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**AD-TEAM** · 11-19-2024, 02:17 AM

11.05 GB | 00:22:00 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :
1 - Machine Learning Intuition (14.5 MB)
2 - Course Overview (19.9 MB)
4 - Install Anaconda and Python 3 on Windows 10 (26.02 MB)
5 - Install Anaconda and Python 3 on Ubuntu Machine (13.6 MB)
6 - Install Anaconda and Python 3 on Mac Machine (71.91 MB)
7 - Install Git Bash and Commander Terminal (31.84 MB)
8 - Jupyter Notebook Shortcuts (16.44 MB)
125 - Text Feature Extraction Intuition Part 1 (17.89 MB)
126 - Text Feature Extraction Intuition Part 2 (39.56 MB)
127 - Bag of Words BoW Code Along in Python (20.87 MB)
128 - Term Frequency TF Code Along in Python (20.25 MB)
129 - Inverse Document Frequency IDF Code Along in Python (28.46 MB)
130 - TFIDF Code Along in Python (15.29 MB)
131 - Load Spam Dataset (18.22 MB)
132 - Balance Dataset (16.29 MB)
133 - Exploratory Data Analysis EDA (22.61 MB)
134 - Data Preparation for Training (27.86 MB)
135 - Build and Train SVM and Random Forest Models (20.58 MB)
136 - Test Your Model with Real Data (4.94 MB)
137 - Notebook Setup (19.05 MB)
138 - SVM Model Training (21.06 MB)
139 - Test Your Model (27.1 MB)
140 - Data Cleaning and Retraining SVM Part 1 (28.95 MB)
141 - Data Cleaning and Retraining SVM Part 2 (28.03 MB)
142 - Fine Tune Your ML Model (44.14 MB)
143 - Saving and Loading ML Model (24.7 MB)
144 - Create Twitter Developer Account (30.68 MB)
145 - Get the Access Tokens (28.73 MB)
146 - Reading Twitter Timeline in RealTime (48.46 MB)
147 - Tracking Keywords in RealTime on Twitter Part 1 (34.68 MB)
148 - Tracking Keywords in RealTime on Twitter Part 2 (49.79 MB)
149 - Tracking Keywords in RealTime on Twitter Part 3 (30.04 MB)
150 - RealTime Sentiment Analysis with TextBlob (51.96 MB)
151 - RealTime Sentiment Analysis with Trained ML Model (65.93 MB)
152 - RealTime Twitter Sentiment Analysis of USA vs China Part 1 (23.28 MB)
153 - RealTime Twitter Sentiment Analysis of USA vs China Part 2 (24.19 MB)
154 - RealTime Twitter Sentiment Animation Plot Part 1 (29.86 MB)
155 - RealTime Twitter Sentiment Animation Plot Part 2 (35.23 MB)
156 - What is Feature Dimensionality Reduction (19.77 MB)
157 - Principal Components Analysis PCA (17.14 MB)
158 - Linear Discriminant Analysis LDA (50.63 MB)
159 - NonNegative Matrix Factorization NMF (7.48 MB)
160 - Truncated Singular Value Decomposition TSVD (34.9 MB)
161 - TFIDF and Sparse Matrix Part 1 (13 MB)
162 - TFIDF and Sparse Matrix Part 2 (17.32 MB)
163 - TFIDF and Sparse Matrix Part 3 (22.2 MB)
164 - NonNegative Matrix Factorization NMF Code Along Part 1 (28.77 MB)
165 - NonNegative Matrix Factorization NMF Code Along Part 2 (27.42 MB)
166 - Truncated Singular Value Decomposition TSVD Code Along (43.57 MB)
167 - What is Hyperparameters Tuning (16.22 MB)
168 - Hyperparameter Tuning Methods (16.64 MB)
169 - Grid Search for Hyperparameters with KFold CrossValidation (27.12 MB)
170 - GridSearch for Logistic Regression Hyperparameters Tuning Part 1 (32.91 MB)
