Python Machine Learning Solutions

Tuto Python Machine Learning Solutions


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100 video tutorials that teach you how to perform various machine learning tasks in the real world, using Python!

About This Python Machine Learning SolutionsVideo course

  • Understand which algorithms to use in a given context with the help of this exciting video-based guide
  • Learn about perceptrons and see how they are used to build neural networks
  • Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniques

Python Learning Machine In Detail

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 video 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.

What will you learn in this course?

Course plan
Chapter 1
The Realm of Supervised Learning
Chapter 2
Constructing a Classifier
Chapter 3
Predictive Modeling
Chapter 4
Clustering with Unsupervised Learning
Chapter 5
Building Recommendation Engines
Chapter 6
Analyzing Text Data
Chapter 7
Speech Recognition

Detailed course plan

Chapter 1 : The Realm of Supervised Learning
Lesson 1The Course Overview
Lesson 2Preprocessing Data Using Different Techniques
Lesson 3Label Encoding
Lesson 4Building a Linear Regressor
Lesson 5Regression Accuracy and Model Persistence
Lesson 6Building a Ridge Regressor
Lesson 7Building a Polynomial Regressor
Lesson 8Estimating housing prices
Lesson 9Computing relative importance of features
Lesson 10Estimating bicycle demand distribution
Chapter 2 : Constructing a Classifier
Lesson 1Building a Simple Classifier
Lesson 2Building a Logistic Regression Classifier
Lesson 3Building a Naive Bayes’ Classifier
Lesson 4Splitting the Dataset for Training and Testing
Lesson 5Evaluating the Accuracy Using Cross-Validation
Lesson 6Visualizing the Confusion Matrix and Extracting the Performance Report
Lesson 7Evaluating Cars based on Their Characteristics
Lesson 8Extracting Validation Curves
Lesson 9Extracting Learning Curves
Lesson 10Extracting the Income Bracket
Chapter 3 : Predictive Modeling
Lesson 1Building a Linear Classifier Using Support Vector Machine
Lesson 2Building Nonlinear Classifier Using SVMs
Lesson 3Tackling Class Imbalance
Lesson 4Extracting Confidence Measurements
Lesson 5Finding Optimal Hyper-Parameters
Lesson 6Building an Event Predictor
Lesson 7Estimating Traffic
Chapter 4 : Clustering with Unsupervised Learning
Lesson 1Clustering Data Using the k-means Algorithm
Lesson 2Compressing an Image Using Vector Quantization
Lesson 3Building a Mean Shift Clustering
Lesson 4Grouping Data Using Agglomerative Clustering
Lesson 5Evaluating the Performance of Clustering Algorithms
Lesson 6Automatically Estimating the Number of Clusters Using DBSCAN
Lesson 7Finding Patterns in Stock Market Data
Lesson 8Building a Customer Segmentation Model
Chapter 5 : Building Recommendation Engines
Lesson 1Building Function Composition for Data Processing
Lesson 2Building Machine Learning Pipelines
Lesson 3Finding the Nearest Neighbors
Lesson 4Constructing a k-nearest Neighbors Classifier
Lesson 5Constructing a k-nearest Neighbors Regressor
Lesson 6Computing the Euclidean Distance Score
Lesson 7Computing the Pearson Correlation Score
Lesson 8Finding Similar Users in a Dataset
Lesson 9Generating Movie Recommendations
Chapter 6 : Analyzing Text Data
Lesson 1Preprocessing Data Using Tokenization
Lesson 2Stemming Text Data
Lesson 3Converting Text to Its Base Form Using Lemmatization
Lesson 4Dividing Text Using Chunking
Lesson 5Building a Bag-of-Words Model
Lesson 6Building a Text Classifier
Lesson 7Identifying the Gender
Lesson 8Analyzing the Sentiment of a Sentence
Lesson 9Identifying Patterns in Text Using Topic Modelling
Chapter 7 : Speech Recognition
Lesson 1Reading and Plotting Audio Data
Lesson 2Transforming Audio Signals into the Frequency Domain
Lesson 3Generating Audio Signals with Custom Parameters
Lesson 4Synthesizing Music
Lesson 5Extracting Frequency Domain Features
Lesson 6Building Hidden Markov Models
Lesson 7Building a Speech Recognizer
Chapter 8 : Dissecting Time Series and Sequential Data
Lesson 1Transforming Data into the Time Series Format
Lesson 2Slicing Time Series Data
Lesson 3Operating on Time Series Data
Lesson 4Extracting Statistics from Time Series
Lesson 5Building Hidden Markov Models for Sequential Data
Lesson 6Building Conditional Random Fields for Sequential Text Data
Lesson 7Analyzing Stock Market Data with Hidden Markov Models
Chapter 9 : Image Content Analysis
Lesson 1Operating on Images Using OpenCV-Python
Lesson 2Detecting Edges
Lesson 3Histogram Equalization
Lesson 4Detecting Corners and SIFT Feature Points
Lesson 5Building a Star Feature Detector
Lesson 6Creating Features Using Visual Codebook and Vector Quantization
Lesson 7Training an Image Classifier Using Extremely Random Forests
Lesson 8Building an object recognizer
Chapter 10 : Biometric Face Recognition
Lesson 1Capturing and Processing Video from a Webcam
Lesson 2Building a Face Detector using Haar Cascades
Lesson 3Building Eye and Nose Detectors
Lesson 4Performing Principal Component Analysis
Lesson 5Performing Kernel Principal Component Analysis
Lesson 6Performing Blind Source Separation
Lesson 7Building a Face Recognizer Using a Local Binary Patterns Histogram
Chapter 11 : Deep Neural Networks
Lesson 1Building a Perceptron
Lesson 2Building a Single-Layer Neural Network
Lesson 3Building a deep neural network
Lesson 4Creating a Vector Quantizer
Lesson 5Building a Recurrent Neural Network for Sequential Data Analysis
Lesson 6Visualizing the Characters in an Optical Character Recognition Database
Lesson 7Building an Optical Character Recognizer Using Neural Networks
Chapter 12 : Visualizing Data
Lesson 1Plotting 3D Scatter plots
Lesson 2Plotting Bubble Plots
Lesson 3Animating Bubble Plots
Lesson 4Drawing Pie Charts
Lesson 5Plotting Date-Formatted Time Series Data
Lesson 6Plotting Histograms
Lesson 7Visualizing Heat Maps
Lesson 8Animating Dynamic Signals

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