check X Features.mp4 -
65.8 MB
342 Word Embeddings Word2Vec CBOW.mp4 -
103.3 MB
343 Word Embed Word2Vec Skip Grams.mp4 -
55.9 MB
344 Word Embeddings GloVe.mp4 -
79.4 MB
345 Word Embeddings FastText.mp4 -
142.0 MB
346 Word Embeddings Cosine Similarity.mp4 -
95.0 MB
347 Neural Networks (NN) LSTMs Part 1.mp4 -
73.4 MB
348 NN LSTMs Part 2 (Architecture).mp4 -
107.0 MB
349 NN LSTMs Part 3 (Deep Dive).mp4 -
28.3 MB
350 NN LSTMs Part 4 Pointwise Operation.mp4 -
34.7 MB
351 NN LSTMs Part 5 (forget gate).mp4 -
61.8 MB
352 NN LSTMs Part 6 (inpute gate).mp4 -
115.0 MB
353 NN LSTMs Part 7 (output gate).mp4 -
49.5 MB
354 NN LSTMs Part 8 (Practicals #1).mp4 -
219.8 MB
355 NN LSTMs Part 9 (Practicals #2).mp4 -
90.1 MB
356 NN LSTMs Part 10 (Practicals #3).mp4 -
112.5 MB
357 NN GRU Part 1.mp4 -
19.8 MB
358 NN GRU Part 2.mp4 -
146.2 MB
359 NN GRU Part 3 (reset gate).mp4 -
41.3 MB
360 NN GRU Part 4 (update gate).mp4 -
44.6 MB
361 NN GRU Part 5 (Practicals).mp4 -
105.5 MB
362 NN Bi-Directional LSTMs.mp4 -
116.3 MB
364 Transformer Types.mp4 -
127.4 MB
365 Introduction to Transformers.mp4 -
145.8 MB
366 Self Attention.mp4 -
125.4 MB
367 Encoder Architecture.mp4 -
48.0 MB
368 Contextual Embeddings.mp4 -
30.2 MB
369 Decoder Architecture.mp4 -
31.4 MB
370 Introduction to BERT.mp4 -
72.1 MB
371 Configurations of BERT.mp4 -
25.4 MB
372 BERT Fine Tuning.mp4 -
21.3 MB
373 BERT Pre Tuning (Masked LM).mp4 -
50.1 MB
374 BERT Input Embeddings.mp4 -
62.1 MB
375 ARLM vs AELM.mp4 -
43.4 MB
376 RoBERTa.mp4 -
60.4 MB
377 DistilBERT.mp4 -
92.4 MB
378 AlBERT.mp4 -
112.4 MB
379 Introduction to GPT (Decoder Only).mp4 -
30.5 MB
380 GPT Architecture.mp4 -
27.7 MB
381 GPT Masked Multi Head Attention.mp4 -
86.0 MB
382 GPT Blocks.mp4 -
48.9 MB
383 GPT Training.mp4 -
54.6 MB
385 LLM Basics Context Window.mp4 -
56.2 MB
386 LLM Basics Prompt.mp4 -
63.9 MB
387 LLM Basics Prompt Engineering.mp4 -
119.9 MB
388 LLM Basics Prompt Tuning.mp4 -
57.1 MB
389 LLM Basics Prompt Structures.mp4 -
106.9 MB
390 RAGs Introduction to RAG.mp4 -
5.7 MB
391 RAGs What and Why.mp4 -
119.2 MB
392 RAGs Use Cases.mp4 -
138.8 MB
393 RAGs Paper Explanation.mp4 -
53.3 MB
394 RAGs Architecture Explanation.mp4 -
106.5 MB
395 RAGs Detailed Architect Walk-thru.mp4 -
74.8 MB
396 RAGs Practical Use Cases.mp4 -
256.5 MB
397 LangChain.mp4 -
83.3 MB
398 Intro Prompt Engineering.mp4 -
81.0 MB
399 Types of Prompting.mp4 -
90.4 MB
400 Few Shot Limitations.mp4 -
50.7 MB
401 Chain of Thoughts Prompting.mp4 -
45.0 MB
402 Vector Databases.mp4 -
86.4 MB
403 Vector Database vs Vector Index.mp4 -
60.8 MB
404 How Vector Databases works.mp4 -
72.1 MB
405 Vector Database (Practicals).mp4 -
260.8 MB
406 LSH.mp4 -
85.4 MB
407 Model Overview Ollama.mp4 -
310.6 MB
408 Getting Started Ollama.mp4 -
317.0 MB
409 Model Testing Ollama.mp4 -
384.5 MB
410 Python Implementation Ollama.mp4 -
131.6 MB
411 RAG Systems Ollama.mp4 -
69.0 MB
412 RAG Systems (Practicals) Ollama.mp4 -
