https://img100.pixhost.to/images/404/537368816_que-es-udemy-analisis-opiniones.jpg
12.15 GB | 10min 38s | mp4 | 1280X720 | 16:9
Genre: eLearning | Language : English
Files Included :
1 Machine Learning Intuition.mp4 (11.57 MB)
2 Course Overview.mp4 (9.16 MB)
4 Install Anaconda and Python 3 on Windows 10.mp4 (26.21 MB)
5 Install Anaconda and Python 3 on Ubuntu Machine.mp4 (13.7 MB)
6 Install Anaconda and Python 3 on Mac Machine.mp4 (72.1 MB)
7 Install VS Code.mp4 (40.61 MB)
8 Install Git Bash and Commander Terminal.mp4 (32.05 MB)
9 Jupyter Notebook Shortcuts.mp4 (16.72 MB)
1 Python Data Types Part 1.mp4 (41.03 MB)
10 Working with Set Part 1.mp4 (24.3 MB)
11 Working with Set Part 2.mp4 (29.37 MB)
12 Working with Tuple.mp4 (19.62 MB)
13 Working with Dictionary.mp4 (34.96 MB)
14 Working with Boolean and Logical Operators.mp4 (18.67 MB)
15 Working with Truthy and Falsy Condition.mp4 (18.6 MB)
16 If Else If Conditional Statement Part 1.mp4 (28.45 MB)
17 If Else If Conditional Statement Part 2.mp4 (15.97 MB)
18 Working with for and while() Loops.mp4 (29.11 MB)
19 Working with continue and break statements in Loops.mp4 (22.05 MB)
2 Python Data Types Part 2.mp4 (17 MB)
20 Working with range iterators.mp4 (44.79 MB)
21 Method and Lambda Function Part 1.mp4 (15.56 MB)
22 Method and Lambda Function Part 2.mp4 (43.48 MB)
23 Exception (Error) Handling.mp4 (33.17 MB)
24 Comprehensions Methods.mp4 (36.3 MB)
25 Create Your Own Python Module.mp4 (41.56 MB)
26 Working with Dates and Times.mp4 (50.79 MB)
3 Python Data Types Conversion.mp4 (20.21 MB)
4 Mutable and Immutable Data Types.mp4 (16.47 MB)
5 String Assignment.mp4 (36.66 MB)
6 Working with String Methods.mp4 (11.21 MB)
7 String Formatting.mp4 (17.45 MB)
8 Working with List Part 1.mp4 (20.81 MB)
9 Working with List Part 2.mp4 (34.03 MB)
1 How to Create Numpy 1D, 2D and 3D Array.mp4 (58.34 MB)
10 Save Preprocessed Arrays with File Read and Write.mp4 (38.16 MB)
11 Concatenate and Sorting.mp4 (27.21 MB)
12 Working with Dates in Numpy.mp4 (23.82 MB)
2 Create Array using Ones and Zeros in Numpy.mp4 (17.42 MB)
3 How to Index 1D, 2D, and 3D Numpy Array.mp4 (19.19 MB)
4 Working with Inf and NaN in Numpy.mp4 (25.38 MB)
5 Statistical Operations in Numpy.mp4 (30.32 MB)
6 All About Shape, Reshape, Ravel, Flatten in Numpy.mp4 (25.29 MB)
7 All About the Sequence and Repetitions in Numpy.mp4 (36.86 MB)
8 Random Numbers in Numpy.mp4 (62.37 MB)
9 Importance of Where(), ArgMax(), ArgMin() in ML Models.mp4 (24.71 MB)
1 Pandas File Reading from Local and Online.mp4 (66.41 MB)
10 Handling NaN and Null Values in Dataframe.mp4 (63.97 MB)
11 Data Imputation with Pandas Dataframe.mp4 (29.79 MB)
12 Lambda Function for Feature Engineering Part 1.mp4 (31.99 MB)
