Tag: Classification

You Will Learn how to implement some of the most common machine learning algorithms in PHP

You will Learn about the some of the common algorithms like classification, regression, clustering

You will learn about Supervised and Unsupervised learning

You will NOT learn the details and mathmatics of each algorithm. Our focus is mainly on implementing them in PHP

You will Learn about the steps to build a machine learning model

You will Learn how to divide your data to training set and test set

You will Learn how to train your machine learning model

You will Learn how to make prdictions

You will learn about the persistency of your model

Requirements

• Basic knowledge of Machine learning is a plus because we are not going though the details of each algorithm and our main focus is on the implementation in PHP

Description

WHY Machine Learning

Machine learning is a rapidly growing field that is changing the way technology and solving complex problems.

Machine learning is widely used in industries such as healthcare, finance, marketing, and self-driving cars to automate processes, improve decision making, and provide personalized experiences to customers.

The amount of data generated by society is continually growing, further increasing the demand for skilled machine learning practitioners.

Learning machine learning provides valuable skills for a career in technology and data science.

The demand for machine learning talent is growing at a rapid pace, with the number of job postings for machine learning roles increasing by over 75% in the past 5 years.

WHY PHP

PHP is used in more than 70 percent of the websites across the Internet. That’s HUGE!

PHP is more alive than ever! It’s simple yet very powerful. It’s secure. It’s scalable.It’s very easy to learn.

Just to get an idea of how powerful PHP is, Websites like Facebook, Wikipedia, Slack, MailChimp, Flickr, SourceForge, Tumblr, Etsy and Yahoo have PHP as their core.

oh and not to forget, the biggest blogging system on the web (WordPress), is powered by PHP.

enough teasing let’s get started with Machine Learning in PHP.

In this course:

• You Will Learn how to implement some of the most common machine learning algorithms in PHP
• You will Learn about the some of the common algorithms like classification, regression, clustering
• You will learn about Supervised and Unsupervised learning
• You will NOT learn the details and mathmatics of each algorithm. Our focus is mainly on implementing them in PHP
• You will Learn about the steps to build a machine learning model
• You will Learn how to divide your data to training set and test set
• You will Learn how to train your machine learning model
• You will Learn how to make prdictions
• You will learn about the persistency of your model
• and a lot more

Prior Knowledge

Basic knowledge of Machine learning is a plus because we are not going though the details of each algorithm and our main focus is on the implementation in PHP

Basic Knowledge about PHP is a plus.

This Course is for:

• PHP Developers who want to start their journey in Machine Learning
• Developers who are familiar with Machine Learning and want to learn how to implement them in PHP
• Curious to learn about Machine Learning

If this is you, then what are you waiting for?! Let’s get Started

Who this course is for:

• PHP Developers who want to start their journey in Machine Learning
• Developers who are familiar with Machine Learning and want to learn how to implement them in PHP
• Curious to learn about Machine Learning

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Course content

Apply Machine Learning to Web sites, Mobile Apps

Requirements

• Beginners level knowledge for working with Data .
• Programming knowledge not required.

Description

The main purpose of this course is to give students the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning and big data.

By- 
Uditha Bandara is specializes in Microsoft Development technologies.  He is the South East Asia`s First XNA/DirectX MVP (Most Valuable Professional).  He had delivered sessions at various events and conferences in Hong Kong, Malaysia, Singapore, Sri Lanka and India. 

Who this course is for:

• Anyone Interested to learn Machine Learning

Course content

Gain a foundational understanding of machine learning

Implement both supervised and unsupervised machine learning models

Measure the performances of different machine learning models using the suitable metrics

Understand which machine learning model to use in which situation

Reduce data of higher dimensions to data of lower dimensions using principal component analysis

Requirements

• A windows machine, and a willingness to learn

Description

In this course, we will cover the foundations of machine learning. The course is designed to not beat around the bush, and cover exactly what is needed concisely and engagingly. Based on a university level machine learning syllabus, this course aims to effectively teach, what can sometimes be dry content, through the use of entertaining stories, professionally edited videos, and clever scriptwriting. This allows one effectively absorb the complex material, without experiencing the usual boredom that can be experienced when trying to study machine learning content.   

The course first goes into a very general explanation of machine learning. It does this by telling a story that involves an angry farmer and his missing donuts. This video sets the foundation for what is to come.

