### 100 videos that teach you how to perform various machine learning tasks in the real world.

#### Course Description

#### Learning Outcomes

- Explore classification algorithms and apply them to the income bracket estimation problem
- Use predictive modeling and apply it to real-world problems
- Understand how to perform market segmentation using unsupervised learning
- Explore data visualization techniques to interact with your data in diverse ways
- Find out how to build a recommendation engine
- Understand how to interact with text data and build models to analyze it
- Work with speech data and recognize spoken words using Hidden Markov Models
- Analyze stock market data using Conditional Random Fields
- Work with image data and build systems for image recognition and biometric face recognition
- Grasp how to use deep neural networks to build an optical character recognition system

#### Pre-requisite

#### Who is this course intended for?

These independent videos teach you how to perform various machine learning tasks in different environments. Each of the video in the section will cover a real-life scenario.

#### Your Instructor

Packt has been committed to developer learning since 2004. A lot has changed in software since then – but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content – more than 4000 books and video courses -Packt’s mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages to cutting-edge data analytics, and DevOps, Packt takes software professionals in every field to what’s important to them now.

From skills that will help you to develop and future-proof your career to immediate solutions to everyday tech challenges, Packt is a go-to resource to make you a better, smarter developer.

### Course Curriculum

The Realm of Supervised Learning | |||

Preprocessing Data Using Different Techniques | 00:00:00 | ||

Label Encoding | 00:00:00 | ||

Building a Linear Regressor | 00:00:00 | ||

Regression Accuracy and Model Persistence | 00:00:00 | ||

Building a Ridge Regressor | 00:00:00 | ||

Building a Polynomial Regressor | 00:00:00 | ||

Estimating housing prices | 00:00:00 | ||

Computing relative importance of features | 00:00:00 | ||

Estimating bicycle demand distribution | 00:00:00 | ||

Constructing a Classifier | |||

Building a Simple Classifier | 00:00:00 | ||

Building a Logistic Regression Classifier | 00:00:00 | ||

Building a Naive Bayes’ Classifier | 00:00:00 | ||

Splitting the Dataset for Training and Testing | 00:00:00 | ||

Evaluating the Accuracy Using Cross-Validation | 00:00:00 | ||

Visualizing the Confusion Matrix and Extracting the Performance Report | 00:00:00 | ||