13 Lambda Function for Feature Engineering Part 2.mp4 (23.59 MB)
14 Importance of Groupby and Aggregation in Smart Data Imputation.mp4 (40.73 MB)
15 Multi Columns Grouping and Aggregation.mp4 (34.17 MB)
16 Pandas Inner Merging.mp4 (23.37 MB)
17 Pandas Left, Right, Outer and Concat Joining.mp4 (57.73 MB)
18 Sorting Pandas Dataframe.mp4 (38.44 MB)
19 Handling Categorical Data.mp4 (28.1 MB)
2 Write File with Custom Column Separator.mp4 (60.44 MB)
20 Handling Dates with Pandas.mp4 (63.6 MB)
3 Selecting Subset of Columns and Doing Arithmetic Operations on Columns.mp4 (39.03 MB)
4 Using Lambda Function with Pandas Dataframe.mp4 (22.99 MB)
5 Column Renaming and Selection Based on Data Types.mp4 (67.06 MB)
6 Head, Tail, Sample, Frac and Random State in Pandas.mp4 (37.12 MB)
7 Dataframe Slicing.mp4 (32.55 MB)
8 Getting Information About Dataframe with Info, Shape, Duplicated, and Drop.mp4 (53.84 MB)
9 Dataframe Filtering Based on Condition on Columns.mp4 (17.91 MB)
1 Create Your First File in write Mode.mp4 (49.36 MB)
10 Covert PDF data into Text Data for ML Models.mp4 (87.21 MB)
11 Read Audio from Microphone to Convert it into Text Data.mp4 (63.7 MB)
12 Read Audio from Local File and Convert into Text.mp4 (26.05 MB)
2 Many Ways to Write Multiple Lines.mp4 (70.17 MB)
3 Read Your First File.mp4 (19.88 MB)
4 How to Write List in File and Evaluate It in Read Mode.mp4 (24.43 MB)
5 Reading CSV and TSV Files.mp4 (57.82 MB)
6 Read Large Files in Chunks.mp4 (73.26 MB)
7 ReadWrite Multisheet Excel File.mp4 (26.17 MB)
8 ReadWrite json Data with Pandas.mp4 (29.54 MB)
9 How to Read Nested json Data in Dataframe.mp4 (20.15 MB)
1 Introduction.mp4 (35.32 MB)
10 Finding All Matches with re findall() in Python.mp4 (29.99 MB)
11 Splitting a String with re split() in Python.mp4 (21.88 MB)
12 Replacing Matches with re sub() in Python.mp4 (32.47 MB)
2 Testing Start of Character Set with ^ (Caret) Metacharacter.mp4 (50.05 MB)
3 Matching End of String with $ and use of Other Metacharacters.mp4 (20.04 MB)
4 Quantifiers Match with Regex101.mp4 (24.38 MB)
5 Matching Specific Set of Characters like 3D or A4.mp4 (22.35 MB)
6 Excluding Specific Set of Characters.mp4 (38.89 MB)
7 Using Escape Chars for Quick Match and Use Regex101 to Find Prebuilt Regex.mp4 (53.66 MB)
8 Matching a Pattern with re match() in Python.mp4 (46.66 MB)
9 Searching for a Pattern with re search() in Python.mp4 (24.44 MB)
1 Introduction to Spacy.mp4 (62.61 MB)
10 Measure Cosine Similarity Between Words.mp4 (35.83 MB)
2 Spacy Tokenization Part 1.mp4 (40.92 MB)
3 Spacy Tokenization Part 2.mp4 (46.86 MB)
4 POS Tagging.mp4 (45.77 MB)
5 Dependency Parsing Part 1.mp4 (69.7 MB)
6 Dependency Parsing Part 2.mp4 (68.81 MB)
7 Lemmatization (Convert Tokens into Base Form).mp4 (39 MB)
