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| 02. - Python+Installation+Guide.pdf |
841.95KB |
| 100 Types of Data.mp4 |
10.97MB |
| 101 Types of Analysis.mp4 |
12.03MB |
| 102 Univariate Analysis.mp4 |
54.11MB |
| 103 Bivariate Analysis.mp4 |
35.65MB |
| 104 Multivariate Analysis.mp4 |
5.23MB |
| 105 Numerical Analysis.mp4 |
19.41MB |
| 106 Analysis Practicals.mp4 |
211.54MB |
| 107 Derived Metrics.mp4 |
26.53MB |
| 108 Feature Binning (Theory).mp4 |
44.16MB |
| 109 Feature Binning (Practicals).mp4 |
71.01MB |
| 10 Datatypes Operators.mp4 |
364.22MB |
| 10 - Power+BI+Ebook.pdf |
14.38MB |
| 10 - RAG+with+GrokAI.ipynb |
20.04KB |
| 10 - RAG+with+Ollama.ipynb |
10.00KB |
| 10 - RAGPaper.pdf |
864.57KB |
| 10 - RAGPaper (1).pdf |
864.57KB |
| 110 Feature Encoding (Theory).mp4 |
83.76MB |
| 111 Feature Encoding (Practicals).mp4 |
169.81MB |
| 112 Case Study.mp4 |
79.59MB |
| 113 Data Exploration.mp4 |
151.44MB |
| 114 Data Cleaning.mp4 |
73.51MB |
| 115 Univariate Analysis.mp4 |
97.59MB |
| 116 Bivariate Analysis Part 1.mp4 |
129.32MB |
| 117 Bivariate Analysis Part 2.mp4 |
54.46MB |
| 118 EDA Report.mp4 |
35.83MB |
| 11 Lists.mp4 |
465.60MB |
| 120 Installation.mp4 |
59.09MB |
| 121 Data Architect - File server vs client server.mp4 |
119.34MB |
| 122 Introduction to SQL.mp4 |
164.50MB |
| 123 Constraints in SQL.mp4 |
289.40MB |
| 124 Table Basics - DDLs.mp4 |
396.54MB |
| 125 Table Basics - DQLs.mp4 |
290.03MB |
| 126 Table Basics - DMLs.mp4 |
461.59MB |
| 127 Joins.mp4 |
448.42MB |
| 128 Data Import Export.mp4 |
545.58MB |
| 129 Aggregation Functions.mp4 |
211.19MB |
| 12 Tuples.mp4 |
424.24MB |
| 130 String functions.mp4 |
287.89MB |
| 131 Date Time Functions.mp4 |
233.88MB |
| 132 Regular Expressions.mp4 |
160.70MB |
| 133 Nested Queries.mp4 |
263.49MB |
| 134 Views.mp4 |
222.76MB |
| 135 Stored Procedures.mp4 |
439.12MB |
| 136 Windows Function.mp4 |
365.77MB |
| 137 SQL Python connectivity.mp4 |
341.27MB |
| 138 Agenda.mp4 |
13.74MB |
| 139 Introduction to ML.mp4 |
32.17MB |
| 13 Sets.mp4 |
220.60MB |
| 140 Types of ML.mp4 |
104.93MB |
| 141 Use Cases Part 1.mp4 |
19.87MB |
| 142 Use Cases Part 2.mp4 |
8.04MB |
| 143 Pre-Requisites Features.mp4 |
88.74MB |
| 144 Pre-Requisites Train-Test Split.mp4 |
115.75MB |
| 145 Pre-Requisites Feature Scaling.mp4 |
74.24MB |
| 146 Pre-Requisites Standardization Example.mp4 |
22.66MB |
| 147 Pre-Requisites Normalization Example.mp4 |
13.95MB |
| 148 Pre-Requisites Feature Encoding.mp4 |
83.76MB |
| 149 Pre-Req Feature Encoding (Practicals).mp4 |
78.84MB |
| 14 Dictionary.mp4 |
297.26MB |
| 150 Regression Intro to Regression Models.mp4 |
38.90MB |
| 151 Regression Regression Metrics.mp4 |
151.37MB |
| 152 Regression Regression Metrics (Practicals).mp4 |
102.58MB |
| 153 Regression Simple Linear Regression.mp4 |
55.94MB |