171 - GridSearch for Logistic Regression Hyperparameters Tuning Part 2 (67.13 MB)
172 - GridSearch for SVM Hyperparameters Tuning Part 1 (33.88 MB)
173 - GridSearch for SVM Hyperparameters Tuning Part 2 (93.73 MB)
174 - Grid Search for Random Forest Classifier Hyperparameters Tuning (55.38 MB)
175 - Random Search for Best Hyperparameters Selection (32.17 MB)
176 - Selecting Best Models from Multiple ML Algorithms (42.15 MB)
177 - How Sentiment is Detected from Text Data (24.91 MB)
178 - Text Preprocessing Package Install (45.01 MB)
179 - Text Cleaning and Preprocessing (42.73 MB)
180 - Data Preparation for Model Training (10.25 MB)
181 - ML Model Building and Training (35.11 MB)
182 - Logistic Regression Model Evaluation (11.42 MB)
183 - Traning and Hyperparameters Tuning of SVM (43.49 MB)
184 - Load and Store ML Model (15.95 MB)
185 - Install Flask (32.1 MB)
186 - Run Flask Server (41.19 MB)
187 - Model Preparation with Flask (35.6 MB)
188 - Running Flask App with ML Model Part 1 (30.31 MB)
189 - Running Flask App with ML Model Part 2 (31.34 MB)
190 - Getting Familiar with Data (43.94 MB)
191 - What is MultiLabel Classification (15.73 MB)
192 - Loading Dataset (22.4 MB)
193 - MultiLabel Binarization (19.05 MB)
194 - Text to TFIDF Vectors (23.69 MB)
195 - Model Building and Jaccard Score (90.87 MB)
196 - Improving and Saving the Model (24.56 MB)
197 - What is word2vec (32.47 MB)
198 - How to Get word2vec (24.32 MB)
199 - Word Vectors with Spacy (29.4 MB)
200 - Semantic Similarity with Spacy (21.22 MB)
201 - Data Preparation (23.8 MB)
202 - Data Preprocessing (23.04 MB)
203 - Get word2vec from DataFrame (31.78 MB)
204 - Split Dataset in Train and Test (27.25 MB)
205 - ML Model Traning and Testing (24.84 MB)
206 - Support Vector Machine on word2vec (12.55 MB)
207 - Grid Search Cross Validation for Hyperparameters Tuning (25.77 MB)
208 - Test Every Machine Learning Model (39.69 MB)
209 - What is GloVe Vectors Part 1 (63.24 MB)
210 - What is GloVe Vectors Part 2 (9.38 MB)
211 - Download Pretrained GloVe Vectors (56.32 MB)
212 - Data Preparation (15.12 MB)
213 - Preprocessing and Cleaning of Emotion Text Data (15.44 MB)
214 - Load GloVe Vector (38.34 MB)
215 - Text to GloVe Vectors (67.58 MB)
216 - Text to GloVe on Pandas DataFrame (15.8 MB)
217 - ML Model Training and Testing (42.05 MB)
218 - Support Vector Machine for Emotion Recognition (12.19 MB)
219 - Predict Text Emotion with Custom Data (24.26 MB)
220 - Resume CV Parsing Introduction (30.51 MB)
221 - NER Training Introduction and Config Setup (48.26 MB)
222 - NER Training Data Preparation (43.59 MB)
223 - Training Configuration File Explanation (48.39 MB)
224 - NER Training Data Preparation Part 1 (31.78 MB)
225 - NER Training Data Preparation Part 2 (16.6 MB)
226 - NER Training with Transformers (43.3 MB)
227 - CV Parsing and NER Prediction (73.57 MB)
228 - What is Deep Learning (33.36 MB)
229 - What Makes Deep Learning StateoftheArt (23.45 MB)
230 - How Deep Learning Works (38.19 MB)
231 - Types of Neural Networks in Deep Learning ANN (36.95 MB)
