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Fundamentals of Machine Learning with scikit-learn

Video Description

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide

About This Video

  • Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.
  • Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.
  • Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.

In Detail

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars, spam detection, document searches, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and data science. The main challenge is how to transform data into actionable knowledge.

In this course you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are: Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature engineering. In this course, you will also learn how these algorithms work and their practical implementation to resolve your problems.

The code bundle for this video course is available at - https://github.com/PacktPublishing/Fundamentals-of-Machine-Learning-with-scikit-learn

Table of Contents

  1. Chapter 1 : Introduction to Machine Learning
    1. The Course Overview 00:03:07
    2. Machine Types and Learning Methods 00:06:28
    3. Data Formats 00:03:48
    4. Learnability 00:04:37
    5. Statistical Learning Approaches 00:05:25
    6. Elements of Information Theory 00:02:58
  2. Chapter 2 : Feature Selection and Feature Engineering
    1. Splitting Datasets 00:02:57
    2. Managing Data 00:05:26
    3. Data Scaling and Normalization 00:04:37
    4. Principal Component Analysis 00:08:13
  3. Chapter 3 : Linear Regression
    1. Linear Models and Its Example 00:02:30
    2. Linear Regression with scikit-learn 00:03:03
    3. Ridge, Lasso, and ElasticNet 00:04:01
    4. Regression Types 00:08:30
  4. Chapter 4 : Logistic Regression
    1. Logistic Regression 00:06:01
    2. Stochastic Gradient Descent Algorithms 00:02:41
    3. Finding the Optimal Hyperparameters 00:01:57
    4. Classification Metrics 00:06:00
    5. ROC Curve 00:02:52
  5. Chapter 5 : Naive Bayes’
    1. Bayes’ Theorem 00:02:41
    2. Naive Bayes’ in scikit-learn 00:06:16
  6. Chapter 6 : Support Vector Machines
    1. scikit-learn Implementation 00:07:40
    2. Controlled Support Vector Machines 00:04:00
  7. Chapter 7 : Decision Trees and Ensemble Learning
    1. Binary Decision Trees 00:05:34
    2. Decision Tree Classification with scikit-learn 00:04:31
    3. Ensemble Learning 00:08:08
  8. Chapter 8 : Clustering Fundamentals
    1. Clustering Basics 00:04:23
    2. DBSCAN and Spectral Clustering 00:04:47
    3. Evaluation Methods Based on the Ground Truth 00:03:49
  9. Chapter 9 : Hierarchical Clustering
    1. Agglomerative Clustering 00:04:55
    2. Implementing Agglomerative Clustering 00:01:50
    3. Connectivity Constraints 00:02:17
  10. Chapter 10 : Introduction to Recommendation Systems
    1. User-Based Systems 00:03:12
    2. Content-Based Systems 00:04:33