8 Named Entity Recognition (NER) with Pre-Built Model.mp4 (75.23 MB)
9 Vector Similarity Introduction.mp4 (60.49 MB)
1 NLTK Tokenization with PUNKT Model.mp4 (36.05 MB)
2 Filtering Stop Words with BERT Text Data.mp4 (66.85 MB)
3 Stemming.mp4 (35.38 MB)
4 POS Tagging with NLTK.mp4 (33.45 MB)
5 Lemmatization with wordnet in NLTK.mp4 (19.37 MB)
6 Chunking Noun Phrases.mp4 (72.64 MB)
7 Using Named Entity Recognition (NER) with NLTK.mp4 (57.16 MB)
8 Finding Collocations - Finding Words Occurring Together.mp4 (27.41 MB)
1 Introduction.mp4 (29.54 MB)
10 Expand Contracted Form of Words.mp4 (82.5 MB)
11 Extract, Count and Remove Emails.mp4 (80.22 MB)
12 Extract, Count and Remove URLs.mp4 (44.9 MB)
13 Remove RT (Retweet) from Tweets.mp4 (23.68 MB)
14 Remove HTML tags.mp4 (68.16 MB)
15 Remove Accented Chars.mp4 (18.81 MB)
16 Remove Mentions, Special Chars and Punctuation.mp4 (44.92 MB)
17 Remove Repeated Characters.mp4 (18.99 MB)
18 Remove Stop Words.mp4 (14.81 MB)
19 Convert into base or root form of word.mp4 (37.66 MB)
2 Tweets Data Loader.mp4 (32.09 MB)
20 Common Words Removal.mp4 (25.8 MB)
21 Rare Words Removal.mp4 (10.81 MB)
22 Word Cloud Visualization.mp4 (62.68 MB)
23 Spelling Correction.mp4 (72.83 MB)
24 Extract Noun Phrases Like Google Map Reviews.mp4 (77.48 MB)
25 N-Gram and Word Cloud.mp4 (46.34 MB)
26 Word Counts, Singularize, Pluralize, Lemmatize and so much more.mp4 (66.75 MB)
27 Use TextBlob's Inbuilt Sentiment Classifier.mp4 (51.06 MB)
28 Language Translation and Detection.mp4 (38.69 MB)
3 Tweet Characters Count.mp4 (16.71 MB)
4 Word Counts and Average Word Length.mp4 (33.42 MB)
5 Stop Words Count.mp4 (19.43 MB)
6 Count #HashTags and @Mentions.mp4 (37.12 MB)
7 Count numeric digits present in tweets.mp4 (27.35 MB)
8 UPPER case words count.mp4 (17.34 MB)
9 Preprocessing and Cleaning - Lower Case Conversion.mp4 (31.46 MB)
1 Introduction.mp4 (8.65 MB)
10 Putting Text Preprocessing Codes Together Part 2.mp4 (33.34 MB)
11 Putting Text Preprocessing Codes Together Part 3.mp4 (64.13 MB)
12 Testing Our Python Package Locally - It Worked.mp4 (110.11 MB)
2 Package Files and Directory Setup.mp4 (38.41 MB)
3 Readme md and License Preparation.mp4 (85.71 MB)
4 Why Setup py is Needed.mp4 (57.76 MB)
5 Complete Package Setup.mp4 (112.09 MB)
6 Prepare Empty Python Package and Do Testing.mp4 (109.76 MB)
7 Put Your First Method Inside Python Package.mp4 (40.89 MB)
8 Putting General Feature Extraction in Your Python Package.mp4 (37.3 MB)
9 Putting Text Preprocessing Codes Together Part 1.mp4 (35.23 MB)
1 Introduction.mp4 (11.37 MB)
2 Importance of Uploading Package to GitHub and PyPi Install Git for Windows.mp4 (33.62 MB)
3 Create and Add SSH Key to GitHub Account.mp4 (67.34 MB)