Evaluating Cars based on Their Characteristics | 00:00:00 | ||

Extracting Validation Curves | 00:00:00 | ||

Extracting Learning Curves | 00:00:00 | ||

Extracting the Income Bracket | 00:00:00 | ||

Predictive Modeling | |||

Building a Linear Classifier Using Support Vector Machine | 00:00:00 | ||

Building Nonlinear Classifier Using SVMs | 00:00:00 | ||

Tackling Class Imbalance | 00:00:00 | ||

Extracting Confidence Measurements | 00:00:00 | ||

Finding Optimal Hyper-Parameters | 00:00:00 | ||

Building an Event Predictor | 00:00:00 | ||

Estimating Traffic | 00:00:00 | ||

Clustering with Unsupervised Learning | |||

Clustering Data Using the k-means Algorithm | 00:00:00 | ||

Compressing an Image Using Vector Quantization | 00:00:00 | ||

Building a Mean Shift Clustering | 00:00:00 | ||

Grouping Data Using Agglomerative Clustering | 00:00:00 | ||

Evaluating the Performance of Clustering Algorithms | 00:00:00 | ||

Automatically Estimating the Number of Clusters Using DBSCAN | 00:00:00 | ||

Finding Patterns in Stock Market Data | 00:00:00 | ||

Building a Customer Segmentation Model | 00:00:00 | ||

Building Recommendation Engines | |||

Building Function Composition for Data Processing | 00:00:00 | ||

Building Machine Learning Pipelines | 00:00:00 | ||

Finding the Nearest Neighbors | 00:00:00 | ||

Constructing a k-nearest Neighbors Classifier | 00:00:00 | ||

Constructing a k-nearest Neighbors Regressor | 00:00:00 | ||

Computing the Euclidean Distance Score | 00:00:00 | ||

Computing the Pearson Correlation Score | 00:00:00 | ||

Finding Similar Users in a Dataset | 00:00:00 | ||

Generating Movie Recommendations | 00:00:00 | ||

Analyzing Text Data | |||

Preprocessing Data Using Tokenization | 00:00:00 | ||

Stemming Text Data | 00:00:00 | ||

Converting Text to Its Base Form Using Lemmatization | 00:00:00 | ||

Dividing Text Using Chunking | 00:00:00 | ||

Building a Bag-of-Words Model | 00:00:00 | ||

Building a Text Classifier | 00:00:00 | ||

Identifying the Gender | 00:00:00 | ||

Analyzing the Sentiment of a Sentence | 00:00:00 | ||

Identifying Patterns in Text Using Topic Modelling | 00:00:00 | ||

Speech Recognition | |||

Reading and Plotting Audio Data | 00:00:00 | ||

Transforming Audio Signals into the Frequency Domain | 00:00:00 | ||

Generating Audio Signals with Custom Parameters | 00:00:00 | ||

Synthesizing Music | 00:00:00 | ||

Extracting Frequency Domain Features | 00:00:00 | ||

Building Hidden Markov Models | 00:00:00 | ||

Building a Speech Recognizer | 00:00:00 | ||

Dissecting Time Series and Sequential Data | |||

Transforming Data into the Time Series Format | 00:00:00 | ||

Slicing Time Series Data | 00:00:00 | ||

Operating on Time Series Data | 00:00:00 | ||

Extracting Statistics from Time Series | 00:00:00 | ||

Building Hidden Markov Models for Sequential Data | 00:00:00 | ||

Building Conditional Random Fields for Sequential Text Data | 00:00:00 | ||

Analyzing Stock Market Data with Hidden Markov Models | 00:00:00 | ||

Image Content Analysis | |||

Operating on Images Using OpenCV-Python | 00:00:00 | ||

Detecting Edges | 00:00:00 | ||

Histogram Equalization | 00:00:00 | ||

Detecting Corners and SIFT Feature Points | 00:00:00 | ||

Building a Star Feature Detector | 00:00:00 | ||

Creating Features Using Visual Codebook and Vector Quantization | 00:00:00 | ||

Training an Image Classifier Using Extremely Random Forests | 00:00:00 | ||

Building an object recognizer | 00:00:00 | ||

Biometric Face Recognition | |||

Capturing and Processing Video from a Webcam | 00:00:00 | ||

Building a Face Detector using Haar Cascades | 00:00:00 | ||

Building Eye and Nose Detectors | 00:00:00 | ||

Performing Principal Component Analysis | 00:00:00 | ||

Performing Kernel Principal Component Analysis | 00:00:00 | ||

Performing Blind Source Separation | 00:00:00 | ||

Building a Face Recognizer Using a Local Binary Patterns Histogram | 00:00:00 | ||

Deep Neural Networks | |||

Building a Perceptron | 00:00:00 | ||

Building a Single-Layer Neural Network | 00:00:00 | ||

Building a deep neural network | 00:00:00 | ||

Creating a Vector Quantizer | 00:00:00 | ||

Building a Recurrent Neural Network for Sequential Data Analysis | 00:00:00 | ||

Visualizing the Characters in an Optical Character Recognition Database | 00:00:00 | ||

Building an Optical Character Recognizer Using Neural Networks | 00:00:00 | ||

Visualizing Data | |||

Plotting 3D Scatter plots | 00:00:00 | ||

Plotting Bubble Plots | 00:00:00 | ||

Animating Bubble Plots | 00:00:00 | ||

Drawing Pie Charts | 00:00:00 | ||

Plotting Date-Formatted Time Series Data | 00:00:00 | ||

Plotting Histograms | 00:00:00 | ||

Visualizing Heat Maps | 00:00:00 | ||

Animating Dynamic Signals | 00:00:00 |

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