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Hands-on Scikit-learn for Machine Learning

Video Description

Machine Learning projects with Python’s own Scikit-learn on real-world datasets

About This Video

  • Apply 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 effort
  • Explore datasets from a diverse set of ML problem domains, applying different models and techniques.

In Detail

Scikit-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-

Table of Contents

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