**Fundamentals of Machine Learning with scikit-learn**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 33m | 422 MB

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

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.

An easy-to-follow, step-by-step guide that will help you get to grips with real-world applications of algorithms for Machine Learning.

What You Will Learn

- 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

**Table of Contents**

**Introduction to Machine Learning**

1 The Course Overview

2 Machine Types and Learning Methods

3 Data Formats

4 Learnability

5 Statistical Learning Approaches

6 Elements of Information Theory

**Feature Selection and Feature Engineering**

7 Splitting Datasets

8 Managing Data

9 Data Scaling and Normalization

10 Principal Component Analysis

**Linear Regression**

11 Linear Models and Its Example

12 Linear Regression with scikit-learn

13 Ridge, Lasso, and ElasticNet

14 Regression Types

**Logistic Regression**

15 Logistic Regression

16 Stochastic Gradient Descent Algorithms

17 Finding the Optimal Hyperparameters

18 Classification Metrics

19 ROC Curve

**Naive Bayes’#**

20 Bayes’ Theorem

21 Naive Bayes’ in scikit-learn

**Support Vector Machines**

22 scikit-learn Implementation

23 Controlled Support Vector Machines

**Decision Trees and Ensemble Learning**

24 Binary Decision Trees

25 Decision Tree Classification with scikit-learn

26 Ensemble Learning

**Clustering Fundamentals**

27 Clustering Basics

28 DBSCAN and Spectral Clustering

29 Evaluation Methods Based on the Ground Truth

**Hierarchical Clustering**

30 Agglomerative Clustering

31 Implementing Agglomerative Clustering

32 Connectivity Constraints

**Introduction to Recommendation Systems**

33 Naive User-Based Systems

34 Content-Based Systems

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