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Apply Machine Learning techniques to solve real-world problems with Python, scikit-learn and TensorFlow

What you'll learn

Solve interesting, real-world problems using machine learning with Python

Evaluate the performance of machine learning systems in common tasks

Create pipelines to deal with real-world input data

Traverse from concept to a production-ready machine learning setup/pipeline capable of real-world usage

Use Python to visualize data spread across multiple dimensions and extract useful features to implement machine learning classification and regression algorithms from scratch in Python

Predict the values of continuous variables using linear regression and K Nearest Neighbors to classify documents and images using logistic regression and support vector machines

Requirements

Familiarity with Machine Learning fundamentals will be useful.

A basic understanding Python programming is assumed.

Description

Machine learning brings together computer science and statistics to build smart, efficient models. Using powerful techniques offered by machine learning, you’ll tackle data-driven problems. The effective blend of Machine Learning with Python, scikit-learn, and TensorFlow, helps in implementing solutions to real-world problems as well as automating analytical model.

This comprehensive 3-in-1 course is your one-stop solution in mastering machine learning algorithms and their implementation. Learn the fundamentals of machine learning and build your own intelligent applications. Explore popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

This course will help you discover the magical black box that is Machine Learning by teaching a practical approach to modeling using Python, scikit-learn and TensorFlow.

The first course, Step-by-Step Machine Learning with Python, covers easy-to-follow examples that get you up and running with machine learning. In this course, you’ll learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. You’ll build your own models from scratch.

The second course, Machine Learning with Scikit-learn, covers effective learning algorithms to real-world problems using scikit-learn. You’ll build systems that classify documents, recognize images, detect ads, and more. You’ll learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model’s performance.

The third course, Machine Learning with TensorFlow, covers hands-on examples with machine learning using Python. You’ll cover the unique features of the library such as data flow Graphs, training, and visualization of performance with TensorBoard—all within an example-rich context using problems from multiple sources.. The focus is on introducing new concepts through problems that are coded and solved over the course of each section.

By the end of this training program you’ll be able to tackle data-driven problems and implement your solutions as well as build efficient models with the powerful yet simple features of Python, scikit-learn and TensorFlow.

About the Authors

Yuxi (Hayden) Liu is currently an applied research scientist focused on developing machine learning models and systems for given learning tasks. He has worked for a few years as a data scientist, and applied his machine learning expertise in computational advertising. He earned his degree from the University of Toronto, and published five first-authored IEEE transaction and conference papers during his research. His first book, titled Python Machine Learning By Example, was ranked the #1 bestseller in India in 2017. He is also a machine learning education enthusiast.

Shams Ul Azeem is an undergraduate in electrical eeering from NUST Islamabad, Pakistan. He has a great interest in the computer science field, and he started his journey with Android development. Now, he’s pursuing his career in Machine Learning, particularly in deep learning, by doing medical-related freelancing projects with different companies. He was also a member of the RISE lab, NUST, and he has a publication credit at the IEEE International Conference, ROBIO as a co-author of Designing of motions for humanoid goalkeeper robots.