232 - Types of Neural Networks in Deep Learning CNN (45.51 MB)
233 - How Deep Learning Learns (27.52 MB)
234 - What is the Difference Between Deep Learning and Machine Learning (12.1 MB)
235 - Build ANN Steps for Building Your First Model (13.52 MB)
236 - Python Package Installation (32.09 MB)
237 - Data Preprocessing (32.31 MB)
238 - Get the word2vec (9.6 MB)
239 - Train Test and Split (27.12 MB)
240 - Feature Standardization (6.64 MB)
241 - ANN Model Building and Training (21.14 MB)
242 - Confusion Matrix Plot (15.61 MB)
243 - Setting Custom Threshold (38.19 MB)
244 - 1D CNN Model Building and Training (44.08 MB)
245 - Plot Learning Curve (19.09 MB)
246 - Model Load Store and Testing (20.18 MB)
10 - Data Types (11.94 MB)
11 - Variable Assignment (8.75 MB)
12 - String Assignment (19.12 MB)
13 - List (7.86 MB)
14 - Set (6.14 MB)
15 - Tuple (7.52 MB)
16 - Dictionary (8.63 MB)
17 - Boolean and Comparison Operator (6.39 MB)
18 - Logical Operator (7.31 MB)
19 - If Else Elif (12.84 MB)
20 - Loops in Python (13.64 MB)
21 - Methods and Lambda Function (9.74 MB)
9 - Introduction (4.02 MB)
247 - Hate Speech Classification Introduction (25.81 MB)
248 - Import Python Package (30.24 MB)
249 - Dataset Balancing (43.25 MB)
250 - Text Preprocessing (16.69 MB)
251 - Text Tokenization (44.65 MB)
252 - Train Test and Split (8.94 MB)
253 - Build and Train CNN (46.85 MB)
254 - Model Testing (22.53 MB)
255 - Testing with Custom Data (16.86 MB)
256 - Load Store Model (10.69 MB)
257 - Introduction to Reccurent Neural Network RNN (24.9 MB)
258 - Types of RNN (17.38 MB)
259 - The Problem of RNNs or LongTerm Dependencies (28.33 MB)
260 - Long Short Term Memory LSTM Networks (32.94 MB)
261 - Sequence Generation Scheme (18.84 MB)
262 - Loading Poetry Dataset (1.18 MB)
263 - Tokenization (33.65 MB)
264 - Prepare Training Data (22.37 MB)
265 - Padding (14.04 MB)
266 - LSTM Model Training (19.57 MB)
267 - Poetry Generation Part 1 (21.74 MB)
268 - Poetry Generation Part 2 (24.31 MB)
269 - Disaster Tweets Dataset Understanding (64.9 MB)
270 - Download Dataset (21.73 MB)
271 - Target Class Distribution (19.41 MB)
272 - Number of Characters Distribution in Tweets (79.25 MB)
273 - Number of Words Average Words Length and Stop words Distribution in Tweets (35.32 MB)
274 - Most and Least Common Words (40.44 MB)
275 - OneShot Data Cleaning (29.98 MB)
276 - Disaster Words Visualization with Word Cloud (40.05 MB)
277 - Classification with TFIDF and SVM (34.45 MB)
279 - Classification with Word2Vec and SVM (45.36 MB)
280 - Word Embeddings and Classification with Deep Learning Part 1 (45.52 MB)
281 - Word Embeddings and Classification with Deep Learning Part 2 (55.64 MB)
283 - Python Data Types Part 1 (34.8 MB)
284 - Python Data Types Part 2 (16.8 MB)
285 - Python Data Types Conversion (20.01 MB)
286 - Mutable and Immutable Data Types (16.28 MB)
287 - String Assignment (36.35 MB)
288 - Working with String Methods (11.07 MB)
289 - String Formatting (17.24 MB)
290 - Working with List Part 1 (20.56 MB)
