Introduction to Machine Learning and Deep Learning

PyTorch Basics: Tensors & Gradients

Linear Regression with PyTorch

Working with Image Data in PyTorch

Image Classification using Convolutional Neural Networks

Residual Networks, Data Augmentation and Regularization Techniques

Generative Adverserial Networks

Requirements

Basic Linear Algebra (matrix multiplication)

Basic Python Programming

Basic Calculus (Derivatives)

Description

“Deep Learning with PyTorch for Beginners is a series of courses covering various topics like the basics of Deep Learning, building neural networks with PyTorch, CNNs, RNNs, NLP, GANs, etc. This course is Part 1 of 5.

Topics Covered:

1. Introduction to Machine Learning & Deep Learning 2. Introduction on how to use Jovian platform 3. Introduction to PyTorch: Tensors & Gradients 4. Interoperability with Numpy 5. Linear Regression with PyTorch – System setup – Training data – Linear Regression from scratch – Loss function – Compute gradients – Adjust weights and biases using gradient descent – Train for multiple epochs – Linear Regression using PyTorch built-ins – Dataset and DataLoader – Using nn.Linear – Loss Function – Optimizer – Train the model – Commit and update the notebook 7. Sharing Jupyter notebooks online with Jovian

Who this course is for:

Beginner Python developers curious about Deep Learning and PyTorch

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

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.

Machine Learning tutorial provides basic and advanced concepts of machine learning. Our machine learning tutorial is designed for students and working professionals.

Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Currently, it is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender system, and many more.

This machine learning tutorial gives you an introduction to machine learning along with the wide range of machine learning techniques such as Supervised, Unsupervised, and Reinforcement learning. You will learn about regression and classification models, clustering methods, hidden Markov models, and various sequential models.

When you tag a face in a Facebook photo, it is AI that is running behind the scenes and identifying faces in a picture. Face tagging is now omnipresent in several applications that display pictures with human faces. Why just human faces? There are several applications that detect objects such as cats, dogs, bottles, cars, etc. We have autonomous cars running on our roads that detect objects in real time to steer the car. When you travel, you use Google Directions to learn the real-time traffic situations and follow the best path suggested by Google at that point of time. This is yet another implementation of object detection technique in real time.

Let us consider the example of Google Translate application that we typically use while visiting foreign countries. Google’s online translator app on your mobile helps you communicate with the local people speaking a language that is foreign to you.

There are several applications of AI that we use practically today. In fact, each one of us use AI in many parts of our lives, even without our knowledge. Today’s AI can perform extremely complex jobs with a great accuracy and speed. Let us discuss an example of complex task to understand what capabilities are expected in an AI application that you would be developing today for your clients.

Example

We all use Google Directions during our trip anywhere in the city for a daily commute or even for inter-city travels. Google Directions application suggests the fastest path to our destination at that time instance. When we follow this path, we have observed that Google is almost 100% right in its suggestions and we save our valuable time on the trip.

You can imagine the complexity involved in developing this kind of application considering that there are multiple paths to your destination and the application has to judge the traffic situation in every possible path to give you a travel time estimate for each such path. Besides, consider the fact that Google Directions covers the entire globe. Undoubtedly, lots of AI and Machine Learning techniques are in-use under the hoods of such applications.

Considering the continuous demand for the development of such applications, you will now appreciate why there is a sudden demand for IT professionals with AI skills.

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

How to implement an Artificial Neural Network in Python

How to do Regression

How to use Google Colab

Requirements

Deep Learning Basics

Description

Are you ready to flex your Deep Learning skills by learning how to build and implement an Artificial Neural Network using Python from scratch?

Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we’re giving you that chance for FREE.

In this free course, AI expert Hadelin de Ponteves guides you through a case study that shows you how to build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant.

The objective is to create a data model that predicts the net hourly electrical energy output (EP) of the plant using available hourly average ambient variables.

Go hands-on with Hadelin in solving this complex, real-world Deep Learning challenge that covers everything from data preprocessing to building and training an ANN, while utilizing the Machine Learning library, Tensorflow 2.0, and Google Colab, the free, browser-based notebook environment that runs completely in the cloud. It’s a game-changing interface that will supercharge your Machine Learning toolkit.

Check out what’s in store for you when you enroll:

Part 1: Data Preprocessing

Importing the dataset

Splitting the dataset into the training set and test set

Part 2: Building an ANN

Initializing the ANN

Adding the input layer and the first hidden layer

Adding the output layer

Compiling the ANN

Part 3: Training the ANN

Training the ANN model on the training set

Predicting the results of the test set

More about Combined-Cycle Power Plants

A combined-cycle power plant is an electrical power plant in which a Gas Turbine (GT) and a Steam Turbine (ST) are used in combination to produce more electrical energy from the same fuel than that would be possible from a single cycle power plant.

