Python: Master Machine Learning With Python: 3-In-1

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 engines, 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-time 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 changing and complex world of emerging 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-Time Alerts Using IFTTT

Lecture 6 Putting It All Together

Lecture 7 The IPO Market

Lecture 8 Feature Engineering

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 Download 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 Time Warping

Lecture 21 Machine Learning on Images

Lecture 22 Working with Images

Lecture 23 Finding Similar Images

Lecture 24 Building an Image Similarity Engine

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 Segmentation 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 Sentiment of a Sentence

Lecture 79 Identifying Patterns in Text Using Topic Modelling

Lecture 80 Reading and Plotting Audio Data

Lecture 81 Transforming 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 Transforming Data into the Time Series Format

Lecture 88 Slicing Time Series Data

Lecture 89 Operating on Time Series Data

Lecture 90 Extracting Statistics from Time 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 Performing Principal Component Analysis

Lecture 106 Performing Kernel Principal Component Analysis

Lecture 107 Performing 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 Time 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.