by Farhan Nazar Zaidi | Released August 2018 | ISBN: 9781789137132

Machine Learning projects with Python's own Scikit-learn on real-world datasetsAbout This VideoApply the Machine Learning techniques and processes and widely used functions Scikit-learn has to offer for a particular task.With practical hands-on approach, build strong intelligent ML systems with minimal effortExplore datasets from a diverse set of ML problem domains, applying different models and techniques.In DetailScikit-learn is arguably the most popular Python library for Machine Learning today. Thousands of Data Scientists and Machine Learning practitioners use it for day to day tasks throughout a Machine Learning project's life cycle. Due to its popularity and coverage of a wide variety of ML models and built-in utilities, jobs for Scikit-learn are in high demand, both in industry and academia.If you're an aspiring machine learning engineer ready to take real-world projects head-on, Hands-on Scikit-Learn for Machine Learning will walk you through the most commonly used models, libraries, and utilities offered by Scikit-learn.By the end of the course, you will have a set of ML problem-solving tools in the form of code modules and utility functions based on Scikit-learn in one place, instead of spread over several books and courses, which you can easily use on real-world projects and data sets.All the code and supporting files for this course are available on Github at: https://github.com/PacktPublishing/Hands-on-Scikit-learn-for-Machine-Learning-V- Show and hide more Publisher Resources Download Example Code

1. Chapter 1 : Getting Started with a Simple ML Model in Scikit-learn
   • The Course Overview 00:07:34
   • Course Objectives, Software Installation, and Setup 00:10:22
   • Overview of Scikit-learn 00:09:07
   • Scikit-learn Programming Workflow Example 00:07:15
   • Applying a KNN Model on Cancer Dataset 00:09:52
   • Improving the KNN Performance on Cancer Dataset 00:08:26
2. Chapter 2 : Classification Models
   • Linear and Logistic Regression 00:14:46
   • Evaluating Classification Models 00:15:06
   • Logistic Regression and Evaluation with Scikit-learn 00:11:19
   • Decision Trees 00:10:57
   • Bagging, Boosting, and Random Forests 00:07:27
   • Applying Ensemble Methods with Scikit-learn 00:07:20
   • Support Vector Machines 00:09:11
   • Applying Support Vector Machines Classifier with Scikit-learn 00:04:43
   • Multi-class Classification Example with Scikit-learn 00:07:51
3. Chapter 3 : Supervised Machine Learning – Regression
   • Downloading and Inspecting the Dataset 00:12:03
   • Handling Categorical Features and Missing Values 00:05:39
   • Creating Train and Test Sets and Finding Correlation 00:11:48
   • Feature Scaling, Evaluating Regression Models, and Applying Linear Regression 00:11:33
   • Regularization Techniques for Regression Analysis 00:10:36
   • Applying Random Forest for Regression Analysis 00:05:57
   • Multi-Layer Perceptron, Neural Networks, and Applying MLP with Scikit-learn 00:19:24
4. Chapter 4 : Unsupervised Learning —Dimensionality Reduction
   • Principle Component Analysis 00:09:19
   • Applying PCA with Scikit-learn for Feature Reduction 00:09:07
   • Applying PCA for a Regression Problem on a Large Dataset 00:14:28
   • Nonlinear Methods of Feature Extraction – t-SNE and Isomap 00:08:36
   • Applying Dimensionality Reduction Techniques to Images 00:18:21
5. Chapter 5 : Unsupervised Learning – Clustering
   • Introduction to Clustering and k-means Clustering 00:09:34
   • Applying k-means with Scikit-learn 00:15:19
   • Agglomerative Clustering 00:08:53
   • DBSCAN Clustering Algorithm 00:06:52
   • Applying DBSCAN with Scikit-learn 00:14:14
6. Chapter 6 : Improving ML Model Performance
   • Handling Missing Values and Data Cleaning 00:10:26
   • Handling Missing Values and Scaling Numerical Features 00:08:26
   • Handling Outliers and Removing Distribution Skew 00:09:51
   • Handling Outliers and Removing Distribution Skew (Continued) 00:13:08
   • Deriving Additional Features 00:10:36
   • Evaluating Different Models and Cross- Validation 00:08:00
   • Model Selection Strategies 00:14:38
   • Feature Engineering for Classification 00:12:20
   • Model Selection Strategies for Credit Risk Assessment 00:09:38
7. Chapter 7 : Creating Pipelines and Advanced Model Selection
   • Creating Processing Pipelines with Scikit-learn 00:12:03
   • Using Pipelines on Our Credit Risk Assessment Dataset 00:09:58
   • Advanced Model Selection Techniques 00:12:05
   • Practicing Pipelines with a Time-Series Dataset 00:14:44
8. Chapter 8 : Handling Text Data with Scikit-learn
   • Bag-of-Words Model and Sentiment Analysis 00:14:48
   • Using Stop-Words and TF-IDF for Sentiment Analysis 00:09:23
   • Using N-Grams to Improve Model Performance for Sentiment Analysis 00:08:05
   • Using Stemming and Lemmatization for Sentiment Analysis 00:12:24
   • Topic Modeling with TruncatedSVD and Latent Dirichlet Allocation 00:19:28
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