Learning Path Python Advanced Machine Learning 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 engineering
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 Amazon 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-time 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 Naïve Bayes
Lecture 14 The Mechanics of Naïve Bayes
Lecture 15 The Naïve 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-Time Alerts Using IFTTT
Lecture 51 Putting It All Together
Lecture 52 The IPO Market
Lecture 53 Feature Engineering
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 Download 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 Time Warping
Lecture 66 Machine Learning on Images
Lecture 67 Working with Images
Lecture 68 Finding Similar Images
Lecture 69 Building an Image Similarity Engine
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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