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Learn the most effective machine learning tools and techniques with Python

What you'll learn

Take the advantage of the power of Python to handle data extraction and manipulation

Delve into the world of analytics to predict accurate situations

Implement machine learning classification and regression algorithms from scratch with Python

Evaluate the performance of a machine learning model and optimize it

Explore and use Python's impressive machine learning ecosystem

Successfully evaluate and apply the most effective models to problems

Learn the fundamentals of NLP—and put them into practice

Visualize data for maximum impact and clarity

Deploy machine learning models using third-party APIs

Get to grips with feature eeering

Requirements

Working knowledge of Python is needed

Basic knowledge of Math and Statistics is also needed

Description

Are you interested to enter into the world of data science and learn the most effective machine learning tools and techniques with Python? then you should surely go for this Learning Path.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

Machine learning and data science are some of the top buzzwords in the technical world today. Machine learning - the application and science of algorithms that makes sense of data, is the most exciting field of all the computer sciences! The resurgent interest in machine learning is due to the same factors that have made data science more popular than ever. We are living in an age where data comes in abundance; using the self-learning algorithms from the field of machine learning, you can turn this data into knowledge. Machine learning gives you unimaginably powerful insights into data. Python has topped the charts in the recent years over other programming languages. The usage of Python is such that it cannot be limited to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complex processes such as artificial intelligence, machine learning, natural language processing, data science, and so on.

The highlights of this Learning Path are:

Solve interesting, real-world problems using machine learning and Python as the learning journey unfolds

Use Python to visualize data spread across multiple dimensions and extract useful features

Let’s take a quick look at your learning journey. This Learning Path is your entry point to machine learning. It starts with an introduction to machine learning and Python language. You’ll learn the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. With the help of the various projects included, you’ll acquire the mechanics of several important machine learning algorithms. You’ll also be guided step-by-step to build your own models from scratch. You’ll learn to tackle data-driven problems and implement your solutions with the powerful yet simple Python language. Interesting and easy-to-follow examples—including news topic classification, spam email detection, online ad click-through prediction, and stock prices forecasts—will keep you glued to the screen. Moving further, six different independent projects will help you master machine learning in Python. Finally, you’ll have a broad picture of the machine learning ecosystem and mastered best practices for applying machine learning techniques.

By the end of this Learning Path, you’ll have learned to apply various machine learning algorithms with Python packages and libraries to implement your own machine learning models.

Meet Your Experts

We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth

Yuxi (Hayden) Liu is currently an applied research scientist working in the largest privately-owned Canadian artificial intelligence R&D company. He is focused on developing machine learning systems and models and implementing appropriate architectures for given learning tasks, including deep neural networks, convolutional neural networks, recurrent networks, SVM, and random forest. He has worked for a few years as a data scientist at several computational advertising companies, where he applied his machine learning expertise in ad optimization. Yuxi earned his degree from the University of Toronto, and published five first-authored IEEE transactions and conference papers during his master's research. He has authored a Packt book titled Python Machine Learning By Example, which was ranked the #1 best seller in India in 2017. He is also a machine learning education enthusiast and provides weekly training in machine learning.

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.

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 NaA¯ve Bayes

Lecture 14 The Mechanics of NaA¯ve Bayes

Lecture 15 The NaA¯ve 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: Python Machine Learning Projects

Lecture 46 The Course Overview

Lecture 47 Sourcing Airfare Pricing Data

Lecture 48 Retrieving the Fare Data with Advanced Web Scraping Techniques

Lecture 49 Parsing the DOM to Extract Pricing Data

Lecture 50 Sending Real- Alerts Using IFTTT

Lecture 51 Putting It All Together

Lecture 52 The IPO Market

Lecture 53 Feature Eeering

Lecture 54 Binary Classification

Lecture 55 Feature Importance

Lecture 56 Creating a Supervised Training Set with the Pocket App

Lecture 57 Using the embed.ly API to Story Bodies

Lecture 58 Natural Language Processing Basics

Lecture 59 Support Vector Machines

Lecture 60 IFTTT Integration with Feeds, Google Sheets, and E-mail

Lecture 61 Setting Up Your Daily Personal Newsletter

Lecture 62 What Does Research Tell Us about the Stock Market?

Lecture 63 Developing a Trading Strategy

Lecture 64 Building a Model and Evaluating Its Performance

Lecture 65 Modeling with Dynamic Warping

Lecture 66 Machine Learning on Images

Lecture 67 Working with Images

Lecture 68 Finding Similar Images

Lecture 69 Building an Image Similarity Ee

Lecture 70 The Design of Chatbots

Lecture 71 Building a Chatbot

This Learning Path is a captivating journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application.,Every concept is explained with the help of a project that solves a real-world problem and involves hands-on work, giving you a deep insight into the world of machine learning. It is also a combination of six independent projects, each taking a unique dataset, a different problem statement, and a different solution.

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