**Machine Learning with Scikit-learn**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 21m | 1.37 GB

Learn to implement and evaluate machine learning solutions with scikit-learn

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning, you can automate any analytical model. This course examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It also discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You’ll learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model’s performance. By the end of this course, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

This course is motivated by the belief that you don’t understand something until you can describe it simply. Work through your problems to develop your understanding of the learning algorithms and models, then apply your learnings to real-life problems.

What You Will Learn

- Review fundamental concepts such as bias and variance
- Extract features from categorical variables, text, and images
- Predict the values of continuous variables using linear regression and K Nearest Neighbors
- Classify documents and images using logistic regression and support vector machines
- Create ensembles of estimators using bagging and boosting techniques
- Discover hidden structures in data using K-Means clustering
- Evaluate the performance of machine learning systems in common tasks

**Table of Contents**

**The Fundamentals of Machine Learning**

1 The Course Overview

2 Defining Machine Learning

3 Training Data, Testing Data, and Validation Data

4 Bias and Variance

5 An Introduction to Scikit-learn

6 Installing Pandas, Pillow, NLTK, and Matplotlib

**The Perceptron**

7 The Perceptron- Basics

8 Limitations of the Perceptron

**From the Perceptron to Support Vector Machines**

9 Kernels and the Kernel Trick

10 Maximum Margin Classification and Support Vectors

11 Classifying Characters in Scikit-learn

**From the Perceptron to Artificial Neural Networks**

12 Nonlinear Decision Boundaries

13 Feed-Forward and Feedback ANNs

14 Multi-Layer Perceptrons and Training Them

**K-means**

15 Clustering

16 K-means

17 Evaluating Clusters

18 Image Quantization

**Dimensionality Reduction with Principal Component Analysis**

19 Principal Component Analysis

20 Visualizing High-Dimensional Data and Face Recognition with PCA

**Simple Linear Regression**

21 What Is Simple Linear Regression

22 Evaluating the Model

**Classification and Regression with k-Nearest Neighbors**

23 KNN, Lazy Learning, and Non-Parametric Models

24 Classification with KNN

25 Regression with KNN

**Feature Extraction**

26 Extracting Features from Categorical Variables

27 Standardizing Features

28 Extracting Features from Text

**From Simple Linear Regression to Multiple Linear Regression**

29 Multiple Linear Regression

30 Polynomial Regression

31 Regularization

32 Applying Linear Regression

33 Gradient Descent

**From Linear Regression to Logistic Regression**

34 Binary Classification with Logistic Regression

35 Spam Filtering

36 Tuning Models with Grid Search

37 Multi-Class Classification

38 Multi-Label Classification and Problem Transformation

**Naive Bayes**

39 Bayes' Theorem

40 Generative and Discriminative Models

41 Naive Bayes with Scikit-learn

**Nonlinear Classification and Regression with Decision Trees**

42 Decision Trees

43 Training Decision Trees

44 Decision Trees with Scikit-learn

**From Decision Trees to Random Forests and Other Ensemble Methods**

45 Bagging

46 Boosting

47 Stacking

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