| 154 Regression Multiple Linear Regression.mp4 |
51.48MB |
| 155 Regression Linear Regression (Practicals).mp4 |
210.80MB |
| 156 Regress Multi Linear Regress (Practicals).mp4 |
96.24MB |
| 157 Regression Polynomial Regression.mp4 |
39.25MB |
| 158 Regression Polynomial Regress (Practicals).mp4 |
176.58MB |
| 159 Regression Bias Variance Tradeoff.mp4 |
30.95MB |
| 15 Loops & Iterations.mp4 |
336.17MB |
| 160 Regression Ridge Regression.mp4 |
55.15MB |
| 161 Regression Lasso Regression.mp4 |
43.86MB |
| 162 Regress Lasso, Ridge Regress (Practicals).mp4 |
335.38MB |
| 163 Classification Intro to Classification.mp4 |
41.27MB |
| 164 Classification Types of Classification.mp4 |
25.69MB |
| 165 Classification Log Loss.mp4 |
63.61MB |
| 166 Classification Confusion Matrix.mp4 |
72.79MB |
| 167 Classification AUC ROC Curve.mp4 |
48.57MB |
| 168 Classification Classification Report.mp4 |
47.22MB |
| 169 Classification kNN Classifier.mp4 |
80.80MB |
| 16 Functions.mp4 |
393.85MB |
| 170 Classification kNN Classifier Example.mp4 |
78.01MB |
| 171 Classification Practicals Part 1.mp4 |
100.52MB |
| 172 Classification kNN Classifier (Practicals).mp4 |
115.41MB |
| 173 Classification Decision Tree.mp4 |
72.98MB |
| 174 Class.. Decision Tree (Entropy based).mp4 |
112.46MB |
| 175 Classification Decision Tree (gini based).mp4 |
104.23MB |
| 176 Classification Decision Tree (Practicals).mp4 |
66.53MB |
| 177 Classification Decision Tree (Visualizing).mp4 |
160.32MB |
| 178 Classification Random Forest Classifier.mp4 |
40.94MB |
| 179 Class.. Random Forest Classifier (Practs).mp4 |
46.69MB |
| 17 Map Reduce Filter.mp4 |
514.75MB |
| 180 Classification Naive Bayes Classifier.mp4 |
89.90MB |
| 181 Classification SVM Classifier Part 1.mp4 |
71.78MB |
| 182 Classification SVM Classifier Part 2.mp4 |
60.17MB |
| 183 Classification Logistic Regression.mp4 |
119.15MB |
| 184 Classification Practicals so far.mp4 |
218.70MB |
| 185 Class.. Issues in Classification (Part 1).mp4 |
48.41MB |
| 186 Class.. Issues in Classification (Part 2).mp4 |
80.24MB |
| 187 Classification Project.mp4 |
308.88MB |
| 188 Ensemble Intro to Ensemble Learning.mp4 |
117.94MB |
| 189 Ensemble Bagging.mp4 |
50.65MB |
| 18 File Handling.mp4 |
327.29MB |
| 190 Ensemble Bagging vs Random Forest.mp4 |
91.07MB |
| 191 Ensemble Bagging (Practicals #1).mp4 |
241.19MB |
| 192 Ensemble Bagging (Practicals #2).mp4 |
178.06MB |
| 193 Ensemble Boosting.mp4 |
41.91MB |
| 194 Ensemble Ada Boost.mp4 |
97.89MB |
| 195 Ensemble Gradient Boost.mp4 |
20.82MB |
| 196 Ensemble CF vs LF.mp4 |
47.15MB |
| 197 Ensemble Cross Entropy.mp4 |
22.06MB |
| 198 Ensemble Xtreme Gradient Boosting (XGB).mp4 |
94.38MB |
| 199 Ensemble Project.mp4 |
210.58MB |
| 19 Control Structures.mp4 |
171.90MB |
| 1 Welcome Page.mp4 |
51.49MB |