291 - Working with List Part 2 (33.71 MB)
292 - Working with Set Part 1 (24.11 MB)
293 - Working with Set Part 2 (29.08 MB)
294 - Working with Tuple (19.44 MB)
295 - Working with Dictionary (34.63 MB)
296 - Working with Boolean and Logical Operators (18.44 MB)
297 - Working with Truthy and Falsy Condition (3.29 MB)
297 - Working with Truthy and Falsy Condition EV (15.47 MB)
298 - If Else If Conditional Statement Part 1 (28.17 MB)
299 - If Else If Conditional Statement Part 2 (15.77 MB)
300 - Working with for and while Loops (28.84 MB)
301 - Working with continue and break statements in Loops (21.78 MB)
302 - Working with range iterators (44.51 MB)
303 - Method and Lambda Function Part 1 (15.4 MB)
304 - Method and Lambda Function Part 2 (43.06 MB)
305 - Exception Error Handling (32.84 MB)
306 - Comprehensions Methods (35.99 MB)
307 - Create Your Own Python Module (41.2 MB)
308 - Working with Dates and Times (50.5 MB)
309 - How to Create Numpy 1D 2D and 3D Array (34.3 MB)
310 - Create Array using Ones and Zeros in Numpy (17.22 MB)
311 - How to Index 1D 2D and 3D Numpy Array (18.9 MB)
312 - Working with Inf and NaN in Numpy (25.04 MB)
313 - Statistical Operations in Numpy (30.02 MB)
314 - All About Shape Reshape Ravel Flatten in Numpy (25.01 MB)
315 - All About the Sequence and Repetitions in Numpy (36.62 MB)
316 - Random Numbers in Numpy (62.05 MB)
317 - Importance of Where ArgMax ArgMin in ML Models (24.45 MB)
318 - Save Preprocessed Arrays with File Read and Write (37.84 MB)
319 - Concatenate and Sorting (26.94 MB)
320 - Working with Dates in Numpy (23.61 MB)
321 - Pandas File Reading from Local and Online (24.87 MB)
322 - Write File with Custom Column Separator (60.24 MB)
323 - Selecting Subset of Columns and Doing Arithmetic Operations on Columns (38.81 MB)
324 - Using Lambda Function with Pandas Dataframe (22.77 MB)
325 - Column Renaming and Selection Based on Data Types (66.74 MB)
326 - Head Tail Sample Frac and Random State in Pandas (36.93 MB)
327 - Dataframe Slicing (32.34 MB)
328 - Getting Information About Dataframe with Info Shape Duplicated and Drop (53.55 MB)
329 - Dataframe Filtering Based on Condition on Columns (17.7 MB)
330 - Handling NaN and Null Values in Dataframe (63.6 MB)
331 - Data Imputation with Pandas Dataframe (29.44 MB)
332 - Lambda Function for Feature Engineering Part 1 (31.79 MB)
333 - Lambda Function for Feature Engineering Part 2 (23.39 MB)
334 - Importance of Groupby and Aggregation in Smart Data Imputation (40.47 MB)
335 - Multi Columns Grouping and Aggregation (33.88 MB)
336 - Pandas Inner Merging (23.17 MB)
337 - Pandas Left Right Outer and Concat Joining (57.43 MB)
338 - Sorting Pandas Dataframe (38.24 MB)
339 - Handling Categorical Data (27.82 MB)
340 - Handling Dates with Pandas (63.33 MB)
341 - Create Your First File in write Mode (24.27 MB)
342 - Many Ways to Write Multiple Lines (69.89 MB)
343 - Read Your First File (19.71 MB)
344 - How to Write List in File and Evaluate It in Read Mode (24.23 MB)
345 - Reading CSV and TSV Files (57.56 MB)
