Practical Machine Learning by Example in Python paid course free. You will learn A Deep Dive into Building Machine Learning and Deep Learning models in this complete course.
- Develop complete machine learning/deep learning solutions in Python
- Write and test Python code interactively using Jupyter notebooks
- Build, train, and test deep learning models using the popular Tensorflow 2 and Keras APIs
- Neural network fundamentals by building models from the ground up using only basic Python
- Manipulate multidimensional data using NumPy
- Load and transform structured data using Pandas
- Build high quality, eye catching visualizations with Matplotlib
- Reduce training time using free Google Colab GPU instances in the cloud
- Recognize images using Convolutional Neural Networks (CNNs)
- Make recommendations using collaborative filtering
- Detect fraud using autoencoders
- Improve model accuracy and eliminate overfitting
Practical Machine Learning by Example in Python Course Requirements
- Basic software development skills
- Basic high school math, such as trigonometry and algebra
Practical Machine Learning by Example in Python Course Description
Is it true that you are an engineer keen on building AI and profound learning models? Would you like to be capable in the quickly developing field of man-made brainpower? One of the quickest and least demanding approaches to get familiar with these abilities is by working through commonsense active models.
LinkedIn delivered it’s yearly “Arising Jobs” list, which positions the quickest developing position classifications. The top job is Artificial Intelligence Specialist, which is any job identified with AI. Recruiting for this job has developed 74% in the previous few years!
In this course, you will work through a few reasonable, AI models, for example, picture acknowledgment, conclusion examination, extortion location, and then some. All the while, you will figure out how to utilize current structures, like Tensorflow 2/Keras, NumPy, Pandas, and Matplotlib. You will likewise figure out how utilize amazing and free advancement conditions in the cloud, similar to Google Colab.
Every model is autonomous and follows a predictable construction, so you can work through models in any request. In every model, 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.
clear explanations..to the point and no jargon..neat presentation of notebooks with codes..it’s a step by step guide on creating machine learning models using Google colab..the models explained here are basic and thus perfect for beginners ,to understand how machine learning models are created based on the given problem and about techniques used to improve the accuracy..with the resources shared and Mr.Madhu’s immediate response to messages/QA,one can learn more about a topic..highly recommended to all machine learning enthusiasts. – Ashraf UI
The cours is easy to understand and well presented, same thing for the practical examples Using google colab was a very good idea to present the course and to do the exercices , we can easily test a function or a line of code. The last three sections are very intresting, they are practical exercices for deep learning well presented and commented – Iheb GANDOUZ
The way it is explained is really cool. I used to be bored after an hour during lectures, but the guide somehow makes it very interesting…. – Anu Priya J
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