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Data Science
Machine Learning
Python: Build Machine Learning Models in 6 Hours
4.1 (10 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
69 students enrolled

Python: Build Machine Learning Models in 6 Hours

A complete & comprehensive course in which you will create machine learning models with ease!
4.1 (10 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
69 students enrolled
Created by Packt Publishing
Last updated 11/2018
English
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Python: Build Machine Learning Models in 6 Hours
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This course includes
  • 5.5 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Expand all 46 lectures 05:40:20
+ – Getting Started with Machine Learning in Python
25 lectures 02:53:05

This video provides an overview of the entire course.

The Course Overview
Preview 02:03

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

Machine Learning versus Rule-Based Programming
14:14

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

Understanding What Machine Learning Can Do Using the Tasks Framework
05:46

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

Creating Machine-Learned Models with Python and scikit-learn
05:58

This video is all about Supervised vs Unsupervised learning

  • Explore a different view of ML

  • Understand Supervised learning

  • Understand Unsupervised learning

Supervised Versus Unsupervised Learning
08:42

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

Fix your machine learning models by understanding your data source
Preview 08:28

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

Dealing with Missing Values – An Example
09:22

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

Standardization and Normalization to Deal with Variables with Different Scales
07:51

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

Eliminating Duplicate Entries
05:22

This video shows how we learn rules to classify objects

  • Learn what an iris dataset is

  • Get the iris dataset

  • Classify irises by hand

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

Understand logistic regression

  • Know about a linear model

  • Understand a sigmoid function

  • Understand logistic regression

Understanding Logistic Regression – Your First Classifier
07:48

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

Applying Logistic Regression to the Iris Classification Task
06:26

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

Closing Our First Machine Learning Pipeline with a Simple Model Evaluator
05:50

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

Creating Formulas That Predict the Future – A House Price Example
08:05

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

Understanding Linear Regression – Your First Regressor
05:58

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

Applying Linear Regression to the Boston House Price Task
05:10

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

Evaluating Numerical Predictions with Least Squares
05:10

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

  • Learn about unsupervised learning

  • Learn about  clustering

  • Learn about dimensionality reduction

Exploring Unsupervised Learning and Its Usefulness
07:23

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

Finding Groups Automatically with K-means Clustering
05:17

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

Reducing the Number of Variables in Your Data with PCA
05:07

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

Smooth out Your Histograms with Kernel Density Estimation
03:48

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

Create Explainable Models with Decision Trees
09:23

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

Automatic Feature Engineering with Support Vector Machines
06:56

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

Deal with Nonlinear Relationships with Polynomial Regression
06:24

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

Reduce the Number of Learned Rules with Regularization
06:20
Test Your Knowledge
5 questions
+ – Building Predictive Models with Machine Learning and Python
21 lectures 02:47:15

This video provides an overview of the entire course.

The Course Overview
Preview 02:13

Illustrate the main tenets of machine learning - data, model, and feedback loop.

  • Learn how data enables machine learning

  • Learn rule based programming

  • Go through the three ingredients of ML - data, model, feedback loop

Introduction to Machine Learning
07:45

This video shows the various toolsets in Python that allows us to do machine learning.

  • Understand why Python is so good for data science

  • Learn about scikit-learn

  • Learn about Pandas

Meet the Python Machine Learning Stack
09:07

Demonstrate how we can install various Python ML packages.

  • Install and test scikit learn

  • Install and test Pandas

  • Install and test Jupyter notebooks

Making Sure It Works in Your Computer
12:41

Walk through the process of exploring a dataset in Excel.

  • Go through the iris dataset

  • Learn what machine learning task is

  • Learn about the train-test-splits and features

Exploring Your First Dataset
10:19

Quick example on how to build a machine learning model.

  • Go through the linear model

  • Create  a linear model with scikit-learn

  • Fit a linear model

Building Your First Model
10:14

Walkthrough the process of evaluating a machine learning model.

  • Know what’s a good model

  • Go through different types of error metrics

  • Measure the error of our classifier

Assessing Your Model
08:00

Show the common alarm bells of unclean data, how to look for them, and how to assess their impact.

  • Issue 1: missing data

  • Issue 2: multiple pieces of information in one column

  • Issue 3: useless/noisy columns

Finding Issues with Your Data
10:47

Show the common fixes and cleaning techniques for bad data.

  • Fix the missing data

  • Split up features with multiple pieces of information

  • Remove unwanted columns

Using Pandas to Get Your Data Ready for Modeling
11:44

Build a machine learning model that predicts survival rate.

  • Take the cleaned data into scikit-learn

  • Create a Logistic Regression model

  • Assess a Logistic Regression model

Building a Model to Assess Your Chances of Surviving the Titanic
07:46

Explain the intuition behind the power of a model, show the types of models with scikit-learn documentation.

  • Understand Model assumptions, parameters, and hyper parameters

  • Learn about the different types of models

  • Understand the Different flavors of models

What Makes Models Truly Different?
08:08

Use the scikit-learn flowchart to understand how models fit with each other.

  • Go through the factors to consider when picking a class of machine learning models

  • Use the scikit-learn flowchart

Understanding the Advantages and Shortcomings of the Most Popular Models
04:10

Show the three most useful models.

  • Explore SVMs

  • Learn about random forests

  • Learn about  L1/L2 regularized linear models

Trying (and Failing) to Use an SVM, a Random Forest and a Linear Model
06:36

Apply a SVM to the Titanic dataset and iterate.

  • Understand the problem

  • Fix the issues

Fixing Our Issues with Our SVM Model
04:29

Apply a Random Forest to the Titanic dataset and iterate.

  • Understand the problem

  • Fix the issues

Fixing Our Issues with the Random Forest Model
04:32

Explain the core concepts around model tuning.

  • Understand the difference between Parameters and hyper parameters

  • Know what is Global and local minima

What Does it Mean to Tune a Model (Theory)?
04:32

Present the core concepts behind grid searching.

  • Understand grid search

  • Use grid search and other types of search

  • Explore grid search in scikit-learn

Grid Search – Just Try Everything!
02:26

Show how to use grid search for linear models.

  • Explore the Boston House Prices dataset

  • Create a linear model to predict house prices

  • Use grid search on this linear model

Tune a Linear Model to Predict House Prices
06:05

Show how to use grid search for SVMs.

  • Explore the congressional voting dataset

  • Create a SVM to predict partisanship

  • Use grid search to tune SVM

Tune an SVM to Predict a Politician’s Party Based on Their Voting Record
07:49

This video will show you the advanced libraries for machine learning.

  • Explore Tensorflow/Torch

  • Understand Keras/Lasagne

  • Go through spaCy/NLTK/StanfordNLP

Advanced Libraries for Machine Learning
14:16

Explore the next steps in this video.

  • Go through Kaggle

  • Go through the Youtube Channels

  • Go through some of the Books

Good Next Steps – Kaggle, Hackathons, YouTube Channels, and More
13:36
Test Your Knowledge
5 questions
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.