346 - Read Large Files in Chunks (73.03 MB)
347 - ReadWrite Multisheet Excel File (25.97 MB)
348 - ReadWrite json Data with Pandas (29.34 MB)
349 - How to Read Nested json Data in Dataframe (20.06 MB)
350 - Covert PDF data into Text Data for ML Models (86.95 MB)
351 - Read Audio from Microphone to Convert it into Text Data (63.45 MB)
352 - Read Audio from Local File and Convert into Text (25.88 MB)
353 - Introduction (23.58 MB)
354 - Testing Start of Character Set with Caret Metacharacter (49.77 MB)
355 - Matching End of String with and use of Other Metacharacters (19.81 MB)
356 - Quantifiers Match with Regex101 (24.1 MB)
357 - Matching Specific Set of Characters like 3D or A4 (22.11 MB)
358 - Excluding Specific Set of Characters (38.62 MB)
359 - Using Escape Chars for Quick Match and Use Regex101 to Find Prebuilt Regex (53.39 MB)
360 - Matching a Pattern with rematch in Python (46.44 MB)
361 - Searching for a Pattern with research in Python (24.28 MB)
362 - Finding All Matches with refindall in Python (29.85 MB)
363 - Splitting a String with resplit in Python (21.78 MB)
364 - Replacing Matches with resub in Python (32.25 MB)
365 - Introduction to Spacy (49.7 MB)
366 - Spacy Tokenization Part 1 (40.72 MB)
367 - Spacy Tokenization Part 2 (46.58 MB)
368 - POS Tagging (45.5 MB)
369 - Dependency Parsing Part 1 (69.41 MB)
370 - Dependency Parsing Part 2 (68.56 MB)
371 - Lemmatization Convert Tokens into Base Form (38.76 MB)
372 - Named Entity Recognition NER with PreBuilt Model (74.91 MB)
373 - Vector Similarity Introduction (60.26 MB)
374 - Measure Cosine Similarity Between Words (35.55 MB)
22 - Introduction (5.72 MB)
23 - Array (23.49 MB)
24 - NaN and INF (16.73 MB)
25 - Statistical Operations (6.14 MB)
26 - Shape Reshape Ravel Flatten (5.99 MB)
27 - Sequence Repetitions and Random Numbers (41.19 MB)
28 - Where ArgMax ArgMin (10.27 MB)
29 - File Read and Write (19.97 MB)
30 - Concatenate and Sorting (8.7 MB)
31 - Working with Dates (5.85 MB)
32 - Introduction (4.92 MB)
33 - DataFrame and Series (10.51 MB)
34 - File Reading and Writing (39.01 MB)
35 - Info Shape Duplicated and Drop (17.63 MB)
36 - Columns (5.86 MB)
37 - NaN and Null Values (22.35 MB)
38 - Imputation (18.82 MB)
39 - Lambda Function (11.97 MB)
40 - Introduction to NLP (9.69 MB)
41 - Spacy 3 Introduction (51.91 MB)
42 - Spacy 3 Tokenization (40.29 MB)
43 - POS Tagging in Spacy 3 (96.96 MB)
44 - Visualizing Dependency Parsing with Displacy (83.96 MB)
45 - Sentence Boundary Detection (30.96 MB)
46 - Stop Words in Spacy 3 (34.52 MB)
47 - Lemmatization in Spacy 3 (21.53 MB)
48 - Stemming in NLTK Lemmatization vs Stemming in NLP (13.86 MB)
49 - Word Frequency Counter (24.96 MB)
50 - Rule Based Matching in Spacy Part 1 (149.26 MB)
51 - Rule Based Token Matching Examples Part 2 (41.21 MB)
52 - Rule Based Phrase Matching in Spacy (47.73 MB)
53 - Rule Based Entity Matching in Spacy (37.22 MB)
54 - NER Named Entity Recognition in Spacy 3 Part 1 (45.5 MB)
55 - NER Named Entity Recognition in Spacy 3 Part 2 (82.32 MB)