4 Upload Package to GitHub.mp4 (85.75 MB)
5 Install Your Package from GitHub and Share Install Instruction with Anyone.mp4 (90.6 MB)
6 Adding Package Documentation, Use Cases and One Shot Cleaning.mp4 (102.6 MB)
7 Testing Your Python Package with GitHub Installation Method.mp4 (67.58 MB)
8 Create PyPi Account to Upload Your Package.mp4 (32.6 MB)
9 Upload Your Package to PyPi (Pip) Server for Open Source Use.mp4 (85.42 MB)
1 Introduction.mp4 (23.44 MB)
2 Why Machine Learning Algorithms.mp4 (18.9 MB)
3 Types of Machine Learning Systems.mp4 (29.98 MB)
4 What is Linear Regression.mp4 (38.2 MB)
5 Linear Regression Cost Function.mp4 (86.58 MB)
6 Evaluation Metrics for Regression.mp4 (38.12 MB)
7 Load California Housing Dataset.mp4 (65.2 MB)
8 Linear Regression Coding on Housing Dataset.mp4 (66.58 MB)
1 Introduction.mp4 (45.98 MB)
10 Calculate Precision, Recall and F1-Score in Classification Report by Yourself.mp4 (58.58 MB)
11 Complete Your ROC-AUC and Precision-Recall Curve Coding.mp4 (27.65 MB)
12 Fundamental Understanding of Model Regularization.mp4 (42.25 MB)
13 Why L1 Regularization Produces Sparse Solution but not L2.mp4 (36.01 MB)
14 Regularization Coding for Classification Model.mp4 (84.28 MB)
2 Logistic Regression and Related Interview Questions on Log Odds and Logits.mp4 (43.34 MB)
3 Logistic Regression Cost Function - Log Loss and Entropy Discussions.mp4 (61.96 MB)
4 Logistic Regression vs Linear Regression Discussions for Job Interviews.mp4 (65.34 MB)
5 Load Breast Cancer Dataset.mp4 (54.62 MB)
6 Logistic Regression Coding for Breast Cancer Classification.mp4 (76.85 MB)
7 Interview Question - Is Accuracy Good Metric to Measure Model Performance.mp4 (13.46 MB)
8 Explain Confusion Matrix, Accuracy, Precision, Recall, F1-Score to Interviewer.mp4 (81.81 MB)
9 Explain Roc-Auc and Precision-Recall Curve to Your Interviewer.mp4 (59.53 MB)
1 Introduction.mp4 (51.42 MB)
10 Reduce Decision Tree Overfitting with Regularization.mp4 (88.17 MB)
11 Visualize Decision Tree Pruning Process.mp4 (89.07 MB)
12 How Random Forest Works.mp4 (71.36 MB)
13 Training Random Forest in Regularized Mode.mp4 (57.45 MB)
14 What is Cross Validation.mp4 (52.75 MB)
15 K-Fold Cross Validation.mp4 (108.67 MB)
16 Leave-One-Out Cross Validation (LOOCV) Coding.mp4 (38.07 MB)
17 Hyperparameter Tuning Introduction.mp4 (47.8 MB)
18 GridSearchCV Parameters Preparations.mp4 (62.6 MB)
19 Finding Best Parameters with GridSearchCV Coding.mp4 (73.23 MB)
2 What is Support Vector Machine (SVM) and Hinge Loss.mp4 (79.35 MB)
20 RandomizedSearchCV for Faster Hyper-Parameter Tuning.mp4 (60.93 MB)
3 SVM Coding with Regularization.mp4 (85.34 MB)
4 What is KNN and Its Cost Function.mp4 (63.66 MB)
5 KNN Coding.mp4 (51.52 MB)
