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Development Data Science Machine Learning

Practical Introduction to Machine Learning with Python

Quickly Learn the Essentials of Artificial Intelligence (AI) and Machine Learning (ML)
Rating: 4.8 out of 54.8 (236 ratings)
10,478 students
Created by Madhu Siddalingaiah
Last updated 5/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Fundamentals of Artificial Intelligence (AI) and Machine Learning
  • Practical business applications of machine learning
  • Classification, regression, clustering, anomaly detection
  • How machines learn from data
  • Supervised, unsupervised, reinforcement, and transfer learning
  • How to identify problems suitable for machine learning
  • How to collect and prepare data suitable for training and testing machine learning models
  • Different types of machine learning models and how to choose among them
  • Machine learning development and production deployment process
  • How to train models using GPU instances in the cloud

Course content

7 sections • 58 lectures • 4h 17m total length

  • Preview02:33
  • Explore Machine Learning Examples
    2 questions
  • Preview05:21

  • About this section
    00:42
  • Preview00:34
  • Preview02:08
  • Machine learning in smart speakers
    03:01
  • Learning process
    07:36
  • Preview01:25
  • Computer vision
    01:59
  • Text analytics and voice recognition
    01:20
  • Preview00:34
  • Content Generation
    00:57
  • Internet of things (IoT)
    01:16
  • Preview01:14
  • Introduction to Jupyter Notebook
    03:02
  • Jupyter Notebook: Text Cells
    02:45
  • Jupyter Notebook: Code Cells
    02:50
  • Jupyter notebook: Math Markup and Magic Commands
    04:23
  • Introduction to Notebooks
    4 questions
  • AI and ML
    3 questions

  • What is a model?
    04:10
  • Linear Regression Example
    08:04
  • Working with Jupyter notebook
    2 questions
  • Feature Engineering
    05:00
  • Preview11:57
  • Predicting handwritten digits
    2 questions
  • Preview08:50
  • Single and Multilayer Perceptrons
    03:38
  • Backpropagation
    04:05
  • Gradient descent
    2 questions
  • Breakthroughs in deep neural networks
    05:21
  • Preview06:53
  • Expert performance
    14:47

  • Introduction
    00:17
  • Preview06:15
  • Preview09:44
  • Experiment with training data size
    1 question
  • Unsupervised learning
    03:45
  • Unsupervised learning in practice
    2 questions
  • Reinforcement learning
    02:28
  • Reinforcement learning examples
    05:41
  • Transfer learning
    03:45
  • Self-supervised learning
    02:03
  • Self-supervised examples
    05:32
  • Self-supervised assignment
    2 questions

  • Introduction
    00:35
  • Natural language text
    03:05
  • Preview09:48
  • Loading data into Google Colab
    08:13
  • Text analytics services
    08:01
  • Clustering example
    05:06
  • Image recognition
    05:00
  • Existing image models
    05:37
  • Image models for non-image problems
    03:23
  • Speech recognition and generation
    02:28
  • Speech recognition and generation services
    04:43

  • Preview02:30
  • Data collection and preparation
    03:34
  • Developing and tuning a model
    05:27
  • Machine learning frameworks
    04:52
  • Speeding up training
    02:18
  • CPUs, GPUs, and FPGAs
    07:46
  • Preview02:38

  • Next steps
    06:13
  • Preview05:43
  • Conclusion
    00:30

Requirements

  • Some Python programming is helpful, but not required
  • Math concepts such as linear algebra and calculus are helpful, but not required

Description

LinkedIn released it's annual "Emerging Jobs" list, which ranks the fastest growing job categories. The top role is Artificial Intelligence Specialist, which is any role related to machine learning. Hiring for this role has grown 74% in the past few years!

Machine learning is the technology behind self driving cars, smart speakers, recommendations, and sophisticated predictions. Machine learning is an exciting and rapidly growing field full of opportunities. In fact, most organizations can not find enough AI and ML talent today.

If you want to learn what machine learning is and how it works, then this course is for you. This course is targeted at a broad audience at an introductory level. By the end of this course you will understand the benefits of machine learning, how it works, and what you need to do next. If you are a software developer interested in developing machine learning models from the ground up, then my second course, Practical Machine Learning by Example in Python might be a better fit.

There are a number of machine learning examples demonstrated throughout the course. Code examples are available on github. You can run each examples using Google Colab. Colab is a free, cloud-based machine learning and data science platform that includes GPU support to reduce model training time. All you need is a modern web browser, there's no software installation is required!

July 2019 course updates include lectures and examples of self-supervised learning. Self-supervised learning is an exciting technique where machines learn from data without the need for expensive human labels. It works by predicting what happens next or what's missing in a data set. Self-supervised learning is partly inspired by early childhood learning and yields impressive results. You will have an opportunity to experiment with self-supervised learning to fully understand how it works and the problems it can solve.

August 2019 course updates include a step by step demo of how to load data into Google Colab using two different methods. Google Colab is a powerful machine learning environment with free GPU support. You can load your own data into Colab for training and testing.

March 2020 course updates migrate all examples to Google Colab and Tensorflow 2. Tensorflow 2 is one of the most popular machine learning frameworks used today. No software installation is required.

April/May 2020 course updates streamline content, include Jupyter notebook lectures and assignment. Jupyter notebook is the preferred environment for machine learning development.

Who this course is for:

  • IT managers, business analysts, software architects, and developers interested in a quick start into the exciting and rapidly growing field of machine learning.
  • Business analysts or non-technical people who want to leverage their skills to add value in machine learning development project
  • Anyone wanting to learn where they can be productive in a changing economy where machines are climbing the corporate ladder

Instructor

Madhu Siddalingaiah
Technology Consultant
Madhu Siddalingaiah
  • 4.5 Instructor Rating
  • 802 Reviews
  • 40,588 Students
  • 2 Courses

Madhu is a professional machine learning practitioner and data scientist. Madhu has three decades of interdisciplinary experience applying great technology for many different organizations, such as FINRA, Apple, Blue Cross/Blue Shield, Food & Drug Administration, and the US Department of Defense.

Over the years, Madhu has developed numerous innovative products and solutions at start ups and established companies. Examples include: machine learning solutions, Internet of Things (IoT) devices, big data systems, mobile medical applications, as well as enterprise applications and specialized hardware for space science, 3D graphics, and wireless communications.

Madhu has been awarded US and EU patents and has authored multiple books and training courses. Madhu has presented papers at technology conferences all over the world, including London, Munich, and Sydney, and many US locations. Madhu is also a private helicopter pilot and enjoys playing electric guitar.

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