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Python Machine Learning Solutions
The Realm of Supervised Learning
Preprocessing Data Using Different Techniques (4:09)
Label Encoding (6:04)
Building a Linear Regressor (2:25)
Regression Accuracy and Model Persistence (4:25)
Building a Ridge Regressor (3:41)
Building a Polynomial Regressor (2:40)
Estimating housing prices (2:32)
Computing relative importance of features (3:45)
Estimating bicycle demand distribution (1:54)
Constructing a Classifier
Building a Simple Classifier (3:40)
Building a Logistic Regression Classifier (4:50)
Building a Naive Bayes’ Classifier (2:11)
Splitting the Dataset for Training and Testing (1:23)
Evaluating the Accuracy Using Cross-Validation (4:06)
Visualizing the Confusion Matrix and Extracting the Performance Report (4:13)
Evaluating Cars based on Their Characteristics (5:11)
Extracting Validation Curves (2:49)
Extracting Learning Curves (1:37)
Extracting the Income Bracket (3:36)
Predictive Modeling
Building a Linear Classifier Using Support Vector Machine (4:23)
Building Nonlinear Classifier Using SVMs (1:46)
Tackling Class Imbalance (2:53)
Extracting Confidence Measurements (2:36)
Finding Optimal Hyper-Parameters (2:16)
Building an Event Predictor (3:45)
Estimating Traffic (2:39)
Clustering with Unsupervised Learning
Clustering Data Using the k-means Algorithm (3:07)
Compressing an Image Using Vector Quantization (3:37)
Building a Mean Shift Clustering (2:35)
Grouping Data Using Agglomerative Clustering (3:04)
Evaluating the Performance of Clustering Algorithms (2:55)
Automatically Estimating the Number of Clusters Using DBSCAN (3:34)
Finding Patterns in Stock Market Data (2:34)
Building a Customer Segmentation Model (2:21)
Building Recommendation Engines
Building Function Composition for Data Processing (3:25)
Building Machine Learning Pipelines (3:54)
Finding the Nearest Neighbors (1:56)
Constructing a k-nearest Neighbors Classifier (4:18)
Constructing a k-nearest Neighbors Regressor (2:43)
Computing the Euclidean Distance Score (2:08)
Computing the Pearson Correlation Score (1:55)
Finding Similar Users in a Dataset (1:34)
Generating Movie Recommendations (2:34)
Analyzing Text Data
Preprocessing Data Using Tokenization (3:00)
Stemming Text Data (2:22)
Converting Text to Its Base Form Using Lemmatization (2:11)
Dividing Text Using Chunking (2:03)
Building a Bag-of-Words Model (2:58)
Building a Text Classifier (4:43)
Identifying the Gender (2:17)
Analyzing the Sentiment of a Sentence (3:09)
Identifying Patterns in Text Using Topic Modelling (4:52)
Speech Recognition
Reading and Plotting Audio Data (2:34)
Transforming Audio Signals into the Frequency Domain (2:09)
Generating Audio Signals with Custom Parameters (1:45)
Synthesizing Music (2:10)
Extracting Frequency Domain Features (2:05)
Building Hidden Markov Models (2:19)
Building a Speech Recognizer (3:12)
Dissecting Time Series and Sequential Data
Transforming Data into the Time Series Format (3:07)
Slicing Time Series Data (1:31)
Operating on Time Series Data (1:42)
Extracting Statistics from Time Series (2:29)
Building Hidden Markov Models for Sequential Data (4:15)
Building Conditional Random Fields for Sequential Text Data (4:27)
Analyzing Stock Market Data with Hidden Markov Models (2:25)
Image Content Analysis
Operating on Images Using OpenCV-Python (3:07)
Detecting Edges (2:46)
Histogram Equalization (2:30)
Detecting Corners and SIFT Feature Points (3:46)
Building a Star Feature Detector (1:34)
Creating Features Using Visual Codebook and Vector Quantization (4:10)
Training an Image Classifier Using Extremely Random Forests (2:30)
Building an object recognizer (1:53)
Biometric Face Recognition
Capturing and Processing Video from a Webcam (1:57)
Building a Face Detector using Haar Cascades (2:40)
Building Eye and Nose Detectors (1:54)
Performing Principal Component Analysis (2:17)
Performing Kernel Principal Component Analysis (2:02)
Performing Blind Source Separation (2:16)
Building a Face Recognizer Using a Local Binary Patterns Histogram (4:14)
Deep Neural Networks
Building a Perceptron (2:39)
Building a Single-Layer Neural Network (1:37)
Building a deep neural network (2:18)
Creating a Vector Quantizer (1:40)
Building a Recurrent Neural Network for Sequential Data Analysis (2:23)
Visualizing the Characters in an Optical Character Recognition Database (1:48)
Building an Optical Character Recognizer Using Neural Networks (2:28)
Visualizing Data
Plotting 3D Scatter plots (2:42)
Plotting Bubble Plots (1:16)
Animating Bubble Plots (1:56)
Drawing Pie Charts (1:33)
Plotting Date-Formatted Time Series Data (1:33)
Plotting Histograms (1:04)
Visualizing Heat Maps (1:15)
Animating Dynamic Signals (2:06)
Creating Features Using Visual Codebook and Vector Quantization
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