Python Machine Learning Solutions

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