Overview

Section 1: Step-by-Step Machine Learning with Python

Lecture 1 The Course Overview

Lecture 2 Introduction to Machine Learning

Lecture 3 Installing Software and Setting Up

Lecture 4 Understanding NLP

Lecture 5 Touring Powerful NLP Libraries in Python

Lecture 6 Getting the Newsgroups Data

Lecture 7 Thinking about Features

Lecture 8 Visualization

Lecture 9 Data Preprocessing

Lecture 10 Clustering

Lecture 11 Topic Modeling

Lecture 12 Getting Started with Classification

Lecture 13 Exploring Naive Bayes

Lecture 14 The Mechanics of Naive Bayes

Lecture 15 The Naive Bayes Implementation

Lecture 16 Classifier Performance Evaluation

Lecture 17 Model Tuning and cross-validation

Lecture 18 Recap and Inverse Document Frequency

Lecture 19 The Mechanics of SVM

Lecture 20 The Implementations of SVM

Lecture 21 The Kernels of SVM

Lecture 22 Choosing Between the Linear and the RBF Kernel

Lecture 23 News topic Classification with Support Vector Machine

Lecture 24 Fetal State Classification with SVM

Lecture 25 Brief Overview of Advertising Click-Through Prediction

Lecture 26 Decision Tree Classifier

Lecture 27 The Implementations of Decision Tree

Lecture 28 Click-Through Prediction with Decision Tree

Lecture 29 Random Forest - Feature Bagging of Decision Tree

Lecture 30 One-Hot Encoding - Converting Categorical Features to Numerical

Lecture 31 Logistic Regression Classifier

Lecture 32 Click-Through Prediction with Logistic Regression by Gradient Descent

Lecture 33 Feature Selection via Random Forest

Lecture 34 Brief Overview of the Stock Market And Stock Price

Lecture 35 Predicting Stock Price with Regression Algorithms

Lecture 36 Data Acquisition and Feature Generation

Lecture 37 Linear Regression

Lecture 38 Decision Tree Regression

Lecture 39 Support Vector Regression

Lecture 40 Regression Performance Evaluation

Lecture 41 Stock Price Prediction with Regression Algorithms

Lecture 42 Best Practices in Data Preparation Stage

Lecture 43 Best Practices in the Training Sets Generation Stage

Lecture 44 Best Practices in the Model Training, Evaluation, and Selection Stage

Lecture 45 Best Practices in the Deployment and Monitoring Stage

Section 2: Machine Learning with Scikit-learn

Lecture 46 The Course Overview

Lecture 47 Defining Machine Learning

Lecture 48 Training Data, Testing Data, and Validation Data

Lecture 49 Bias and Variance

Lecture 50 An Introduction to Scikit-learn

Lecture 51 Installing Pandas, Pillow, NLTK, and Matplotlib

Lecture 52 What Is Simple Linear Regression?

Lecture 53 Evaluating the Model

Lecture 54 KNN, Lazy Learning, and Non-Parametric Models

Lecture 55 Classification with KNN

Lecture 56 Regression with KNN

Lecture 57 Extracting Features from Categorical Variables

Lecture 58 Standardizing Features

Lecture 59 Extracting Features from Text

Lecture 60 Multiple Linear Regression

Lecture 61 Polynomial Regression

Lecture 62 Regularization

Lecture 63 Applying Linear Regression

Lecture 64 Gradient Descent

Lecture 65 Binary Classification with Logistic Regression

Lecture 66 Spam Filtering

Lecture 67 Tuning Models with Grid Search

Lecture 68 Multi-Class Classification

Lecture 69 Multi-Label Classification and Problem Transformation

Lecture 70 Bayes' Theorem

Lecture 71 Generative and Discriminative Models

Lecture 72 Naive Bayes with Scikit-learn

Lecture 73 Decision Trees

Lecture 74 Training Decision Trees

Lecture 75 Decision Trees with Scikit-learn

Lecture 76 Bagging

Lecture 77 Boosting

Lecture 78 Stacking

Lecture 79 The Perceptron–Basics

Lecture 80 Limitations of the Perceptron

Lecture 81 Kernels and the Kernel Trick

Lecture 82 Maximum Ma Classification and Support Vectors

Lecture 83 Classifying Characters in Scikit-learn

Lecture 84 Nonlinear Decision Boundaries

Lecture 85 Feed-Forward and Feedback ANNs

Lecture 86 Multi-Layer Perceptrons and Training Them

Lecture 87 Clustering

Lecture 88 K-means

Lecture 89 Evaluating Clusters

Lecture 90 Image Quantization

Lecture 91 Principal Component Analysis

Lecture 92 Visualizing High-Dimensional Data and Face Recognition with PCA

Section 3: Machine Learning with TensorFlow

Lecture 93 The Course Overview

Lecture 94 Introducing Deep Learning

Lecture 95 Installing TensorFlow on Mac OSX

Lecture 96 Installation on Windows – Pre-Reqeusite Virtual Machine Setup

Lecture 97 Installation on Windows/Linux

Lecture 98 The Hand-Written Letters Dataset

Lecture 99 Automating Data Preparation

Lecture 100 Understanding Matrix Conversions

Lecture 101 The Machine Learning Life Cycle

Lecture 102 Reviewing Outputs and Results

Lecture 103 Getting Started with TensorBoard

Lecture 104 TensorBoard Events and Histograms

Lecture 105 The Graph Explorer

Lecture 106 Our Previous Project on TensorBoard

Lecture 107 Fully Connected Neural Networks

Lecture 108 Convolutional Neural Networks

Lecture 109 Programming a CNN

Lecture 110 Using TensorBoard on Our CNN

Lecture 111 CNN Versus Fully Connected Network Performance

Anyone interested in entering the data science stream with Machine Learning.,Software eeers who want to understand how common Machine Learning algorithms work.,Data scientists and researchers who want to learn about the scikit-learn API.

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