Python Machine Learning- Projects, Tips and Troubleshooting

Python Machine Learning: Projects, Tips and Troubleshooting, available at $19.99, has an average rating of 3.88, with 113 lectures, 3 quizzes, based on 8 reviews, and has 107 subscribers.

You will learn about Use pre-written libraries in python to work with powerful algorithms. Tips and tricks to speed up your modeling process and obtain better results. Make predictions using advanced regression analysis with Python. Modern techniques for solving supervised learning problems. Build your own recommendation engine and perform collaborative filtering. Eliminate common data wrangling problems in Pandas and scikit-learn. Troubleshoot advanced models such as Random Forests and SVMs. Wrangling with unsupervised learning and the curse of dimensionality. Solving prediction visualization issues with Matplotlib. Perform common natural language processing featuring engineering tasks. This course is ideal for individuals who are This course is perfect for: or Data Scientists, Developers who are familiar with basic Python programming and want to build efficient, faster, and progressive Machine Learning models to tackle real data. It is particularly useful for This course is perfect for: or Data Scientists, Developers who are familiar with basic Python programming and want to build efficient, faster, and progressive Machine Learning models to tackle real data.

Enroll now: Python Machine Learning: Projects, Tips and Troubleshooting

Summary

Title: Python Machine Learning: Projects, Tips and Troubleshooting

Price: $19.99

Average Rating: 3.88

Number of Lectures: 113

Number of Quizzes: 3

Number of Published Lectures: 113

Number of Published Quizzes: 3

Number of Curriculum Items: 116

Number of Published Curriculum Objects: 116

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Use pre-written libraries in python to work with powerful algorithms.

Tips and tricks to speed up your modeling process and obtain better results.

Make predictions using advanced regression analysis with Python.

Modern techniques for solving supervised learning problems.

Build your own recommendation engine and perform collaborative filtering.

Eliminate common data wrangling problems in Pandas and scikit-learn.

Troubleshoot advanced models such as Random Forests and SVMs.

Wrangling with unsupervised learning and the curse of dimensionality.

Solving prediction visualization issues with Matplotlib.

Perform common natural language processing featuring engineering tasks.

Who Should Attend

This course is perfect for:

Data Scientists, Developers who are familiar with basic Python programming and want to build efficient, faster, and progressive Machine Learning models to tackle real data.

Target Audiences

This course is perfect for:

Data Scientists, Developers who are familiar with basic Python programming and want to build efficient, faster, and progressive Machine Learning models to tackle real data.

Machine learning is one of the most sought-after skills in the market giving you powerful insights into data. Today, implementations of Machine Learning are adopted throughout Industry and its concepts are many. Python makes this easier with its huge set of libraries that can be used for Machine Learning. The effective blend of Machine Learning with Python helps in implementing solutions to real-world problems as well as automating analytical model.

This comprehensive 4-in-1 course follows a step-by-step practical approach to building powerful Machine Learning models using Python. Initially, you’ll use pre-written libraries in python to work with powerful algorithms and get an intuitive understanding of where to use which machine learning approach. You’ll explore Tips and tricks to speed up your modeling process and obtain better results. Moving further, you’ll learn modern techniques for solving supervised learning problems. Finally, you’ll eliminate common data wrangling problems in Pandas and scikit-learn as well as perform common natural language processing featuring engineering tasks.

By the end of the course, you’ll explore practical and unique solutions to common Machine Learning problems to avoid any roadblocks while working with the Python data science ecosystem.

Contents and Overview

This training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Python Machine Learning in 7 Days, covers building powerful Machine Learning models using Python with hands-on practical examples in just a week. In this course, you will be introduced to a new machine learning aspect in each section followed by a practical assignment as homework to help you in efficiently implement the learnings in a practical manner. With the systematic and fast-paced approach to this course, learn machine learning using Python in the most practical and structured way to develop machine learning projects in Python in a week. This course is structured to unlock the potential of Python machine learning in the shortest amount of time. If you are looking to upgrade your machine learning skills using Python in the quickest possible time, then this course is for you!

The second course, Python Machine Learning Projects, covers hands-on Supervised, unsupervised learning, and more. 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. By the end of the course, you will have learned to apply various machine learning algorithms and will have mastered Python’s packages and libraries to facilitate computation. You will be able to implement your own machine learning models after taking this course.

The third course, Python Machine Learning Tips, Tricks, and Techniques, covers transforming your simple machine learning model into a cutting edge powerful version. In this course, you will learn from a top Kaggle master to upgrade your Python skills with the latest advancements in Python.

It is essential to keep upgrading your machine learning skills as there are immense advancements taking place every day. In this course, you will get hands-on experience of solving real problems by implementing cutting-edge techniques to significantly boost your Python Machine Learning skills and, as a consequence, achieve optimized results in almost any project you are working on. Each technique we cover is itself enough to improve your results. However; combining them together is where the real magic is. Throughout the course, you will work on real datasets to increase your expertise and keep adding new tools to your machine learning toolbox. By the end of this course, you will know various tips, tricks, and techniques to upgrade your machine learning algorithms to reduce common problems, all the while building efficient machine learning models.

The fourth course, Troubleshooting Python Machine Learning, covers quick fixes for all your Python Machine Learning frustrations. We have systematically researched common ML problems documented online around data wrangling, debugging models such as Random Forests and SVMs, and visualizing tricky results. We leverage statistics from Stack Overflow, Medium, and GitHub to get a cross-section of what data scientists struggle with. We have collated for you the top issues, such as retrieving the most important regression features and explaining your results after clustering, and their corresponding solutions. We present these case studies in a problem-solution format, making it very easy for you to incorporate this into your knowledge. Taking this course will help you to precisely 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 explore practical and unique solutions to common Machine Learning problems to avoid any roadblocks while working with the Python data science ecosystem.