| 200 Clustering Introduction to Clustering.mp4 |
104.01MB |
| 201 Clustering kMeans Clustering.mp4 |
121.01MB |
| 2025 Data Science & AI Masters From Python To Gen AI ~ Udemy - Satyajit Pattnaiko.txt |
228B |
| 202 Clustering kMeans Clustering (Practicals).mp4 |
133.40MB |
| 203 Clustering Hierarchical Clustering.mp4 |
81.94MB |
| 204 Clustering Hierarchy Cluster (Practicals).mp4 |
106.49MB |
| 205 Clustering Mean Shift Clustering.mp4 |
73.35MB |
| 206 Feature Engineering Introduction.mp4 |
87.40MB |
| 207 Feature Engineering RFE and SFS.mp4 |
29.05MB |
| 208 Feature Engineering RFE (Practicals).mp4 |
190.89MB |
| 209 Feature Eng.. Successive Feature Selection.mp4 |
180.08MB |
| 20 OOPs.mp4 |
335.18MB |
| 210 Feature Engineering Chi-Square.mp4 |
31.70MB |
| 211 Feature Eng.. Chi-Square (Practicals).mp4 |
54.64MB |
| 212 Feat Eng Principal Component Analysis.mp4 |
258.21MB |
| 213 Feat Eng Principal Component Analy (Practls).mp4 |
79.70MB |
| 214 Feat Eng Linear Discriminant Analysis.mp4 |
54.25MB |
| 215 Feat Eng Linear Discriminant Analysis (Practls).mp4 |
84.87MB |
| 216 Feature Engineering kPCA & QDA.mp4 |
53.51MB |
| 217 Feature Engineering kPCA & QDA (Practicals).mp4 |
50.71MB |
| 218 Hyper Parameter Optimization (HPO) Basics.mp4 |
76.02MB |
| 219 Hyper Parameter Optimization Manual HPO.mp4 |
31.87MB |
| 21 NumPy.mp4 |
485.43MB |
| 220 HPO GridSearch vs RandomizedSearch.mp4 |
70.77MB |
| 221 HPO Manual HPO (Practicals).mp4 |
164.77MB |
| 222 HPO RandomizedSearchCV (Practicals).mp4 |
138.78MB |
| 223 HPO GridSearchCV (Practicals).mp4 |
60.41MB |
| 224 Introduction to TSA.mp4 |
23.29MB |
| 225 Time Series vs Regression.mp4 |
77.14MB |
| 226 Time Series Analysis.mp4 |
14.58MB |
| 227 Anomaly Detection.mp4 |
29.89MB |
| 228 Components of Time Series.mp4 |
46.34MB |
| 229 Decomposition.mp4 |
6.46MB |
| 22 Pandas.mp4 |
567.40MB |
| 230 Decomposition (Practicals).mp4 |
46.48MB |
| 231 AdditiveMultiplicative Decomp.mp4 |
38.94MB |
| 232 Stationarity.mp4 |
28.53MB |
| 233 Testing TS Stationarity.mp4 |
43.64MB |
| 234 Transformation.mp4 |
21.42MB |
| 235 Introduction to Pre-Processing.mp4 |
17.81MB |
| 236 Handle Missing Value.mp4 |
58.55MB |
| 237 Handle Missing Value (Practicals).mp4 |
92.12MB |
| 238 Outlier Treatment.mp4 |
59.77MB |
| 239 3-Sigma Technique.mp4 |
102.73MB |
| 23 Data Visualization.mp4 |
113.44MB |
| 240 Feature Scaling.mp4 |
74.24MB |
| 241 Feature Scaling Standardization.mp4 |
22.67MB |
| 242 Feature Scaling Normalization.mp4 |
13.95MB |
| 243 Feature Scaling (Practicals).mp4 |
111.60MB |
| 244 Feature Encoding.mp4 |
83.77MB |
| 245 Feature Encoding (Practicals).mp4 |
78.85MB |
| 246 Models - Algorithms.mp4 |
5.79MB |
| 247 Models - ARIMA Part 1.mp4 |
11.35MB |
| 248 Models - ARIMA Part 2.mp4 |
32.11MB |
| 249 Models - AR Theory.mp4 |
41.67MB |
| 24 Matplotlib.mp4 |