After a general understanding is obtained, the course moves into supervised classification. It is here that we are introduced to neural networks through the use of a plumbing system on a flower farm.

Thereafter, we delve into supervised regression, by exploring how we can figure out whether certain properties are value for money or not.

We then cover unsupervised classification and regression by using other farm-based examples.

This course is probably the best foundational machine learning course out there, and you will definitely benefit greatly from it.

Who this course is for:

• Beginners to machine learning. College students looking to improve their capability. Professionals looking to implement machine learning in their day to day business.

Course content

Basics of Image Processing for deep learning using tensorflow

Image Classification

Tensorflow

Machine Learning

Deep Learning

Neural Networks

Requirements

• Basics of Python 3 programming

Description

Want to dive into Deep Learning and can’t find a simple yet comprehensive course?

Don’t worry you have come to the right place.

We provide easily digestible lessons with plenty of programming question to fill your coding appetite. All topic are thoroughly explained and NO MATH BACKGROUND IS NEEDED. This class will give you a head start among your peers.

This class contains fundamentals of Image Classification with Tensorflow.

This course will teach you everything you need to get started.

Who this course is for:

• Data Scientists
• If you have some Knowledge about Python and want to explore Deep learning
• Beginner python developer curious about Data Science

Course content

How to implement regression, classification and boosting algorithms

Which algorithms work best for a given dataset

Data preprocessing

Requirements

• Basic python knowledge
• Google Colab account

Description

Machine learning is a rapidly growing field. However, a lot of courses on the internet today do not go over some of it’s most powerful algorithms. In this course, we will learn multiple machine learning algorithms, along with data preprocessing, all in under an hour. We will go over regression, classification, component analysis and boosting all in scikit-learn, one of the most popular machine learning libraries for python.

Algorithms we’ll go over (in order):

• Linear Regression
• Polynomial Regression
• Multiple Linear Regression
• Logistic Regression
• Support Vector Machines
• Decision Trees
• Random Forest
• Principle Component Analysis
• Gradient Boosting
• XGBoost

Who this course is for:

• People looking to get into AI but don’t know where to start
• People who want to build accurate models as quickly as possible

Course content

About this Course

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling).

This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.

WHAT YOU WILL LEARN

• Understand how text is handled in Python
• Apply basic natural language processing methods
• Write code that groups documents by topic
• Describe the nltk framework for manipulating text

SKILLS YOU WILL GAIN

• Natural Language Toolkit (NLTK)
• Text Mining
• Python Programming
• Natural Language Processing

Describe the innovations and novelties that the EfficientNet paper

Describe what it means to measure a convolutional neural network

Describe the various ways to scale a convolutional neural network architecture

Perform image classification using a pretrained EfficientNetB0 model architecture

Requirements

• The target learners are students with a strong foundation in machine learning and a basic understanding of deep learning. These students need to learn about the history and current state of computer vision, as well as gain practical skills for developing and training deep neural networks for image classification tasks.

Description

How do you measure how big a convolutional neural network is?

You can’t weigh it or use a ruler to measure it. And if you can’t measure it…then how can you scale it? Until 2020, the process of measuring a convolutional neural network was never well understood. That is until researchers set out to answer an important question:

Is there a principled method to scale up ConvNets, so they achieve better accuracy and efficiency?

And in the process, they accomplished two feats which changed the direction of deep learning:

1) Discovered a novel scaling method called compound scaling.

2) Created a new family of SOTA architectures called EfficientNet.

Now, back to the original question: how do we measure the size of a ConvNet?

By looking at three factors:

1) Resolution (dimensions of its inputs)

2) Width (number of feature maps)

3) Depth (number of layers in the network)

All three factors — depth, width, and resolution — impact the accuracy and efficiency of your network. Ideally, you want to maximize all these factors and accomplish the following:

• Retain the baseline model architecture, i.e. keep the operations in each layer fixed.

• Leave the memory footprint of your model constrained to some target hardware.

• Keep the number of FLOPs below some predefined threshold.

But there’s a catch…

Scaling up only one network dimension (width, depth or resolution) improves accuracy, but the accuracy rapidly diminishes. For better accuracy and efficiency, you must balance all network width, depth, and resolution dimensions during ConvNet scaling.