The gas turbine compresses air and mixes it with a fuel heated to a very high temperature. The hot air-fuel mixture moves through the blades, making them spin. The fast-spinning gas turbine drives a generator to generate electricity. The exhaust (waste) heat escaped through the exhaust stack of the gas turbine is utilized by a Heat Recovery Steam Generator (HSRG) system to produce steam that spins a steam turbine. This steam turbine drives a generator to produce additional electricity. CCCP is assumed to produce 50% more energy than a single power plant.

Who this course is for:

Anyone interested in Machine Learning and Deep Learning

Simple implementation of convolutional neural networks, deep neural networks, recurrent neural networks, and linear regression

Understanding of keras syntax

Understanding of different deep learning algorithms

Requirements

Basic python knowledge

Familiarity with data science and numpy

Description

When I started learning deep learning, I had a hard time figuring out how everything worked. What library was the best for me? Which algorithms worked best for which data set? How could I know my model was accurate? I spent a lot of time on tutorials, courses and reading to try and answer these questions. In the end, I felt like the process I took to learn deep learning was too inefficient. That is why I created this course.

Learn Keras: Build 4 Deep Learning Applications is a course that I designed to solve the problems my past self had. This course is designed to get you up and running with deep learning as quickly as possible. We use keras in this course because it is one of the easiest libraries to learn for deep learning. Each video, we go over a different machine learning algorithm and its use cases. The four algorithms we focus on the most are:

1. Linear Regression

2. Dense Neural Networks

3. Convolutional Neural Networks

4. Recurrent Neural Networks

In conclusion, if you are looking at a quick intro into deep learning, this course is for you.

So what are you waiting for? Let’s get started!

Who this course is for:

Someone who wants to get into machine learning but feels overwhelmed by other tutorials

Someone who is interested in machine learning but doesn’t know where to start

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

Applied Learning Project

Learners will implement and apply predictive, classification, clustering, and information retrieval machine learning algorithms to real datasets throughout each course in the specialization. They will walk away with applied machine learning and Python programming experience.

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you’d like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You’ll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you’ll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you’ll earn a Certificate that you can share with prospective employers and your professional network.

Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)

Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods

Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection

Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model

SKILLS YOU WILL GAIN

Decision Trees

Artificial Neural Network

Logistic Regression

Recommender Systems

Linear Regression

Regularization to Avoid Overfitting

Gradient Descent

Supervised Learning

Logistic Regression for Classification

Xgboost

Tensorflow

Tree Ensembles

About this Specialization

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

Applied Learning Project

By the end of this Specialization, you will be ready to:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.

• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.

• Build and train a neural network with TensorFlow to perform multi-class classification.

• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.

• Build and use decision trees and tree ensemble methods, including random forests and boosted trees.

• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.

• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.

Become an advanced, confident, and modern data scientist from scratch

Become job-ready by understanding how KNN really works behind the scenes

Apply robust Data Science techniques for the K-Nearest Neighbors algorithm

Solve Machine Learning Prediction Problems using KNN

How to think and work like a data scientist: problem-solving, researching, workflows

Get fast and friendly support in the Q&A area

Requirements

No data science experience is necessary to take this course.

Any computer and OS will work — Windows, macOS or Linux. We will set up your code environment in the course.

Description

You’ve just stumbled upon the most complete, in-depth KNN for Regression course online.

Whether you want to:

– build the skills you need to get your first data science job

– move to a more senior software developer position

– become a computer scientist mastering in data science

– or just learn KNN to be able to create your own projects quickly.

…this complete K-Nearest Neighbors for Regression Masterclass is the course you need to do all of this, and more.

This course is designed to give you the KNN skills you need to become a data science expert. By the end of the course, you will understand the K-Nearest Neighbors for Regression method extremely well and be able to apply them in your own data science projects and be productive as a computer scientist and developer.

What makes this course a bestseller?

Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.

Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete K-Nearest Neighbors for Regression course. It’s designed with simplicity and seamless progression in mind through its content.

This course assumes no previous data science experience and takes you from absolute beginner core concepts. You will learn the core dimensionality reduction skills and master the K-Nearest Neighbors technique. It’s a one-stop shop to learn Multilayer Networks. If you want to go beyond the core content you can do so at any time.

What if I have questions?

As if this course wasn’t complete enough, I offer full support, answering any questions you have.

This means you’ll never find yourself stuck on one lesson for days on end. With my hand-holding guidance, you’ll progress smoothly through this course without any major roadblocks.

Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

And as an extra, this course includes Python code templates which you can download and use on your own projects.

Ready to get started, developer?

Enroll now using the “Add to Cart” button on the right, and get started on your way to creative, advanced Multilayer Networks brilliance. Or, take this course for a free spin using the preview feature, so you know you’re 100% certain this course is for you.

See you on the inside (hurry, K-Nearest Neighbors is waiting!)

Who this course is for:

Any people who want to start learning K-Nearest Neighbors in Data Science

Anyone interested in Machine Learning

Anyone who want to understand how to use K-Nearest Neighbors in datasets using Python