Machine learning is the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms therefore learn from test data that has not been labeled, classified or categorized.
Ninety percent of all the world’s data has been created in the last two years. Business around the world generate approximately 450 billion transactions a day. The amount of data collected by all organizations is approximately 2.5 exabytes a day. That number doubles every month.That means,there is always exponential need for Machine learning engineers to build, implement, and maintain machine learning systems,algorithms in technology products with focus on machine learning system reliability, performance, and scalability.
TAKE ACTION AND START ENROLLING TODAY! All of the below listed courses are running at discount of about 90%. And for any reason you are unhappy with the course, Udemy has a 30 day Money Back Refund Policy, So no questions asked and no Risk to you. You got nothing to lose.
Do check out earlier compilation on Data Science,Machine Learning and Artificial Intelligence courses.
The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques
Created by Lazy Programmer Inc
Requirements:
- For earlier sections, just know some basic arithmetic
- For advanced sections, know calculus, linear algebra, and probability for a deeper understanding
- Be proficient in Python and the Numpy stack (see my free course)
- For the deep learning section, know the basics of using Keras
Ratings:
4.7 (251 ratings)
Computer Vision and Data Science and Machine Learning combined! In Theano and TensorFlow
In this course we are going to up the ante and look at the StreetView House Number (SVHN) dataset – which uses larger color images at various angles – so things are going to get tougher both computationally and in terms of the difficulty of the classification task. But we will show that convolutional neural networks, or CNNs, are capable of handling the challenge!
Created by Lazy Programmer Inc
Requirements:
- Install Python, Numpy, Scipy, Matplotlib, Scikit Learn, Theano, and TensorFlow
- Learn about backpropagation from Deep Learning in Python part 1
- Learn about Theano and TensorFlow implementations of Neural Networks from Deep Learning part 2
Ratings:
4.6 (1,733 ratings)
Complete guide to artificial intelligence and machine learning, prep for deep reinforcement learning
Created by Lazy Programmer Inc.
Requirements:
- Calculus
- Probability
- Markov Models
- The Numpy Stack
- Have experience with at least a few supervised machine learning methods
- Gradient descent
- Good object-oriented programming skills
Ratings:
4.5 (3,402 ratings)
Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.
Recommender systems are complex; don’t enroll in this course expecting a learn-to-code type of format. There’s no recipe to follow on how to make a recommender system; you need to understand the different algorithms and how to choose when to apply each one for a given situation. We assume you already know how to code.
However, this course is very hands-on; you’ll develop your own framework for evaluating and combining many different recommendation algorithms together, and you’ll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people.
Created by Sundog Education by Frank Kane
Requirements:
- A Windows, Mac, or Linux PC with at least 3GB of free disk space.
- Some experience with a programming or scripting language (preferably Python)
- Some computer science background, and an ability to understand new algorithms.
Ratings:
Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing
This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language.
In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python.
We’ll start off with the basics, learning how to open and work with text and PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside of text files.
Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text.
We’ll understand fundamental NLP concepts such as stemming, lemmatization, stop words, phrase matching, tokenization and more!
Created by Jose Portilla
Requirements:
- Understand general Python
- Have permissions to install python packages onto computer
- Internet connection
Ratings:
4.5 (265 ratings)
Theano / Tensorflow: Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA
In these course we’ll start with some very basic stuff – principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding).
Next, we’ll look at a special type of unsupervised neural network called the autoencoder. After describing how an autoencoder works, I’ll show you how you can link a bunch of them together to form a deep stack of autoencoders, that leads to better performance of a supervised deep neural network. Autoencoders are like a non-linear form of PCA.
Created by Lazy Programmer Inc
Requirements:
- Knowledge of calculus and linear algebra
- Python coding skills
- Some experience with Numpy, Theano, and Tensorflow
- Know how gradient descent is used to train machine learning models
- Install Python, Numpy, and Theano
- Some probability and statistics knowledge
- Code a feedforward neural network in Theano or Tensorflow
Ratings:
4.6 (943 ratings)
Learn all the new APIs including ARKit (Artificial Reality), Machine Learning and Vision Framework – App Development
In this course you are going to learn some of the new features added to iOS 11 and Xcode 9. We are going to create multiple apps that focus on each of the new features.
Created by Dee Aliyu Odumosu, Jason Taylor
Requirements:
- You should have at least 6 months of iOS development experience
Ratings:
4.7 (23 ratings)
Learning to read and write CNC programs with FANUC G Code has never been so easy
This course will teach you how to program CNC parts using G-Code, the language of CNC Machines.