449.69MB |
| 250 Models - MA Theory.mp4 |
46.00MB |
| 251 Models - ACFPACF Plots.mp4 |
45.36MB |
| 252 Models - Find p,d,q in ARIMA.mp4 |
11.88MB |
| 253 Models - ARIMA (Practicals Part 1).mp4 |
90.90MB |
| 254 Models - ARIMA (Practicals Part 2).mp4 |
85.74MB |
| 255 Models - ARIMA (Final).mp4 |
70.73MB |
| 256 Models - Decomposition.mp4 |
31.81MB |
| 257 Models - ACFPACF.mp4 |
21.56MB |
| 258 Models - Best Transformation.mp4 |
72.32MB |
| 259 Models - Grid Search (Part 1).mp4 |
90.15MB |
| 25 Seaborn.mp4 |
325.23MB |
| 260 Models - Grid Search (Part 2).mp4 |
16.93MB |
| 261 Models - Final Model Building.mp4 |
83.57MB |
| 262 Models - Facebook Prophet (Part 1).mp4 |
52.00MB |
| 263 Models - Facebook Prophet (Part 2).mp4 |
84.67MB |
| 264 Models - Facebook Prophet (Part 3).mp4 |
51.95MB |
| 265 Mods - Multi Variate Time Series Analy.mp4 |
42.59MB |
| 266 Mods - Facebook Prophet Uni v Multi.mp4 |
118.20MB |
| 267 Introduction to Metrics.mp4 |
29.98MB |
| 268 Forecasting Evaluation Metrics.mp4 |
6.72MB |
| 269 Mean Squarred Error.mp4 |
7.04MB |
| 270 Root Mean Squarred Error.mp4 |
7.10MB |
| 271 Mean Absolute Percentage Error.mp4 |
16.31MB |
| 272 Proj 1 - Energy Forecasting Part 1.mp4 |
25.59MB |
| 273 Proj 1 - Energy Forecasting Part 2.mp4 |
53.23MB |
| 274 Proj 1 - Energy Forecasting Part 3.mp4 |
77.66MB |
| 275 Proj 2 - Stock Market Prediction Pt 1.mp4 |
30.64MB |
| 276 Proj 2 - Stock Market Prediction Pt 2.mp4 |
37.68MB |
| 277 Proj 2 - Stock Market Prediction Pt 3.mp4 |
152.52MB |
| 278 Proj 3 - Demand Forecasting Part 1.mp4 |
24.07MB |
| 279 Proj 3 - Demand Forecasting Part 2.mp4 |
113.42MB |
| 27 Introduction.mp4 |
42.33MB |
| 280 Proj 3 - Demand Forecasting Part 3.mp4 |
94.14MB |
| 281 Proj 3 - Demand Forecasting Part 4.mp4 |
10.83MB |
| 282 Proj 3 - Demand Forecasting Part 5.mp4 |
141.06MB |
| 283 Proj 3 - Demand Forecasting Part 6.mp4 |
79.80MB |
| 285 Introduction to Deep Learning.mp4 |
10.62MB |
| 286 Understanding Deep Learning.mp4 |
92.76MB |
| 287 What is a Neuron.mp4 |
132.66MB |
| 288 Activation Functions.mp4 |
70.41MB |
| 289 Activation Function Step Function.mp4 |
91.35MB |
| 28 Types of Data (Agenda).mp4 |
3.18MB |
| 290 Activation Function Linear Function.mp4 |
170.98MB |
| 291 Activation Function Sigmoid Function.mp4 |
93.51MB |
| 292 Activation Function TanH Function.mp4 |
45.91MB |
| 293 Activation Function ReLu Function.mp4 |
148.45MB |
| 294 Backpropagation & Forward Pass.mp4 |
212.37MB |
| 295 Gradient Descent.mp4 |
107.42MB |
| 296 Artificial Neural Networks Intuition.mp4 |
28.24MB |
| 297 Artificial Neural Networks Practicals.mp4 |
140.78MB |
| 298 Artificial NN Hyper Param Optimize.mp4 |
101.91MB |
| 299 Convolutional Neural Networks (CNN).mp4 |
123.11MB |
| 29 Descriptive Stats.mp4 |
79.79MB |
| 300 CNN Steps in CNN.mp4 |
176.23MB |