219.3 MB
413 Model Overview LLM APIs.mp4 -
198.6 MB
414 RAG Systems with xAI LLM APIs.mp4 -
34.1 MB
415 RAG Sys w xAI (Practicals) LLM APIs.mp4 -
141.5 MB
416 Deployment Basics.mp4 -
27.4 MB
417 Introduction to Flask.mp4 -
72.2 MB
418 Flask Basic App.mp4 -
88.4 MB
419 Model Building (Breast Cancer Predict).mp4 -
135.8 MB
420 Flask App (Breast Cancer Prediction).mp4 -
187.8 MB
421 AWS.mp4 -
58.9 MB
422 AWS Deploy (Breast Cancer Predict).mp4 -
238.2 MB
423 Introduction to Data Engineering.mp4 -
3.3 MB
424 What is ETL.mp4 -
39.3 MB
425 ETL Tools.mp4 -
25.1 MB
426 What is Data Warehouse.mp4 -
26.6 MB
427 Benefits of Data Warehouse.mp4 -
18.9 MB
428 Data Warehouse Structure.mp4 -
19.1 MB
429 Why do we need Staging.mp4 -
30.4 MB
430 What are Data Marts.mp4 -
11.8 MB
431 Data Lake.mp4 -
22.4 MB
432 Data lake vs Data Warehouse.mp4 -
28.5 MB
433 Elements of Datalake.mp4 -
14.6 MB
434 ChatScholar (EdTech Project).mp4 -
399.7 MB
435 Research RAG Chatbot.mp4 -
295.1 MB
436 Auto AI Claims Processing - Gen AI.mp4 -
403.9 MB
437 PDF RAG(s) Chatbot Web Scrape Data.mp4 -
309.7 MB
438 AI Career Coach Part 1.mp4 -
94.0 MB
439 AI Career Coach Part 2.mp4 -
112.5 MB
440 AI Career Coach Part 3.mp4 -
223.2 MB
441 Sustainability Chatbot (GROK AI).mp4 -
379.5 MB
442 ML Interview Prep.mp4 -
49.2 MB
443 ML Interview #1.mp4 -
178.0 MB
444 ML Interview #2.mp4 -
205.0 MB
445 ML Interview #3.mp4 -
145.7 MB
446 ML Interview #4.mp4 -
143.2 MB
447 ML Interview #5.mp4 -
98.4 MB
448 ML Interview #6.mp4 -
151.3 MB
449 ML Interview #7.mp4 -
117.3 MB
450 ML Interview #8.mp4 -
137.4 MB
451 ML Interview #9.mp4 -
182.6 MB
452 ML Interview #10.mp4 -
136.4 MB
453 DL Interview #1.mp4 -
162.6 MB
454 DL Interview #2.mp4 -
110.6 MB
455 DL Interview #3.mp4 -
94.5 MB
456 DL Interview #4.mp4 -
102.5 MB
457 DL Interview #5.mp4 -
118.7 MB
458 DL Interview #6.mp4 -
148.4 MB
459 DL Interview #7.mp4 -
55.1 MB
460 DL Interview #8.mp4 -
140.0 MB
461 DL Interview #9.mp4 -
58.6 MB
462 DL Interview #10.mp4 -
136.6 MB
463 Gen AI Interview #1.mp4 -
76.8 MB
464 Gen AI Interview #2.mp4 -
90.5 MB
465 Gen AI Interview #3.mp4 -
157.7 MB
466 Gen AI Interview #4.mp4 -
126.8 MB
467 Gen AI Interview #5.mp4 -
122.9 MB
468 Gen AI Interview #6.mp4 -
144.1 MB
469 Gen AI Interview #7.mp4 -
116.4 MB
470 Gen AI Interview #8.mp4 -
150.2 MB
471 Gen AI Interview #9.mp4 -
149.2 MB
472 Gen AI Interview #10.mp4 -
156.0 MB
473 Gen AI Interview #2.mp4 -
90.5 MB
474 Gen AI Interview #3.mp4 -
157.7 MB
475 Gen AI Interview #4.mp4 -
126.8 MB
476 Gen AI Interview #5.mp4 -
122.9 MB
477 Gen AI Interview #6.mp4 -
144.1 MB
478 Gen AI Interview #7.mp4 -
116.4 MB
479 Gen AI Interview #8.mp4 -
150.2 MB
480 Gen AI Interview #9.mp4 -
149.2 MB
481 Gen AI Interview #10.mp4 -
156.0 MB
2025 Data Science & AI Masters From Python To Gen AI ~ Udemy - Satyajit Pattnaiko.txt -
228 bytes
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