About the Authors

Arish Ali started his machine learning journey 5 years ago by winning an all India machine learning competition conducted by the Indian Institute of Science and Microsoft. He was a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has also worked on some of the cutting edge problems of Multi-Touch Attribution Modelling, Market Mix Modelling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at Bridge School of Management, which offers its course in Predictive Business Analytics along with North-western University (SPS). Currently, he is working at a mental health startup called Bemo as an AI developer where his role is to help automate the therapy provided to users and make it more personalized.

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.

Valeriy Babushkin has done an M. Sc. and has 5+ years’ experience in industrial data science and academia. He is a Kaggle competition master and a 2018 IEEE SP Cup finalist. He has been a Data Science Team Lead at Yandex (the largest search engine in Russia; it outperforms Google) and runs an online taxi service (he acquired Uber in Russia and 15 other countries) and the biggest e-commerce platform in Russia. He was also a Head of Data Science at Monetha. Monetha is creating a universal, transferable, immutable trust, and reputation system combined with a payment solution. Finally, he is decentralized and empowered by the Ethereum Blockchain.

Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, QuantCopy 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 feedback into how our AI generated content. Prior to founding QuantCopy, Rudy ran High Dimension.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 High Dimension.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 to learn deeply 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.

Course Curriculum

Chapter 1: Python Machine Learning in 7 Days

Lecture 1: The Course Overview

Lecture 2: Setting Up Your Machine Learning Environment

Lecture 3: Exploring Types of Machine Learning

Lecture 4: Using Scikit-learn for Machine Learning

Lecture 5: Assignment – Train Your First Pre-built Machine Learning Model

Lecture 6: Supervised Learning Algorithm

Lecture 7: Architecture of a Machine Learning System

Lecture 8: Machine Learning Model and Its Components

Lecture 9: Linear Regression

Lecture 10: Predicting Weight Using Linear Regression

Lecture 11: Assignment – Predicting Energy Output of a Power Plant

Lecture 12: Review of Predicting Energy Output of a Power Plant

Lecture 13: Logistic Regression

Lecture 14: Classifying Images Using Logistic Regression

Lecture 15: Support Vector Machines

Lecture 16: Kernels in a SVM

Lecture 17: Classifying Images Using Support Vector Machines

Lecture 18: Assignment – Start Image Classifying Using Support Vector Machines

Lecture 19: Review of Classifying Images Using Support Vector Machines

Lecture 20: Model Evaluation

Lecture 21: Better Measures than Accuracy

Lecture 22: Understanding the Results

Lecture 23: Improving the Models

Lecture 24: Assignment – Getting Better Test Sample Results by Measuring Model Performance

Lecture 25: Review of Getting Better Test Sample Results by Measuring Model Performance

Lecture 26: Unsupervised Learning

Lecture 27: Clustering

Lecture 28: K-means Clustering

Lecture 29: Determining the Number of Clusters

Lecture 30: Assignment – Write Your Own Clustering Implementation for Customer Segmentation

Lecture 31: Review of Clustering Customers Together

Lecture 32: Why Neural Network

Lecture 33: Parts of a Neural Network

Lecture 34: Working of a Neural Network

Lecture 35: Improving the Network

Lecture 36: Assignment – Build a Sentiment Analyzer Based on Social Network Using ANN

Lecture 37: Review of Building a Sentiment Analyser ANN

Lecture 38: Decision Trees

Lecture 39: Working of a Decision Tree

Lecture 40: Techniques to Further Improve a Model

Lecture 41: Random Forest as an Improved Machine Learning Approach

Lecture 42: Weekend Task – Solving Titanic Problem Using Random Forest

Chapter 2: 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

Chapter 3: Python Machine Learning Tips, Tricks, and Techniques

Lecture 1: The Course Overview

Lecture 2: Using Feature Scaling to Standardize Data

Lecture 3: Implementing Feature Engineering with Logistic Regression

Lecture 4: Extracting Data with Feature Selection and Interaction

Lecture 5: Combining All Together

Lecture 6: Build Model Based on Real-World Problems

Lecture 7: Support Vector Machines

Lecture 8: Implementing kNN on the Data Set

Lecture 9: Decision Tree as Predictive Model

Lecture 10: Tricks with Dimensionality Reduction

Lecture 11: Combining All Together

Lecture 12: Random Forest for Classification

Lecture 13: Gradient Boosting Trees and Bayes Optimization

Lecture 14: CatBoost to Handle Categorical Data

Lecture 15: Implement Blending

Lecture 16: Implement Stacking

Lecture 17: Memory-Based Collaborative Filtering

Lecture 18: Item-to-Item Recommendation with kNN

Lecture 19: Applying Matrix Factorization on Datasets

Lecture 20: Wordbatch for Real-World Problem

Lecture 21: Validation Dataset Tuning

Lecture 22: Regularizing Model to Avoid Overfitting

Lecture 23: Adversarial Validation

Lecture 24: Perform Metric Selection on Real Data

Chapter 4: Troubleshooting Python Machine Learning

Lecture 1: The Course Overview

Lecture 2: Splitting Your Datasets for Train, Test, and Validate

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 2 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 1 votes

5 stars: 5 votes

Frequently Asked Questions

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