| 301 CNN Architecture Explained.mp4 |
253.06MB |
| 302 CNN Image Augmentation.mp4 |
205.75MB |
| 303 CNN Batch size vs iterations vs epochs.mp4 |
120.93MB |
| 304 CNN Practicals.mp4 |
308.33MB |
| 305 CNN Model Summary & Parameters.mp4 |
113.47MB |
| 306 CNN Project (X-Ray detection).mp4 |
260.89MB |
| 307 Recurrent Neural Networks (RNN) Basics.mp4 |
35.18MB |
| 308 RNN Types of RNN.mp4 |
19.00MB |
| 309 RNN Vanishing, Exploding Gradient Prob.mp4 |
94.64MB |
| 30 Inferential Stats.mp4 |
13.38MB |
| 310 RNN LSTMs.mp4 |
36.19MB |
| 311 RNN LSTMs (Practicals).mp4 |
88.98MB |
| 312 Pre-Trained Models.mp4 |
171.97MB |
| 313 Pre-Trained Models (Practicals).mp4 |
214.15MB |
| 314 Pre-Trained Models VGG16.mp4 |
75.88MB |
| 315 Pre-Trained Models MobileNet.mp4 |
46.76MB |
| 316 Transfer Learning.mp4 |
39.19MB |
| 317 Proj Pneumonia Detection X-Ray Img.mp4 |
124.27MB |
| 319 Intro to NLP Introduction.mp4 |
59.10MB |
| 31 Qualitative Data.mp4 |
50.03MB |
| 320 Intro to NLP Introduction continued.mp4 |
46.87MB |
| 321 Intro to NLP Key Challenges.mp4 |
67.52MB |
| 322 Intro to NLP Linguistics.mp4 |
31.11MB |
| 323 NLP Basics Case Folding.mp4 |
27.96MB |
| 324 NLP Basics SCR.mp4 |
89.81MB |
| 325 NLP Basics Handling Contractions.mp4 |
64.58MB |
| 326 NLP Basics Tokenization.mp4 |
38.86MB |
| 327 NLP Basics Stop Word Removal.mp4 |
40.58MB |
| 328 NLP Basics nGrams.mp4 |
52.28MB |
| 329 NLP Basics Vectorization.mp4 |
24.37MB |
| 32 Quantitative Data.mp4 |
20.51MB |
| 330 NLP Basics Word Embeddings.mp4 |
14.51MB |
| 331 NLP Basics Bag of Words.mp4 |
50.68MB |
| 332 NLP Basics Bag of Words (Practicals).mp4 |
154.60MB |
| 333 NLP Basics TF-IDF.mp4 |
68.59MB |
| 334 NLP Basics TF-IDF (Practicals).mp4 |
150.40MB |
| 335 NLP Part of Speech Tag, Named Entity Recog.mp4 |
57.27MB |
| 336 NLP Basics NER (Practicals).mp4 |
96.94MB |
| 337 Word Embeddings Word2Vec Introduction.mp4 |
23.68MB |
| 338 Word Embeddings Word2Vec Part 2.mp4 |
13.59MB |
| 339 Word Embeddings Pre-Trained Word2Vec.mp4 |
62.30MB |
| 33 Sampling Techniques (Agenda).mp4 |
7.62MB |
| 340 Word Embeddings Word2Vec Intuition.mp4 |
37.53MB |
| 341 Word Embed Word2Vec - Check X Features.mp4 |
65.76MB |
| 342 Word Embeddings Word2Vec CBOW.mp4 |
103.25MB |
| 343 Word Embed Word2Vec Skip Grams.mp4 |
55.89MB |
| 344 Word Embeddings GloVe.mp4 |
79.39MB |
| 345 Word Embeddings FastText.mp4 |
141.98MB |
| 346 Word Embeddings Cosine Similarity.mp4 |
94.96MB |
| 347 Neural Networks (NN) LSTMs Part 1.mp4 |
73.43MB |
| 348 NN LSTMs Part 2 (Architecture).mp4 |
106.95MB |
| 349 NN LSTMs Part 3 (Deep Dive).mp4 |
28.26MB |
| 34 Population vs Sample.mp4 |
17.90MB |
| 350 NN LSTMs Part 4 Pointwise Operation.mp4 |
34.74MB |
| 351 NN LSTMs Part 5 (forget gate).mp4 |
61.76MB |
| 352 NN LSTMs Part 6 (inpute gate).mp4 |
115.02MB |
| 353 NN LSTMs Part 7 (output gate).mp4 |
