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


Course Description

Machine learning is increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.
With this course, you will learn how to perform various machine learning tasks in different environments.
We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks.
Throughout the course, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.
You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modelling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

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

This course is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code.

Who is this course intended for?

This course is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This course is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code.

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 Publishing

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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