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Practical Machine Learning by Example in Python

Online Courses Udemy - Learn modern machine learning, deep learning, and data science skills
Created by Madhu Siddalingaiah | English [Auto-generated]

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Description
Are you a developer interested in becoming a machine learning engineer or data scientist? Do you want to be proficient in the rapidly growing field of artificial intelligence? One of the fastest and easiest ways to learn these skills is by working through practical hands-on examples.

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!

In this course, you will work through several practical, machine learning examples, such as image recognition, sentiment analysis, fraud detection, and more. In the process, you will learn how to use modern frameworks, such as Tensorflow 2/Keras, NumPy, Pandas, and Matplotlib. You will also learn how use powerful and free development environments in the cloud, like Google Colab.

Each example is independent and follows a consistent structure, so you can work through examples in any order.  In each example, you will learn:

The nature of the problem

How to analyze and visualize data

How to choose a suitable model

How to prepare data for training and testing

How to build, test, and improve a machine learning model

Answers to common questions

What to do next

Of course, there are some required foundations you will need for each example. Foundation sections are presented as needed. You can learn what interests you, in the order you want to learn it, on your own schedule.

Why choose me as your instructor?

Practical experience. I actively develop real world machine learning systems. I bring that experience to each course.

Teaching experience. I've been writing and teaching for over 20 years.

Commitment to quality. I am constantly updating my courses with improvements and new material.

Ongoing support. Ask me anything! I'm here to help. I answer every question or concern promptly.

January 2020 updates:

New mathematics and machine learning foundation section including

Logistic regression, loss and cost functions, gradient descent, and backpropagation

All examples updated to use Tensorflow 2 (Tensorflow 1 examples are available also)

Jupyter note introduction

Python quick start

Basic linear algebra

March 2020 updates:

A sentiment and natural language processing section

This includes a modern BERT classification model with surprisingly high accuracy

April/May 2020 updates:

Numerous assignment improvements, e.g. self-paced or guided approach

Add lectures on Google Colab, Python quick start, classify your own images and more!



Who this course is for:
Anyone interesting in developing machine learning and deep learning skills

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