Last updated 6/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 4.72 GB | Duration: 10h 45m
Practical and unique solutions to common Machine Learning problems that you face! What you'll learn Evaluate and apply the most effective models to problems Deploy machine learning models using third-party APIs Interact with text data and build models to analyze it Use deep neural networks to build an optical character recognition system Work with image data and build systems for image recognition and biometric face recognition Eliminate common data wrangling problems in Pandas and scikit-learn as well as solve prediction visualization issues with Matplotlib Explore data visualization techniques to interact with your data in diverse ways Requirements Prior familiarity with Python programming is assumed. Basic understanding of Machine Learning concepts would certainly be useful. Description You are a data scientist. Every day, you stare at reams of data trying to apply the latest and brightest of models to uncover new insights, but there seems to be an endless supply of obstacles. Your colleagues depend on you to monetize your firm's data - and the clock is ticking. What do you do?Troubleshooting Python Machine Learning is the answer.Machine learning gives you powerful insights into data. Today, implementations of machine learning are adopted throughout Industry and its concepts are many. Machine learning is pervasive in the modern data-driven world. Used across many fields such as search ees, robotics, self-driving cars, and more.The effective blend of Machine Learning with Python, helps in implementing solutions to real-world problems as well as automating analytical model. This comprehensive 3-in-1 course is a comprehensive, practical tutorial that helps you get superb insights from your data in different scenarios and deploy machine learning models with ease. Explore the power of Python and create your own machine learning models with this project-based tutorial. Try and test solutions to solve common problems, while implementing Machine learning with Python. Contents and Overview This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Python Machine Learning Projects, covers Machine Learning with Python's insightful projects. This video is a unique blend of projects that teach you what Machine Learning is all about and how you can implement machine learning concepts in practice. Six different independent projects will help you master machine learning in Python. The video will cover concepts such as classification, regression, clustering, and more, all the while working with different kinds of databases. You’ll be able to implement your own machine learning models after taking this course. The second course, Python Machine Learning Solutions, covers 100 videos that teach you how to perform various machine learning tasks in the real world. Explore 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. Discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning The third course, Troubleshooting Python Machine Learning, covers practical and unique solutions to common Machine Learning problems that you face. Debug your models and research pipelines, so you can focus on pitching new ideas and not fixing old bugs. By the end of the course, you’ll get up-and-running via Machine Learning with Python’s insightful projects to perform various Machine Learning tasks in the real world.About the Authors Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He is currently a full- lead instructor for a data science immersive program in New York City. Prateek Joshi is an Artificial Intelligence researcher, the published author of five books, and a TEDx speaker. He is the founder of Pluto AI, a venture-funded Silicon Valley startup building an analytics platform for smart water management powered by deep learning. His work in this field has led to patents, tech demos, and research papers at major IEEE conferences. He has been an invited speaker at technology and entrepreneurship conferences including TEDx, AT&T Foundry, Silicon Valley Deep Learning, and Open Silicon Valley. Prateek has also been featured as a guest author in prominent tech magazines. His tech blog has received more than 1.2 million page views from over 200 countries and has over 6,600+ followers. He frequently writes on topics such as Artificial Intelligence, Python programming, and abstract mathematics. He is an avid coder and has won many hackathons utilizing a wide variety of technologies. He