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Practical Introduction to Machine Learning with Python

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Practical Introduction to Machine Learning with Python, available at $54.99, has an average rating of 4.3, with 66 lectures, 1 quizzes, based on 269 reviews, and has 10607 subscribers.

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

Enroll now: Practical Introduction to Machine Learning with Python

Summary

Title: Practical Introduction to Machine Learning with Python

Price: $54.99

Average Rating: 4.3

Number of Lectures: 66

Number of Quizzes: 1

Number of Published Lectures: 58

Number of Published Quizzes: 1

Number of Curriculum Items: 75

Number of Published Curriculum Objects: 67

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • 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

Target Audiences

  • 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

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course Quick Tips

Chapter 2: What is Artificial Intelligence (AI) and Machine Learning (ML)?

Lecture 1: About this section

Lecture 2: Opening questions

Lecture 3: Let's ask a machine!

Lecture 4: Machine learning in smart speakers

Lecture 5: Learning process

Lecture 6: Machine learning is everywhere

Lecture 7: Computer vision

Lecture 8: Text analytics and voice recognition

Lecture 9: Anomaly detection

Lecture 10: Content Generation

Lecture 11: Internet of things (IoT)

Lecture 12: Not build self driving car?

Lecture 13: Introduction to Jupyter Notebook

Lecture 14: Jupyter Notebook: Text Cells

Lecture 15: Jupyter Notebook: Code Cells

Lecture 16: Jupyter notebook: Math Markup and Magic Commands

Chapter 3: Machine Learning Models

Lecture 1: What is a model?

Lecture 2: Linear Regression Example

Lecture 3: Feature Engineering

Lecture 4: Handwritten digits example

Lecture 5: Machine Learning timeline

Lecture 6: Single and Multilayer Perceptrons

Lecture 7: Backpropagation

Lecture 8: Breakthroughs in deep neural networks

Lecture 9: Power of deep neural networks

Lecture 10: Expert performance

Chapter 4: Learning Style

Lecture 1: Introduction

Lecture 2: Supervised learning

Lecture 3: Training Data Size

Lecture 4: Unsupervised learning

Lecture 5: Reinforcement learning

Lecture 6: Reinforcement learning examples

Lecture 7: Transfer learning

Lecture 8: Self-supervised learning

Lecture 9: Self-supervised examples

Chapter 5: Practical examples

Lecture 1: Introduction

Lecture 2: Natural language text

Lecture 3: Sentiment analysis example

Lecture 4: Loading data into Google Colab

Lecture 5: Text analytics services

Lecture 6: Clustering example

Lecture 7: Image recognition

Lecture 8: Existing image models

Lecture 9: Image models for non-image problems

Lecture 10: Speech recognition and generation

Lecture 11: Speech recognition and generation services

Chapter 6: Development process

Lecture 1: Conventional and ML development process

Lecture 2: Data collection and preparation

Lecture 3: Developing and tuning a model

Lecture 4: Machine learning frameworks

Lecture 5: Speeding up training

Lecture 6: CPUs, GPUs, and FPGAs

Lecture 7: Retraining

Chapter 7: Next steps

Lecture 1: Next steps

Lecture 2: The future

Lecture 3: Conclusion

Instructors

Rating Distribution

  • 1 stars: 6 votes
  • 2 stars: 4 votes
  • 3 stars: 26 votes
  • 4 stars: 106 votes
  • 5 stars: 127 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!

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