Python- Build Machine Learning Models in 6 Hours

Python: Build Machine Learning Models in 6 Hours, available at $44.99, has an average rating of 4.1, with 46 lectures, 2 quizzes, based on 18 reviews, and has 136 subscribers.

You will learn about Learn the core concepts of machine learning in Python Clean your data to optimize how it feeds into your machine learning models Perform regression in a supervised learning setting, so that you can predict numbers, prices, and conversion rates Perform classification in a supervised-learning setting, teaching the model to distinguish between different plants, discussion topics, and objects Measure and evaluate your Machine-Learning pipeline, so that you can improve your solution over time Read, explore, clean, and prepare your data using Pandas, the most popular library for analyzing data tables Use the Scikit-Learn library to deploy ready-built models, train them, and see results in just a few lines of code Use hyper-parameter optimization to get the best possible version of each model for your specific application This course is ideal for individuals who are This course is aimed at novice data scientists and developers who want to get started with machine learning in Python. Developers who are curious about building and deploying machine learning-based models will find that this course will guide them to understand why some models are better than others at tackling certain challenges. It is particularly useful for This course is aimed at novice data scientists and developers who want to get started with machine learning in Python. Developers who are curious about building and deploying machine learning-based models will find that this course will guide them to understand why some models are better than others at tackling certain challenges.

Enroll now: Python: Build Machine Learning Models in 6 Hours

Summary

Title: Python: Build Machine Learning Models in 6 Hours

Price: $44.99

Average Rating: 4.1

Number of Lectures: 46

Number of Quizzes: 2

Number of Published Lectures: 46

Number of Published Quizzes: 2

Number of Curriculum Items: 48

Number of Published Curriculum Objects: 48

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the core concepts of machine learning in Python

Clean your data to optimize how it feeds into your machine learning models

Perform regression in a supervised learning setting, so that you can predict numbers, prices, and conversion rates

Perform classification in a supervised-learning setting, teaching the model to distinguish between different plants, discussion topics, and objects

Measure and evaluate your Machine-Learning pipeline, so that you can improve your solution over time

Read, explore, clean, and prepare your data using Pandas, the most popular library for analyzing data tables

Use the Scikit-Learn library to deploy ready-built models, train them, and see results in just a few lines of code

Use hyper-parameter optimization to get the best possible version of each model for your specific application

Who Should Attend

This course is aimed at novice data scientists and developers who want to get started with machine learning in Python. Developers who are curious about building and deploying machine learning-based models will find that this course will guide them to understand why some models are better than others at tackling certain challenges.

Target Audiences

This course is aimed at novice data scientists and developers who want to get started with machine learning in Python. Developers who are curious about building and deploying machine learning-based models will find that this course will guide them to understand why some models are better than others at tackling certain challenges.

Given the constantly increasing amounts of data they’re faced with, programmers and data scientists have to come up with better solutions to make machines smarter and reduce manual work along with finding solutions to the obstacles faced in between. Python comes to the rescue to craft better solutions and process them effectively.

This comprehensive 2-in-1 course teaches you how to perform different machine learning tasks along with fixing common machine learning problems you face in your day-to-day tasks. You will learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. You will also use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups. Further to get a complete hold on the technology, you will work with tools using which you can build predictive models in Python.

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

In the first course, Getting Started with Machine Learning in Python, you will learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. You will then use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups. You will also learn to understand and estimate the value of your dataset. Next, you will learn how to clean data for your application, and how to recognize which machine learning task you are dealing with.
The second course, Building Predictive Models with Machine Learning and Python, will introduce you to tools with which you can build predictive models with Python, the core of a Data Scientist’s toolkit. Through some really interesting examples, the course will take you through a variety of challenges: predicting the value of a house in Boston, the batting average of a baseball player, their survival chances had they been on the Titanic, or any other number of other interesting problems.

By the end of this course, you will be able to take the Python machine learning toolkit and apply it to your own projects to build and deploy machine learning models in just a few lines of code.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

Colibri Digital is 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 such as 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 each of them to make better sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.

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 feed-back into how our AI generates content. Prior to founding QuantCopy, 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 about reinforcement learning in depth, 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: Getting Started with Machine Learning in Python

Lecture 1: The Course Overview

Lecture 2: Machine Learning versus Rule-Based Programming

Lecture 3: Understanding What Machine Learning Can Do Using the Tasks Framework

Lecture 4: Creating Machine-Learned Models with Python and scikit-learn

Lecture 5: Supervised Versus Unsupervised Learning

Lecture 6: Fix your machine learning models by understanding your data source

Lecture 7: Dealing with Missing Values – An Example

Lecture 8: Standardization and Normalization to Deal with Variables with Different Scales

Lecture 9: Eliminating Duplicate Entries

Lecture 10: How Do We Learn Rules to Classify Objects?

Lecture 11: Understanding Logistic Regression – Your First Classifier

Lecture 12: Applying Logistic Regression to the Iris Classification Task

Lecture 13: Closing Our First Machine Learning Pipeline with a Simple Model Evaluator

Lecture 14: Creating Formulas That Predict the Future – A House Price Example

Lecture 15: Understanding Linear Regression – Your First Regressor

Lecture 16: Applying Linear Regression to the Boston House Price Task

Lecture 17: Evaluating Numerical Predictions with Least Squares

Lecture 18: Exploring Unsupervised Learning and Its Usefulness

Lecture 19: Finding Groups Automatically with K-means Clustering

Lecture 20: Reducing the Number of Variables in Your Data with PCA

Lecture 21: Smooth out Your Histograms with Kernel Density Estimation

Lecture 22: Create Explainable Models with Decision Trees

Lecture 23: Automatic Feature Engineering with Support Vector Machines

Lecture 24: Deal with Nonlinear Relationships with Polynomial Regression

Lecture 25: Reduce the Number of Learned Rules with Regularization

Chapter 2: Building Predictive Models with Machine Learning and Python

Lecture 1: The Course Overview

Lecture 2: Introduction to Machine Learning

Lecture 3: Meet the Python Machine Learning Stack

Lecture 4: Making Sure It Works in Your Computer

Lecture 5: Exploring Your First Dataset

Lecture 6: Building Your First Model

Lecture 7: Assessing Your Model

Lecture 8: Finding Issues with Your Data

Lecture 9: Using Pandas to Get Your Data Ready for Modeling

Lecture 10: Building a Model to Assess Your Chances of Surviving the Titanic

Lecture 11: What Makes Models Truly Different?

Lecture 12: Understanding the Advantages and Shortcomings of the Most Popular Models

Lecture 13: Trying (and Failing) to Use an SVM, a Random Forest and a Linear Model

Lecture 14: Fixing Our Issues with Our SVM Model

Lecture 15: Fixing Our Issues with the Random Forest Model

Lecture 16: What Does it Mean to Tune a Model (Theory)?

Lecture 17: Grid Search – Just Try Everything!

Lecture 18: Tune a Linear Model to Predict House Prices

Lecture 19: Tune an SVM to Predict a Politician’s Party Based on Their Voting Record

Lecture 20: Advanced Libraries for Machine Learning

Lecture 21: Good Next Steps – Kaggle, Hackathons, YouTube Channels, and More

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 1 votes

2 stars: 2 votes

3 stars: 3 votes

4 stars: 4 votes

5 stars: 8 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!