Fundamentals of Machine Learning with scikit-learn

Fundamentals of Machine Learning with scikit-learn

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

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About This Course

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

Other Information

  • Certificate will provided in this course on Completion
  • Full lifetime access
  • Available on Mobile & Laptop

What Students Will Learn In Your Course?

  • Acquaint yourself with important elements of Machine Learning
  • Understand the feature selection and feature engineering process
  • Assess performance and error trade-offs for Linear Regression
  • Build a data model
  • Understand how a data model works
  • Understand strategies for hierarchical clustering
  • Ensemble learning with decision trees
  • Learn to tune the parameters of Support Vector machines
  • Implement clusters to a dataset

Are There Any Course Requirements Or Prerequisites?

Familiarity with languages such as R and Python will be invaluable here.

Who Are Your Target Students?

This course is for IT professionals who want to enter the field of data science and are very new to Machine Learning.

Course Content

  • 34 lectures
  • 02:33:32
  • The Course Overview
    00:03:06
  • Machine Types and Learning Methods
    00:06:27
  • Data Formats
    00:03:47
  • Learnability
    00:04:37
  • Statistical Learning Approaches
    00:05:25
  • Elements of Information Theory
    00:02:58
  • User-Based Systems
    00:03:12
  • Content-Based Systems
    00:04:33
  • Splitting Datasets
    00:02:57
  • Managing Data
    00:05:25
  • Data Scaling and Normalization
    00:04:37
  • Principal Component Analysis
    00:08:12
  • Linear Models and Its Example
    00:02:30
  • Linear Regression with scikit-learn
    00:03:03
  • Ridge, Lasso, and ElasticNet
    00:04:01
  • Regression Types
    00:08:29
  • Logistic Regression
    00:06:00
  • Stochastic Gradient Descent Algorithms
    00:02:41
  • Finding the Optimal Hyperparameters
    00:01:56
  • Classification Metrics
    00:05:59
  • ROC Curve
    00:02:51
  • Bayes‚Ao Theorem
    00:02:41
  • Naive Bayes‚Ao in scikit-learn
    00:06:16
  • scikit-learn Implementation
    00:07:40
  • Controlled Support Vector Machines
    00:04:00
  • Binary Decision Trees
    00:05:33
  • Decision Tree Classification with scikit-learn
    00:04:31
  • Ensemble Learning
    00:08:07
  • Clustering Basics
    00:04:23
  • DBSCAN and Spectral Clustering
    00:04:47
  • Evaluation Methods Based on the Ground Truth
    00:03:48
  • Agglomerative Clustering
    00:04:54
  • Implementing Agglomerative Clustering
    00:01:49
  • Connectivity Constraints
    00:02:17
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