Who this course is for:

• To complete this course, learners should have a strong foundation in machine learning and a basic understanding of computer vision. This includes knowledge of supervised learning, neural networks, and image processing. Regarding skill level, learners should to be advanced beginners to intermediate. They have a solid understanding of the fundamental concepts and techniques of machine learning but may still be learning about more advanced topics such as computer vision. They have experience with Python, Pandas, scikit-learn and PyTorch.

Course content

Learn about the basics of Convolutional Neural Network models without any prior knowledge

Learn to use python to design a Convolutional Neural Network model without any prior knowledge

Learn from top tier Data Scientists to build Convolutional Neural Network models for production

Learn to develop your own customized Convolutional Neural Network models

Requirements

• No prior programming experience needed. You will learn directly in this class.

Description

This course is created to follow up with the AI4ALL initiatives. The course presents coding materials at a pre-college level and introduces a fundamental pipeline for a neural network model. The course is designed for the first-time learners and the audience who only want to get a taste of a machine learning project but still uncertain whether this is the career path. We will not bored you with the unnecessary component and we will directly take you through a list of topics that are fundamental for industry practitioners and researchers to design their customized neural network model.  The course focuses on Convolutional Neural Network models and introduce the important building block using Tensorflow.

This instructor team is lead by Ivy League graduate students and we have had 3+ years coaching high school students. We have seen all the ups and downs. Moreover, we want to share these roadblocks with you. This course is designed for beginner students at pre-college level who just want to have a quick taste of what AI is about and efficiently build a quick Github package to showcase some technical skills. We have other longer courses for more advanced students. However, we welcome anybody to take this course!

Who this course is for:

• Pre-college level students interested in neural network models

Course content

Learn to perform Classification and Regression modelling

Master Machine Learning and use it on the job

Use Seaborn to create beautiful statistical plots with Python.

Get set-up quickly with the Anaconda data science stack environment.

Requirements

• There is no specific prerequisite to learn machine learning. But you need to be from engineering/science/Maths/Stats background to understand the theory and the techniques used. You need to be good in mathematics. If you are not, still you can machine learning, but you will face difficulty when solving complex real world problems. Many say you need to know Linear algebra, Calculus etc. etc. but I never learnt it, yet I am able to work on machine learning.

Description

In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku).

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value.

More and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.

In This Course, We Are Going To Work On 2 Real World Projects Listed Below:

Project-1: Toxic Comment Classification

Project-2: UK_Road_Accident_Timeseries_Forecasting

The Only Course You Need To Become A Data Scientist, Get Hired And Start A New Career

Note (Read This): This Course Is Worth Of Your Time And Money, Enroll Now Free

Who this course is for:

• Beginners in machine learning
• Career transition from Non Technical into Data Science
• Fresher to get job into Machine Learning Engineer
• Beginner into Machine Learning

Course content

Convolutional Neural Networks

Image Processing

Advance Deep Learning Techniques

Regularization, Normalization

Transfer Learning

Requirements

• Machine Learning
• Linear Regression and Classification
• Matrix Calculus, Probability
• Deep Learning basis: Multi perceptron, optimization
• Python, PyTorch

Description

Dear friend, welcome to the course “Modern Deep Convolutional Neural Networks”! I tried to do my best in order to share my practical experience in Deep Learning and Computer vision with you.

The course consists of 4 blocks:

1. Introduction section, where I remind you, what is Linear layers, SGD, and how to train Deep Networks.
2. Convolution section, where we discuss convolutions, it’s parameters, advantages and disadvantages.
3. Regularization and normalization section, where I share with you useful tips and tricks in Deep Learning.
4. Fine tuning, transfer learning, modern datasets and architectures

If you don’t understand something, feel free to ask equations. I will answer you directly or will make a video explanation.

Prerequisites:

• Matrix calculus, Linear Algebra, Probability theory and Statistics
• Basics of Machine Learning: Regularization, Linear Regression and Classification,
• Basics of Deep Learning: Linear layers, SGD,  Multi-layer perceptron
• Python, Basics of PyTorch

Who this course is for:

• Who knows a bit about neural networks
• Who wants to enrich their Deep Learning and Image Processing knowledge
• Who wants to study advanced techniques and practices

Course content