49.50MB |
| 354 NN LSTMs Part 8 (Practicals #1).mp4 |
219.76MB |
| 355 NN LSTMs Part 9 (Practicals #2).mp4 |
90.07MB |
| 356 NN LSTMs Part 10 (Practicals #3).mp4 |
112.51MB |
| 357 NN GRU Part 1.mp4 |
19.82MB |
| 358 NN GRU Part 2.mp4 |
146.16MB |
| 359 NN GRU Part 3 (reset gate).mp4 |
41.27MB |
| 35 Why Sampling is important.mp4 |
17.00MB |
| 360 NN GRU Part 4 (update gate).mp4 |
44.60MB |
| 361 NN GRU Part 5 (Practicals).mp4 |
105.53MB |
| 362 NN Bi-Directional LSTMs.mp4 |
116.33MB |
| 364 Transformer Types.mp4 |
127.41MB |
| 365 Introduction to Transformers.mp4 |
145.80MB |
| 366 Self Attention.mp4 |
125.40MB |
| 367 Encoder Architecture.mp4 |
47.99MB |
| 368 Contextual Embeddings.mp4 |
30.20MB |
| 369 Decoder Architecture.mp4 |
31.39MB |
| 36 Types of Sampling.mp4 |
20.74MB |
| 370 Introduction to BERT.mp4 |
72.09MB |
| 371 Configurations of BERT.mp4 |
25.37MB |
| 372 BERT Fine Tuning.mp4 |
21.34MB |
| 373 BERT Pre Tuning (Masked LM).mp4 |
50.10MB |
| 374 BERT Input Embeddings.mp4 |
62.12MB |
| 375 ARLM vs AELM.mp4 |
43.40MB |
| 376 RoBERTa.mp4 |
60.41MB |
| 377 DistilBERT.mp4 |
92.38MB |
| 378 AlBERT.mp4 |
112.40MB |
| 379 Introduction to GPT (Decoder Only).mp4 |
30.54MB |
| 37 Cluster Random Sampling.mp4 |
30.59MB |
| 380 GPT Architecture.mp4 |
27.73MB |
| 381 GPT Masked Multi Head Attention.mp4 |
85.96MB |
| 382 GPT Blocks.mp4 |
48.92MB |
| 383 GPT Training.mp4 |
54.63MB |
| 385 LLM Basics Context Window.mp4 |
56.20MB |
| 386 LLM Basics Prompt.mp4 |
63.94MB |
| 387 LLM Basics Prompt Engineering.mp4 |
119.88MB |
| 388 LLM Basics Prompt Tuning.mp4 |
57.09MB |
| 389 LLM Basics Prompt Structures.mp4 |
106.90MB |
| 38 Probability Sampling.mp4 |
40.69MB |
| 390 RAGs Introduction to RAG.mp4 |
5.72MB |
| 391 RAGs What and Why.mp4 |
119.24MB |
| 392 RAGs Use Cases.mp4 |
138.77MB |
| 393 RAGs Paper Explanation.mp4 |
53.32MB |
| 394 RAGs Architecture Explanation.mp4 |
106.51MB |
| 395 RAGs Detailed Architect Walk-thru.mp4 |
74.79MB |
| 396 RAGs Practical Use Cases.mp4 |
256.53MB |
| 397 LangChain.mp4 |
83.30MB |
| 398 Intro Prompt Engineering.mp4 |
80.96MB |
| 399 Types of Prompting.mp4 |
90.41MB |
| 39 Non probability sampling.mp4 |
31.45MB |
| 400 Few Shot Limitations.mp4 |
50.66MB |
| 401 Chain of Thoughts Prompting.mp4 |
45.02MB |
| 402 Vector Databases.mp4 |
86.44MB |
| 403 Vector Database vs Vector Index.mp4 |
60.82MB |
| 404 How Vector Databases works.mp4 |
72.07MB |
| 405 Vector Database (Practicals).mp4 |
260.83MB |
| 406 LSH.mp4 |
85.36MB |
| 407 Model Overview Ollama.mp4 |
310.61MB |
| 408 Getting Started Ollama.mp4 |
316.98MB |
| 409 Model Testing Ollama.mp4 |
384.54MB |
| 40 Population Sampling.mp4 |
56.37MB |
| 410 Python Implementation Ollama.mp4 |
131.64MB |
| 411 RAG Systems Ollama.mp4 |
69.04MB |
| 412 RAG Systems (Practicals) Ollama.mp4 |
