Hands-on Scikit-learn for Machine Learning

Hands-on Scikit-learn for Machine Learning, available at $34.99, has an average rating of 3.79, with 50 lectures, based on 7 reviews, and has 82 subscribers.

You will learn about Tackle real-world problems in Machine Learning through a structured process using Scikit-learn Achieve substantially more in less time and with much less code by leveraging the power and simplicity of Scikit-learn Develop a thorough understanding of core predictive analytics with regression, classification, and unsupervised learning such as clustering and PCA Create ensemble models with Random-Forest and Gradient-boosting methods and see your model performance improve drastically Build a portfolio of tools and techniques that can readily be applied to your own projects Discover the intuition behind contemporary Machine Learning models and algorithms without going into deep mathematical details Develop the ability to evaluate and improve the accuracy and performance of Machine Learning models Explore the foundations of text analytics and develop a set of tools to apply to your common text-analysis tasks This course is ideal for individuals who are If you are a software developer, machine learning engineer, or data analyst and want to use Scikit-learn for different Machine Learning and analytics tasks, this course is for you. It is particularly useful for If you are a software developer, machine learning engineer, or data analyst and want to use Scikit-learn for different Machine Learning and analytics tasks, this course is for you.

Enroll now: Hands-on Scikit-learn for Machine Learning

Summary

Title: Hands-on Scikit-learn for Machine Learning

Price: $34.99

Average Rating: 3.79

Number of Lectures: 50

Number of Published Lectures: 50

Number of Curriculum Items: 50

Number of Published Curriculum Objects: 50

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Tackle real-world problems in Machine Learning through a structured process using Scikit-learn

Achieve substantially more in less time and with much less code by leveraging the power and simplicity of Scikit-learn

Develop a thorough understanding of core predictive analytics with regression, classification, and unsupervised learning such as clustering and PCA

Create ensemble models with Random-Forest and Gradient-boosting methods and see your model performance improve drastically

Build a portfolio of tools and techniques that can readily be applied to your own projects

Discover the intuition behind contemporary Machine Learning models and algorithms without going into deep mathematical details

Develop the ability to evaluate and improve the accuracy and performance of Machine Learning models

Explore the foundations of text analytics and develop a set of tools to apply to your common text-analysis tasks

Who Should Attend

If you are a software developer, machine learning engineer, or data analyst and want to use Scikit-learn for different Machine Learning and analytics tasks, this course is for you.

Target Audiences

If you are a software developer, machine learning engineer, or data analyst and want to use Scikit-learn for different Machine Learning and analytics tasks, this course is for you.

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

About the Author

Farhan Nazar Zaidi has 25 years’ experience in software architecture, big data engineering, and hands-on software development in a variety of languages and technologies. He is skilled in architecting and designing networked, distributed software systems and data analytics applications, and in designing enterprise-grade software systems.

Farhan holds an MS in Computer Science from University of Southern California, Los Angeles, USA and a BS in Electrical Engineering from University of Engineering, Lahore, Pakistan. He has worked for several Silicon-Valley companies in the past in the US as a Senior Software Engineer, and also held key positions in the software industry in Pakistan. Farhan works as consultant, solutions developer, and in-person trainer on big data engineering, microservices, advanced analytics, and Machine Learning.

Course Curriculum

Chapter 1: Getting Started with a Simple ML Model in Scikit-learn

Lecture 1: The Course Overview

Lecture 2: Course Objectives, Software Installation, and Setup

Lecture 3: Overview of Scikit-learn

Lecture 4: Scikit-learn Programming Workflow Example

Lecture 5: Applying a KNN Model on Cancer Dataset

Lecture 6: Improving the KNN Performance on Cancer Dataset

Chapter 2: Classification Models

Lecture 1: Linear and Logistic Regression

Lecture 2: Evaluating Classification Models

Lecture 3: Logistic Regression and Evaluation with Scikit-learn

Lecture 4: Decision Trees

Lecture 5: Bagging, Boosting, and Random Forests

Lecture 6: Applying Ensemble Methods with Scikit-learn

Lecture 7: Support Vector Machines

Lecture 8: Applying Support Vector Machines Classifier with Scikit-learn

Lecture 9: Multi-class Classification Example with Scikit-learn

Chapter 3: Supervised Machine Learning – Regression

Lecture 1: Downloading and Inspecting the Dataset

Lecture 2: Handling Categorical Features and Missing Values

Lecture 3: Creating Train and Test Sets and Finding Correlation

Lecture 4: Feature Scaling, Evaluating Regression Models, and Applying Linear Regression

Lecture 5: Regularization Techniques for Regression Analysis

Lecture 6: Applying Random Forest for Regression Analysis

Lecture 7: Multi-Layer Perceptron, Neural Networks, and Applying MLP with Scikit-learn

Chapter 4: Unsupervised Learning —Dimensionality Reduction

Lecture 1: Principle Component Analysis

Lecture 2: Applying PCA with Scikit-learn for Feature Reduction

Lecture 3: Applying PCA for a Regression Problem on a Large Dataset

Lecture 4: Nonlinear Methods of Feature Extraction – t-SNE and Isomap

Lecture 5: Applying Dimensionality Reduction Techniques to Images

Chapter 5: Unsupervised Learning – Clustering

Lecture 1: Introduction to Clustering and k-means Clustering

Lecture 2: Applying k-means with Scikit-learn

Lecture 3: Agglomerative Clustering

Lecture 4: DBSCAN Clustering Algorithm

Lecture 5: Applying DBSCAN with Scikit-learn

Chapter 6: Improving ML Model Performance

Lecture 1: Handling Missing Values and Data Cleaning

Lecture 2: Handling Missing Values and Scaling Numerical Features

Lecture 3: Handling Outliers and Removing Distribution Skew

Lecture 4: Handling Outliers and Removing Distribution Skew (Continued)

Lecture 5: Deriving Additional Features

Lecture 6: Evaluating Different Models and Cross- Validation

Lecture 7: Model Selection Strategies

Lecture 8: Feature Engineering for Classification

Lecture 9: Model Selection Strategies for Credit Risk Assessment

Chapter 7: Creating Pipelines and Advanced Model Selection

Lecture 1: Creating Processing Pipelines with Scikit-learn

Lecture 2: Using Pipelines on Our Credit Risk Assessment Dataset

Lecture 3: Advanced Model Selection Techniques

Lecture 4: Practicing Pipelines with a Time-Series Dataset

Chapter 8: Handling Text Data with Scikit-learn

Lecture 1: Bag-of-Words Model and Sentiment Analysis

Lecture 2: Using Stop-Words and TF-IDF for Sentiment Analysis

Lecture 3: Using N-Grams to Improve Model Performance for Sentiment Analysis

Lecture 4: Using Stemming and Lemmatization for Sentiment Analysis

Lecture 5: Topic Modeling with TruncatedSVD and Latent Dirichlet Allocation

Instructors

Packt Publishing
Tech Knowledge in Motion

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4 stars: 2 votes

5 stars: 2 votes

Frequently Asked Questions

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