6 How Decision Tree Works.mp4 (44.15 MB)
7 Calculate Gini Impurity and Entropy By Hand with An Example.mp4 (68.61 MB)
8 Decision Tree Coding.mp4 (48.47 MB)
9 Deep Dive in Decision Tree Parameters which Controls Overfitting.mp4 (97.17 MB)
1 Text Feature Extraction Intuition Part 1.mp4 (18.09 MB)
10 Data Preparation for Training.mp4 (28.11 MB)
11 Build and Train SVM and Random Forest Models.mp4 (20.86 MB)
12 Test Your Model with Real Data.mp4 (9.24 MB)
2 Text Feature Extraction Intuition Part 2.mp4 (39.86 MB)
3 Bag of Words (BoW) Code Along in Python.mp4 (21.04 MB)
4 Term Frequency (TF) Code Along in Python.mp4 (20.45 MB)
5 Inverse Document Frequency (IDF) Code Along in Python.mp4 (28.71 MB)
6 TFIDF Code Along in Python.mp4 (15.43 MB)
7 Load Spam Dataset.mp4 (18.36 MB)
8 Balance Dataset.mp4 (16.41 MB)
9 Exploratory Data Analysis (EDA).mp4 (22.77 MB)
1 How Sentiment is Detected from Text Data.mp4 (25.04 MB)
2 Text Preprocessing Package Install.mp4 (45.18 MB)
3 Text Cleaning and Preprocessing.mp4 (42.96 MB)
4 Data Preparation for Model Training.mp4 (10.32 MB)
5 ML Model Building and Training.mp4 (35.4 MB)
6 Logistic Regression Model Evaluation.mp4 (11.49 MB)
7 Traning and Hyperparameters Tuning of SVM.mp4 (43.67 MB)
8 Load and Store ML Model.mp4 (18.86 MB)
1 Install Flask.mp4 (32.25 MB)
2 Run Flask Server.mp4 (41.4 MB)
3 Model Preparation with Flask.mp4 (35.82 MB)
4 Running Flask App with ML Model Part 1.mp4 (30.48 MB)
5 Running Flask App with ML Model Part 2.mp4 (34.13 MB)
1 Getting Familiar with Data.mp4 (44.1 MB)
2 What is Multi-Label Classification.mp4 (15.83 MB)
3 Loading Dataset.mp4 (22.6 MB)
4 Multi-Label Binarization.mp4 (19.19 MB)
5 Text to TFIDF Vectors.mp4 (23.87 MB)
6 Model Building and Jaccard Score.mp4 (91.3 MB)
7 Improving and Saving the Model.mp4 (24.79 MB)
1 What is word2vec.mp4 (32.65 MB)
10 Support Vector Machine on word2vec.mp4 (17.24 MB)
11 Grid Search Cross Validation for Hyperparameters Tuning.mp4 (25.93 MB)
12 Test Every Machine Learning Model.mp4 (39.86 MB)
2 How to Get word2vec.mp4 (24.5 MB)
3 Word Vectors with Spacy.mp4 (29.59 MB)
4 Semantic Similarity with Spacy.mp4 (21.37 MB)
5 Data Preparation.mp4 (23.94 MB)
6 Data Preprocessing.mp4 (23.15 MB)
7 Get word2vec from DataFrame.mp4 (31.96 MB)
8 Split Dataset in Train and Test.mp4 (27.41 MB)
9 ML Model Traning and Testing.mp4 (24.96 MB)
1 What is GloVe Vectors Part 1.mp4 (63.44 MB)
10 Support Vector Machine for Emotion Recognition.mp4 (12.29 MB)
11 Predict Text Emotion with Custom Data.mp4 (24.38 MB)
2 What is GloVe Vectors Part 2.mp4 (9.48 MB)
3 Download Pre-trained GloVe Vectors.mp4 (56.52 MB)
4 Data Preparation.mp4 (15.23 MB)
5 Preprocessing and Cleaning of Emotion Text Data.mp4 (15.57 MB)
6 Load GloVe Vector.mp4 (38.54 MB)
