Packt Step by Step Machine Learning with Python

/1 - Getting Started with Python and Machine Learning/

Installing Software and Setting Up.mp4

Introduction to Machine Learning.mp4

The Course Overview.mp4

/2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/

Clustering.mp4

Data Preprocessing.mp4

Getting the Newsgroups Data.mp4

Machine Learning with Python.mp4

Thinking about Features.mp4

Topic Modeling.mp4

Touring Powerful NLP Libraries in Python.mp4

Understanding NLP.mp4

Visualization.mp4

/3 - Spam Email Detection with Naïve Bayes#/

Classifier Performance Evaluation.mp4

Exploring Naïve Bayes.mp4

Getting Started with Classification.mp4

Model Tuning and cross-validation.mp4

The Mechanics of Naïve Bayes.mp4

The Naïve Bayes Implementation.mp4

/4 - News Topic Classification with Support Vector Machine/

Choosing Between the Linear and the RBF Kernel.mp4

Fetal State Classification with SVM.mp4

News topic Classification with Support Vector Machine.mp4

Recap and Inverse Document Frequency.mp4

The Implementations of SVM.mp4

The Kernels of SVM.mp4

The Mechanics of SVM.mp4

/5 - Click-Through Prediction with Tree-Based Algorithms/

Brief Overview of Advertising Click-Through Prediction.mp4

Click-Through Prediction with Decision Tree.mp4

Decision Tree Classifier.mp4

Random Forest - Feature Bagging of Decision Tree.mp4

The Implementations of Decision Tree.mp4

/6 - Click-Through Prediction with Logistic Regression#/

Click-Through Prediction with Logistic Regression by Gradient Descent.mp4

Feature Selection via Random Forest.mp4

Logistic Regression Classifier.mp4

One-Hot Encoding - Converting Categorical Features to Numerical.mp4

/7 - Stock Price Prediction with Regression Algorithms/

Brief Overview of the Stock Market And Stock Price.mp4

Data Acquisition and Feature Generation.mp4

Decision Tree Regression.mp4

Linear Regression.mp4

Machine Learning with Python.mp4

Predicting Stock Price with Regression Algorithms.mp4

Regression Performance Evaluation.mp4

Stock Price Prediction with Regression Algorithms.mp4

Support Vector Regression.mp4

/8 - Best Practices/

Best Practices in Data Preparation Stage.mp4

Best Practices in the Deployment and Monitoring Stage.mp4

Best Practices in the Model Training, Evaluation, and Selection Stage.mp4

Best Practices in the Training Sets Generation Stage.mp4

/V09050_Code/V09050_Code/Section 02/.ropeproject/

config.py

globalnames

history

objectdb

/V09050_Code/V09050_Code/Section 02/

0_getting.py

1histogram.py

2clean_words.py

3post_clustering.py

4topic_model.py

/V09050_Code/V09050_Code/Section 03/

.DS_Store

email_spam.py

/V09050_Code/V09050_Code/Section 04/

.DS_Store

1email_spam_tfidf_submit.py

2topic_categorization.py

3plot_rbf_kernels.py

4ctg.py

CTG.xls

/V09050_Code/V09050_Code/Section 05/

.DS_Store

1decision_tree_submit.py

2avazu_ctr.py

/V09050_Code/V09050_Code/Section 06/

.DS_Store

1one_hot_encode.py

2logistic_function.py

3logistic_regression_from_scratch.py

4random_forest_feature_selection.py

5scikit_logistic_regression.py

/V09050_Code/V09050_Code/Section 07/

.DS_Store

198810101_20151231.csv

1stock_price_prediction.py

20051201_20151210.csv

2linear_regression.py

3decision_tree_regression.py

4support_vector_regression.py

/V09050_Code/V09050_Code/Section 08/

.DS_Store

1imputation.py

2feature_selection.py

3dimensionality_reduction.py

4generic_feature_engineering.py

5save_reuse_monitor_model.py

Total files 86