graduated from University of Southern California with a Master's degree, specializing in Artificial Intelligence. He has worked at companies such as Nvidia and Microsoft Research.Colibriis a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly chag and complex world of emeg technologies, with deep expertise in areas like big data, data science, Machine Learning, and Cloud Computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world's most popular soft drinks companies, helping all of them to better make sense of their data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.Rudy Lai is the founder of Quant Copy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, Quant Copy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feed back into how our AI generated content. Prior to founding Quant Copy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced firsthand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with HighDimension.IO's Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye. In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. Quantitative trading was also a great platform from which to learn a lot about reinforcement learning and supervised learning topics in a commercial setting. Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean's List, and received awards such as the Deutsche Bank Artificial Intelligence prize. Overview Section 1: Python Machine Learning Projects Lecture 1 The Course Overview Lecture 2 Sourcing Airfare Pricing Data Lecture 3 Retrieving the Fare Data with Advanced Web Scraping Techniques Lecture 4 Parsing the DOM to Extract Pricing Data Lecture 5 Sending Real- Alerts Using IFTTT Lecture 6 Putting It All Together Lecture 7 The IPO Market Lecture 8 Feature Eeering Lecture 9 Binary Classification Lecture 10 Feature Importance Lecture 11 Creating a Supervised Training Set with the Pocket App Lecture 12 Using the embed.ly API to Story Bodies Lecture 13 Natural Language Processing Basics Lecture 14 Support Vector Machines Lecture 15 IFTTT Integration with Feeds, Google Sheets, and E-mail Lecture 16 Setting Up Your Daily Personal Newsletter Lecture 17 What Does Research Tell Us about the Stock Market? Lecture 18 Developing a Trading Strategy Lecture 19 Building a Model and Evaluating Its Performance Lecture 20 Modeling with Dynamic Warping Lecture 21 Machine Learning on Images Lecture 22 Working with Images Lecture 23 Finding Similar Images Lecture 24 Building an Image Similarity Ee Lecture 25 The Design of Chatbots Lecture 26 Building a Chatbot Section 2: Python Machine Learning Solutions Lecture 27 The Course Overview Lecture 28 Preprocessing Data Using Different Techniques Lecture 29 Label Encoding Lecture 30 Building a Linear Regressor Lecture 31 Regression Accuracy and Model Persistence Lecture 32 Building a Ridge Regressor Lecture 33 Building a Polynomial Regressor Lecture 34 Estimating housing prices Lecture 35 Computing relative importance of features Lecture 36 Estimating bicycle demand distribution Lecture 37 Building a Simple Classifier Lecture 38 Building a Logistic Regression Classifier Lecture 39 Building a Naive Bayes’ Classifier Lecture 40 Splitting the Dataset for Training and Testing Lecture 41 Evaluating the Accuracy Using Cross-Validation Lecture 42 Visualizing the Confusion Matrix and Extracting the Performance Report Lecture 43 Evaluating Cars based on Their Characteristics Lecture 44 Extracting Validation Curves Lecture 45 Extracting Learning Curves Lecture 46 Extracting the Income Bracket Lecture 47 Building a Linear Classifier Using Support Vector Machine Lecture 48 Building Nonlinear Classifier Using SVMs Lecture 49 Tackling Class Imbalance Lecture 50 Extracting Confidence Measurements Lecture 51 Finding Optimal Hyper-Parameters Lecture 52 Building an Event Predictor Lecture 53 Estimating Traffic Lecture 54 Clustering Data Using the k-means Algorithm Lecture 55 Compressing an Image Using Vector Quantization Lecture 56 Building a Mean Shift Clustering Lecture 57 Grouping Data Using Agglomerative Clustering Lecture 58 Evaluating the Performance of Clustering Algorithms Lecture 59 Automatically Estimating the Number of Clusters Using DBSCAN Lecture 60 Finding Patterns in Stock Market Data Lecture 61 Building a Customer Sntation Model Lecture 62 Building Function Composition for Data Processing Lecture 63 Building Machine Learning Pipelines Lecture 64 Finding the Nearest Neighbors Lecture 65 Constructing a k-nearest Neighbors Classifier Lecture 66 Constructing a k-nearest Neighbors