7 Text to GloVe Vectors.mp4 (67.85 MB)
8 Text to GloVe on Pandas DataFrame.mp4 (15.87 MB)
9 ML Model Training and Testing.mp4 (42.3 MB)
1 Resume (CV) Parsing Introduction.mp4 (30.65 MB)
2 NER Training Introduction and Config Setup.mp4 (48.45 MB)
3 NER Training Data Preparation.mp4 (43.82 MB)
4 Training Configuration File Explanation.mp4 (48.6 MB)
5 NER Training Data Preparation Part 1.mp4 (32.04 MB)
6 NER Training Data Preparation Part 2.mp4 (16.85 MB)
7 NER Training with Transformers.mp4 (43.58 MB)
8 CV Parsing and NER Prediction.mp4 (73.97 MB)
1 What is Deep Learning.mp4 (33.54 MB)
10 Data Preprocessing.mp4 (32.54 MB)
11 Get the word2vec.mp4 (9.7 MB)
12 Train Test and Split.mp4 (27.31 MB)
13 Feature Standardization.mp4 (6.71 MB)
14 ANN Model Building and Training.mp4 (21.36 MB)
15 Confusion Matrix Plot.mp4 (15.74 MB)
16 Setting Custom Threshold.mp4 (38.47 MB)
17 1D CNN Model Building and Training.mp4 (44.38 MB)
18 Plot Learning Curve.mp4 (19.2 MB)
19 Model Load, Store and Testing.mp4 (20.43 MB)
2 What Makes Deep Learning State-of-the-Art.mp4 (23.65 MB)
3 How Deep Learning Works.mp4 (38.38 MB)
4 Types of Neural Networks in Deep Learning - ANN.mp4 (37.15 MB)
5 Types of Neural Networks in Deep Learning - CNN.mp4 (45.72 MB)
6 How Deep Learning Learns.mp4 (27.68 MB)
7 What is the Difference Between Deep Learning and Machine Learning.mp4 (12.2 MB)
8 Build ANN - Steps for Building Your First Model.mp4 (13.69 MB)
9 Python Package Installation.mp4 (32.24 MB)
1 Hate Speech Classification Introduction.mp4 (25.94 MB)
10 Load Store Model.mp4 (10.77 MB)
2 Import Python Package.mp4 (30.43 MB)
3 Dataset Balancing.mp4 (43.52 MB)
4 Text Preprocessing.mp4 (16.87 MB)
5 Text Tokenization.mp4 (44.96 MB)
6 Train Test and Split.mp4 (9.06 MB)
7 Build and Train CNN.mp4 (47.12 MB)
8 Model Testing.mp4 (22.7 MB)
9 Testing with Custom Data.mp4 (17.01 MB)
1 Introduction to Reccurent Neural Network (RNN).mp4 (25.06 MB)
10 LSTM Model Training.mp4 (19.79 MB)
11 Poetry Generation Part 1.mp4 (21.97 MB)
12 Poetry Generation Part 2.mp4 (24.54 MB)
2 Types of RNN.mp4 (17.49 MB)
3 The Problem of RNN's or Long-Term Dependencies.mp4 (28.55 MB)
4 Long Short Term Memory (LSTM) Networks.mp4 (33.2 MB)
5 Sequence Generation Scheme.mp4 (19.03 MB)
6 Loading Poetry Dataset.mp4 (14.79 MB)
7 Tokenization.mp4 (33.97 MB)
8 Prepare Training Data.mp4 (22.57 MB)
9 Padding.mp4 (14.29 MB)
1 Disaster Tweets Dataset Understanding.mp4 (65.12 MB)
11 Classification with Word2Vec and SVM.mp4 (45.67 MB)
12 Word Embeddings and Classification with Deep Learning Part 1.mp4 (45.81 MB)
13 Word Embeddings and Classification with Deep Learning Part 2.mp4 (56.01 MB)
2 Download Dataset.mp4 (21.87 MB)
3 Target Class Distribution.mp4 (19.64 MB)
4 Nu
Видео