56 - Word to Vector word2vec and Sentence Similarity in Spacy (65.12 MB)
57 - Regular Expression Part 1 (37.08 MB)
58 - Regular Expression Part 2 (18.01 MB)
59 - String Formatting (26.38 MB)
60 - Working with open Files in write Mode Part 1 (20.2 MB)
61 - Working with open Files in write Mode Part 2 (23.18 MB)
62 - Working with open Files in write Mode Part 3 (6.74 MB)
63 - Read and Evaluate the Files (31.45 MB)
64 - Reading and Writing CSV and TSV Files with Pandas (32.02 MB)
65 - Reading and Writing XLSX Files with Pandas (16.31 MB)
66 - Reading and Writing JSON Files (33.87 MB)
67 - Reading Files from URL Links (4.97 MB)
68 - Extract Text Data From PDF (62.7 MB)
69 - Record the Audio and Convert to Text (48.96 MB)
70 - Convert Audio in Text Data (4.36 MB)
71 - Text to Speech Generation (24.21 MB)
72 - Introduction (40.65 MB)
73 - Word Counts (16.27 MB)
74 - Characters Counts (11.37 MB)
75 - Average Word Length (7.72 MB)
76 - Stop Words Count (40.12 MB)
77 - Count hashtag and mentions (16.44 MB)
78 - Numeric Digit Count (15.97 MB)
79 - Upper case Words Count (9.02 MB)
80 - Lower case Conversion (10.14 MB)
81 - Contraction to Expansion (41.05 MB)
82 - Count and Remove Emails (26.27 MB)
83 - Count and Remove URLs (44.02 MB)
84 - Remove RT from Tweeter Data (13.43 MB)
85 - Special Chars Removal and Punctuation Removal (13.22 MB)
86 - Remove Multiple Spaces (2.95 MB)
87 - Remove HTML Tags (15.61 MB)
88 - Remove Accented Chars (5.72 MB)
89 - Remove Stop Words (7.32 MB)
90 - Convert into Base or Root Form of Words (23.43 MB)
91 - Common Words Removal (14.66 MB)
92 - Rare Words Removal (7.64 MB)
93 - Word Cloud Visualization (16.8 MB)
94 - Spelling Correction (9.64 MB)
95 - Tokenization with TextBlob (3.58 MB)
96 - Nouns Detection (2.63 MB)
97 - Language Translation and Detection (12.53 MB)
98 - Sentiment Prediction with TextBlob (6.31 MB)
100 - Readme and License File Preparation (21.71 MB)
101 - Setuppy Preparation (75.32 MB)
102 - Utilspy Code Along Part 1 (42.99 MB)
103 - Utilspy Code Along Part 2 (49.73 MB)
104 - Utilspy Code Along Part 3 (55.8 MB)
105 - Utilspy Code Along Part 4 (65.86 MB)
106 - initpy Code Along (61.91 MB)
107 - GitHub Account Setup and Package Upload (39.78 MB)
108 - SSH Key Setup for GitHub (40.61 MB)
109 - Install Preprocess Python Package (16.81 MB)
110 - Removing the Errors Part 1 (12.03 MB)
111 - Removing the Errors Part 2 (104.37 MB)
112 - Testing the Package (21.73 MB)
99 - Code Files Setup (35.97 MB)
113 - Logistic Regression Intuition (20.25 MB)
114 - Support Vector Machine Intuition (27.03 MB)
115 - Decision Tree Intuition (13.45 MB)
116 - Random Forest Intuition (10.26 MB)
117 - L2 Regularization (21.51 MB)
118 - L1 Regularization (8.43 MB)
119 - Model Evaluation Metrics Accuracy Precision Recall and Confusion Matrix (14.22 MB)
120 - Model Evaluation Metrics ROC and AUC (6.07 MB)
121 - Code Along in Python Part 1 (26.15 MB)
122 - Code Along in Python Part 2 (53.76 MB)
123 - Code Along in Python Part 3 (36.29 MB)
124 - Code Along in Python Part 4 (56.69 MB)]
Screenshot
[Image: gnoLIhzk_o.jpg]