Created by Marc Cronin
Requirements:
- Basic knowledge of G-Code required
Ratings:
4.6 (13 ratings)
Learn how to integrate machine learning into your apps. Hands-on live coding with CoreML, Vision, NLP, CreateML and more
This course is going to familiarize you with common machine learning tasks. We’ll focus on practical applications, using hands-on Swift code examples.
We’ll delve into advanced topics like synthetic vision and natural language processing. You’ll apply what you’ve learned by building iOS applications capable of identifying faces, barcodes, text and rectangular areas in photos in real-time.
You’ll learn how to train machine learning models on your computer. You’re going to develop several smart apps, including a flower recognizer and an Amazon review sentiment analyzer.
And there’s a lot more!
Created by Karoly Nyisztor
Requirements:
- You should have a Mac with macOS Mojave with Xcode 10 or later installed on it
- You should have a solid understanding of the Swift 3 or Swift 4 programming language
- You should definitely go ahead if you know how Xcode works
Ratings:
An overview of Machine Learning with hands-on implementation of classification models using Python’s scikit-learn
This course will give you a fundamental understanding of Machine Learning overall with a focus on building classification models. Basic ML concepts of ML are explained, including Supervised and Unsupervised Learning; Regression and Classification; and Overfitting. There are 3 lab sections which focus on building classification models using Support Vector Machines, Decision Trees and Random Forests using real data sets. The implementation will be performed using the scikit-learn library for Python.
Created by Loony Corn
Ratings:
4.5 (16 ratings)
Learn how to use R from within T-SQL and publish your results in SSRS!
In SQL Server 2016, you have to the ability to integrate R for in-database, scalable machine learning. We will explore some of the challenges faced when pushing your algorithms and results to a consumable format. In this course, we will:
- Configure R
- Write R in SQL Server Management Studio (in T-SQL)
- Create Dynamic Stored Procedures
- Display Statistics and Graphs in SSRS
This is the first course in a series of courses to be released.
Created by Andrew McLaughlin
Requirements:
- Familiarity with R, SQL, and SSRS would be helpful.
Ratings:
5.0 (1 ratings)
Master the most popular Machine Learning tools by building your own models to tackle real-world problems
This course will introduce you to tools with which you can build predictive models with Python, the core of a Data Scientist’s toolkit. Through some really interesting examples, the course will take you through a variety of challenges: predicting the value of a house in Boston, the batting average of a baseball player, their survival chances had they been on the Titanic, or any other number of other interesting problems.
Once you master the content of the course, you can level-up your knowledge of the Python Data Analytics and Machine Learning stack by exploring these recommended libraries.
This course will guide you through the tools in the Python ecosystem that Data Scientists use to get results in a matter of hours – and with practice – in a matter of minutes. The best way to learn is through examples, and this course will guide you through all the steps needed to train and test your models by tackling several classifications and regression challenges.
By the end of the course, you will be able to take the Python Machine Learning toolkit we cover and apply it to your own projects to deploy models in just a few lines of code.
Created by Packt Publishing
Requirements:
- Some knowledge of mathematics and Python is assumed.
Ratings:
4.5 (1 ratings)
Advanced Computer Vision and Convolutional Neural Networks in Tensorflow, Keras, and Python
In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label.
You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time)
We’ll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors.
Another very popular computer vision task that makes use of CNNs is called neural style transfer.
This is where you take one image called the content image, and another image called the style image, and you combine these to make an entirely new image, that is as if you hired a painter to paint the content of the first image with the style of the other. Unlike a human painter, this can be done in a matter of seconds.
Created by Lazy Programmer Inc.
Requirements:
- Know how to build, train, and use a CNN using some library (preferably in Python)
- Understand basic theoretical concepts behind convolution and neural networks
- Decent Python coding skills, preferably in data science and the Numpy Stack
Ratings:
4.7 (927 ratings)
Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow
Created by Lazy Programmer Inc.
Requirements:
- Know how to build a neural network in Theano and/or Tensorflow
- Probability
- Multivariate Calculus
- Numpy, etc.
Ratings:
The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow
This course will get you started in building your FIRST artificial neural network using deep learningtechniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.
Created by Lazy Programmer Inc.
Requirements:
- How to take partial derivatives and log-likelihoods (ex. finding the maximum likelihood estimations for a die)
- Install Numpy and Python (approx. latest version of Numpy as of Jan 2016)
- Don’t worry about installing TensorFlow, we will do that in the lectures.
- Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course
Ratings:
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TOP 25 Udemy Machine Learning courses (Level - Intermediate)
Description
In this post,we take look at TOP 25 Udemy Machine Learning Intermediate Level courses that will help boost your career and expand your knowledge.
Author
Karthik
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upnxtblog
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