219.26MB |
| 413 Model Overview LLM APIs.mp4 |
198.55MB |
| 414 RAG Systems with xAI LLM APIs.mp4 |
34.15MB |
| 415 RAG Sys w xAI (Practicals) LLM APIs.mp4 |
141.55MB |
| 416 Deployment Basics.mp4 |
27.39MB |
| 417 Introduction to Flask.mp4 |
72.20MB |
| 418 Flask Basic App.mp4 |
88.42MB |
| 419 Model Building (Breast Cancer Predict).mp4 |
135.84MB |
| 41 Why n-1 and not n.mp4 |
32.83MB |
| 420 Flask App (Breast Cancer Prediction).mp4 |
187.83MB |
| 421 AWS.mp4 |
58.89MB |
| 422 AWS Deploy (Breast Cancer Predict).mp4 |
238.25MB |
| 423 Introduction to Data Engineering.mp4 |
3.25MB |
| 424 What is ETL.mp4 |
39.27MB |
| 425 ETL Tools.mp4 |
25.14MB |
| 426 What is Data Warehouse.mp4 |
26.63MB |
| 427 Benefits of Data Warehouse.mp4 |
18.90MB |
| 428 Data Warehouse Structure.mp4 |
19.13MB |
| 429 Why do we need Staging.mp4 |
30.38MB |
| 42 Descriptive Analytics (Agenda).mp4 |
4.97MB |
| 430 What are Data Marts.mp4 |
11.84MB |
| 431 Data Lake.mp4 |
22.44MB |
| 432 Data lake vs Data Warehouse.mp4 |
28.52MB |
| 433 Elements of Datalake.mp4 |
14.58MB |
| 434 ChatScholar (EdTech Project).mp4 |
399.70MB |
| 435 Research RAG Chatbot.mp4 |
295.13MB |
| 436 Auto AI Claims Processing - Gen AI.mp4 |
403.93MB |
| 437 PDF RAG(s) Chatbot Web Scrape Data.mp4 |
309.70MB |
| 438 AI Career Coach Part 1.mp4 |
93.98MB |
| 439 AI Career Coach Part 2.mp4 |
112.49MB |
| 43 Measures of Central Tendency.mp4 |
9.09MB |
| 440 AI Career Coach Part 3.mp4 |
223.18MB |
| 441 Sustainability Chatbot (GROK AI).mp4 |
379.46MB |
| 442 ML Interview Prep.mp4 |
49.21MB |
| 443 ML Interview #1.mp4 |
178.00MB |
| 444 ML Interview #2.mp4 |
204.99MB |
| 445 ML Interview #3.mp4 |
145.65MB |
| 446 ML Interview #4.mp4 |
143.18MB |
| 447 ML Interview #5.mp4 |
98.43MB |
| 448 ML Interview #6.mp4 |
151.35MB |
| 449 ML Interview #7.mp4 |
117.27MB |
| 44 Mean.mp4 |
26.98MB |
| 450 ML Interview #8.mp4 |
137.44MB |
| 451 ML Interview #9.mp4 |
182.60MB |
| 452 ML Interview #10.mp4 |
136.39MB |
| 453 DL Interview #1.mp4 |
162.63MB |
| 454 DL Interview #2.mp4 |
110.60MB |
| 455 DL Interview #3.mp4 |
94.54MB |
| 456 DL Interview #4.mp4 |
102.47MB |
| 457 DL Interview #5.mp4 |
118.68MB |
| 458 DL Interview #6.mp4 |
148.35MB |
| 459 DL Interview #7.mp4 |
55.11MB |
| 45 Median.mp4 |
36.45MB |
| 460 DL Interview #8.mp4 |
139.98MB |
| 461 DL Interview #9.mp4 |
58.59MB |
| 462 DL Interview #10.mp4 |
136.64MB |
| 463 Gen AI Interview #1.mp4 |
76.82MB |
| 464 Gen AI Interview #2.mp4 |
90.47MB |
| 465 Gen AI Interview #3.mp4 |
157.71MB |
| 466 Gen AI Interview #4.mp4 |
126.79MB |
| 467 Gen AI Interview #5.mp4 |
122.87MB |
| 468 Gen AI Interview #6.mp4 |
144.05MB |
| 469 Gen AI Interview #7.mp4 |
116.41MB |
| 46 Mode.mp4 |
28.12MB |
| 470 Gen AI Interview #8.mp4 |
150.24MB |
| 471 Gen AI Interview #9.mp4 |
149.16MB |
| 472 Gen AI Interview #10.mp4 |