Regressor Lecture 67 Computing the Euclidean Distance Score Lecture 68 Computing the Pearson Correlation Score Lecture 69 Finding Similar Users in a Dataset Lecture 70 Generating Movie Recommendations Lecture 71 Preprocessing Data Using Tokenization Lecture 72 Stemming Text Data Lecture 73 Converting Text to Its Base Form Using Lemmatization Lecture 74 Dividing Text Using Chunking Lecture 75 Building a Bag-of-Words Model Lecture 76 Building a Text Classifier Lecture 77 Identifying the Gender Lecture 78 Analyzing the Sennt of a Sentence Lecture 79 Identifying Patterns in Text Using Topic Modelling Lecture 80 Reading and Plotting Audio Data Lecture 81 Transfog Audio Signals into the Frequency Domain Lecture 82 Generating Audio Signals with Custom Parameters Lecture 83 Synthesizing Music Lecture 84 Extracting Frequency Domain Features Lecture 85 Building Hidden Markov Models Lecture 86 Building a Speech Recognizer Lecture 87 Transfog Data into the Series Format Lecture 88 Slicing Series Data Lecture 89 Operating on Series Data Lecture 90 Extracting Statistics from Series Lecture 91 Building Hidden Markov Models for Sequential Data Lecture 92 Building Conditional Random Fields for Sequential Text Data Lecture 93 Analyzing Stock Market Data with Hidden Markov Models Lecture 94 Operating on Images Using OpenCV-Python Lecture 95 Detecting Edges Lecture 96 Histogram Equalization Lecture 97 Detecting Corners and SIFT Feature Points Lecture 98 Building a Star Feature Detector Lecture 99 Creating Features Using Visual Codebook and Vector Quantization Lecture 100 Training an Image Classifier Using Extremely Random Forests Lecture 101 Building an object recognizer Lecture 102 Capturing and Processing Video from a Webcam Lecture 103 Building a Face Detector using Haar Cascades Lecture 104 Building Eye and Nose Detectors Lecture 105 Perfog Principal Component Analysis Lecture 106 Perfog Kernel Principal Component Analysis Lecture 107 Perfog Blind Source Separation Lecture 108 Building a Face Recognizer Using a Local Binary Patterns Histogram Lecture 109 Building a Perceptron Lecture 110 Building a Single-Layer Neural Network Lecture 111 Building a deep neural network Lecture 112 Creating a Vector Quantizer Lecture 113 Building a Recurrent Neural Network for Sequential Data Analysis Lecture 114 Visualizing the Characters in an Optical Character Recognition Database Lecture 115 Building an Optical Character Recognizer Using Neural Networks Lecture 116 Plotting 3D Scatter plots Lecture 117 Plotting Bubble Plots Lecture 118 Animating Bubble Plots Lecture 119 Drawing Pie Charts Lecture 120 Plotting Date-Formatted Series Data Lecture 121 Plotting Histograms Lecture 122 Visualizing Heat Maps Lecture 123 Animating Dynamic Signals Section 3: Troubleshooting Python Machine Learning Lecture 124 The Course Overview Lecture 125 Splitting Your Datasets for Train, Test, and Validate Lecture 126 Persist Your Hard Earned Models by Saving Them to Disk Lecture 127 Calculate Word Frequencies Efficiently in Good ol' Python Lecture 128 Transform Your Variable Length Features into One-Hot Vectors Lecture 129 Finding the Most Important Features in Your Classifier Lecture 130 Predicting Multiple Targets with the Same Dataset Lecture 131 Retrieving the Best Estimators after Grid Search Lecture 132 Regress on Your Pandas Data Frame with Simple Statsmodels OLS Lecture 133 Extracting Decision Tree Rules from scikit-learn Lecture 134 Finding Out Which Features Are Important in a Random Forest Model Lecture 135 Classifying with SVMs When Your Data Has Unbalanced Classes Lecture 136 Computing True/False Positives/Negatives after in scikit-learn Lecture 137 Labelling Dimensions with Original Feature Names after PCA Lecture 138 Clustering Text Documents with scikit-learn K-means Lecture 139 Listing Word Frequency in a Corpus Using Only scikit-learn Lecture 140 Polynomial Kernel Regression Using Pipelines Lecture 141 Visualize Outputs Over Two-Dimensions Using NumPy's Meshgrid Lecture 142 Drawing Out a Decision Tree Trained in scikit-learn Lecture 143 Clarify Your Histogram by Labelling Each Bin Lecture 144 Centralizing Your Color Legend When You Have Multiple Subplots Developers and data scientist, who have a basic machine learning knowledge and want to explore the various arenas of machine learning by creating insightful and interesting projects.,Python programmers who are looking to use machine-learning algorithms to create real-world applications. 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