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Python: Build Machine Learning Models in 6 Hours
Rating: 4.1 out of 5(21 ratings)
148 students

Python: Build Machine Learning Models in 6 Hours

A complete & comprehensive course in which you will create machine learning models with ease!
Created byPackt Publishing
Last updated 11/2018
English

What you'll 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

Course content

2 sections • 46 lectures • 5h 40m total length

  • The Course Overview2:03

    This video provides an overview of the entire course.

  • Machine Learning versus Rule-Based Programming14:14

    This video is all about Machine Learning versus rule-based programming.

    • Learn how does data enable machine learning

    • Learn what rule based programming

    • Know the three ingredients of ML - data, model,  and feedback loop

  • Understanding What Machine Learning Can Do Using the Tasks Framework5:46

    In this video, we will understand what Machine Learning can do using the tasks framework

    • Understand what Machine Learning can do using the tasks framework

    • Predict numbers

    • Know more about Classification

  • Creating Machine-Learned Models with Python and scikit-learn5:58

    In this video, we will learn to create machine learned models with Python and scikit-learn

    • Learn why is python so good for data science

    • Learn about scikit- learn

    • Learn about Pandas

  • Supervised Versus Unsupervised Learning8:42

    This video is all about Supervised vs Unsupervised learning

    • Explore a different view of ML

    • Understand Supervised learning

    • Understand Unsupervised learning

  • Fix your machine learning models by understanding your data source8:28

    Fix your machine learning models by understanding your data source

    • Know the importance of data

    • Know what to look out for in your data

    • Boston house prices example

  • Dealing with Missing Values – An Example9:22

    In this video, we will learn how to Deal with missing values

    • Remove rows with missing data

    • Fill in data with averages

    • Explore other data imputation strategies

  • Standardization and Normalization to Deal with Variables with Different Scales7:51

    Understand the concept of Standardization and normalization to deal with variables with different scales

    • Learn about Standardization

    • Perform Standardization using scikit-learn

    • Perform Normalization using scikit-learn

  • Eliminating Duplicate Entries5:22

    In this video we will be eliminating duplicate entries

    • Understand the issue with duplicate data

    • Put your data into a pandas data-frame

    • Remove duplicate data in your data-frame

  • How Do We Learn Rules to Classify Objects?10:14

    This video shows how we learn rules to classify objects

    • Learn what an iris dataset is

    • Get the iris dataset

    • Classify irises by hand

  • Understanding Logistic Regression – Your First Classifier7:48

    Understand logistic regression

    • Know about a linear model

    • Understand a sigmoid function

    • Understand logistic regression

  • Applying Logistic Regression to the Iris Classification Task6:26

    In this video, we will be applying logistic regression to the iris classification task

    • Explore the scikit-learn model framework

    • Load the model

    • Fit the model

  • Closing Our First Machine Learning Pipeline with a Simple Model Evaluator5:50

    In this video we will be closing our first Machine Learning pipeline with a simple model evaluator

    • Predict the test set

    • Store predictions

    • Evaluate your predictions

  • Creating Formulas That Predict the Future – A House Price Example8:05

    In this video we will be creating formulas that predict the future - A house price example

    • Revisit the house prices dataset

    • Understand what are we predicting

    • Data visualization on prices

  • Understanding Linear Regression – Your First Regressor5:58

    In this video we will be understanding linear regression - your first regressor

    • Explore another linear model

    • Differentiate between logistic regression and linear  regression

    • Do price predictions by hand

  • Applying Linear Regression to the Boston House Price Task5:10

    In this video we will be applying linear regression to the Boston house price task

    • Load a linear regression model

    • Setting up linear regression, what are the options?

    • Applying linear regression

  • Evaluating Numerical Predictions with Least Squares5:10

    In this video we will be evaluating numerical predictions with least squares

    • Learn about least squares

    • Pick evaluators in scikit-learn

    • Load the appropriate error metric

  • Exploring Unsupervised Learning and Its Usefulness7:23

    In this video we will be exploring unsupervised learning and its usefulness

    • Learn about unsupervised learning

    • Learn about  clustering

    • Learn about dimensionality reduction

  • Finding Groups Automatically with K-means Clustering5:17

    In this video, we will be finding groups automatically with k-means clustering

    • Learn how does k-means do clustering

    • Use scikit-learn to do k-means

  • Reducing the Number of Variables in Your Data with PCA5:07

    In this video, we will be reducing the number of variables in your data with PCA

    • Learn about PCA

    • Use scikit-learn to do PCA

  • Smooth out Your Histograms with Kernel Density Estimation3:48

    In this video, we will Smooth out your histograms with kernel density estimation

    • Learn about kernel density estimation

    • Use scikit-learn to do kernel density estimation

  • Create Explainable Models with Decision Trees9:23

    In this video, we will be create explainable models with decision trees.

    • Fir a simple decision tree to the iris dataset

    • Use an inner property of our decision tree model to extract rules

    • Construct a recursive visitor to the decision tree

  • Automatic Feature Engineering with Support Vector Machines6:56

    In this video, we will be looking into automatic feature engineering with support vector machines.

    • Learn about SVM

    • Implement SVMs on the Boston House Price Data

  • Deal with Nonlinear Relationships with Polynomial Regression6:24

    In this video, we will Deal with non-linear relationships with polynomial regression.

    • Preprocess your feature space

    • Combine linear regression and polynomial features

    • Performing polynomial regression on dummy data

  • Reduce the Number of Learned Rules with Regularization6:20

    In this video, we will reduce the number of learned rules with regularization.

    • Learn about Regularization

    • Learn what does LI, L2 and alpha mean

    • Implement regularized linear regression

  • Test Your Knowledge

Requirements

  • Some knowledge of mathematics and Python is assumed.

Description

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.

Who this course is 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.

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