156.04MB |
| 473 Gen AI Interview #2.mp4 |
90.47MB |
| 474 Gen AI Interview #3.mp4 |
157.71MB |
| 475 Gen AI Interview #4.mp4 |
126.79MB |
| 476 Gen AI Interview #5.mp4 |
122.87MB |
| 477 Gen AI Interview #6.mp4 |
144.05MB |
| 478 Gen AI Interview #7.mp4 |
116.41MB |
| 479 Gen AI Interview #8.mp4 |
150.24MB |
| 47 Measures of Dispersion.mp4 |
21.57MB |
| 480 Gen AI Interview #9.mp4 |
149.16MB |
| 481 Gen AI Interview #10.mp4 |
156.04MB |
| 48 Range.mp4 |
7.26MB |
| 49 IQR.mp4 |
19.47MB |
| 50 Variance Standard Deviation.mp4 |
56.38MB |
| 51 Mean Deviation.mp4 |
18.94MB |
| 52 Probability (Agenda).mp4 |
4.67MB |
| 53 Probability.mp4 |
41.99MB |
| 54 Addition Rule.mp4 |
45.38MB |
| 55 Independent Events.mp4 |
25.97MB |
| 56 Cumulative Probability.mp4 |
29.42MB |
| 57 Conditional Probability.mp4 |
57.91MB |
| 58 Bayes Theorem 1.mp4 |
9.45MB |
| 59 Bayes Theorem 2.mp4 |
24.97MB |
| 5 Let's install Python together!!.mp4 |
273.19MB |
| 60 Probability Distrubution (Agenda).mp4 |
10.38MB |
| 61 Uniform Distribution.mp4 |
44.12MB |
| 62 Binomial Distribution.mp4 |
70.84MB |
| 63 Poisson Distribution.mp4 |
18.84MB |
| 64 Normal Distribution Part 1.mp4 |
77.48MB |
| 65 Normal Distribution Part 2.mp4 |
34.26MB |
| 66 Skewness.mp4 |
25.21MB |
| 67 Kurtosis.mp4 |
14.06MB |
| 68 Calc Prob w Z-score - Normal Distrib Pt 1.mp4 |
49.64MB |
| 69 Calc Prob w Z-score - Normal Distrib Pt 2.mp4 |
46.98MB |
| 6 Google Colab, what's that.mp4 |
51.36MB |
| 70 Calc Prob w Z-score - Normal Distrib Pt 3.mp4 |
27.00MB |
| 71 Covariance & Correlation (Agenda).mp4 |
2.68MB |
| 72 Covariance.mp4 |
54.35MB |
| 73 Correlation.mp4 |
86.55MB |
| 74 Covariance VS Correlation.mp4 |
26.64MB |
| 75 Hypothesis Testing.mp4 |
52.89MB |
| 76 Tailed Tests.mp4 |
16.83MB |
| 77 p-value.mp4 |
32.00MB |
| 78 Types of Test.mp4 |
26.87MB |
| 79 T Test.mp4 |
51.26MB |
| 7 Let's leverage chatGPT for help!!.mp4 |
70.76MB |
| 80 Z Test.mp4 |
67.39MB |
| 81 Chi Square Test.mp4 |
67.34MB |
| 82 ANOVA.mp4 |
68.94MB |
| 83 Correlation Test (Practicals).mp4 |
44.60MB |
| 85 Agenda.mp4 |
15.24MB |
| 86 DA,DS Processes.mp4 |
24.27MB |
| 87 What is EDA.mp4 |
27.14MB |
| 88 Visualization.mp4 |
30.52MB |
| 89 Steps involved in EDA (Data Sourcing).mp4 |
28.80MB |
| 8 Introduction to Python.mp4 |
94.25MB |
| 90 Steps involved in EDA (Data Cleaning).mp4 |
30.44MB |
| 91 Handle Missing Values (Theory).mp4 |
58.52MB |
| 92 Handle Missing Values (Practicals).mp4 |
92.11MB |
| 93 Feature Scaling (Theory).mp4 |
74.23MB |
| 94 Standardization Example.mp4 |
22.66MB |
| 95 Normalization Example.mp4 |
13.95MB |
| 96 Feature Scaling (Practicals).mp4 |
111.56MB |
| 97 Outlier Treatment (Theory).mp4 |
59.81MB |
| 98 Outlier Treatment (Practicals).mp4 |
102.74MB |
| 99 Invalid Data.mp4 |
42.51MB |
| 9 Variables & Keywords.mp4 |
352.33MB |