Eduonix - Machine Learning With TensorFlow The Practical Guide [90% Off]

Eduonix - Machine Learning With TensorFlow The Practical Guide 

Eduonix - Machine Learning With TensorFlow The Practical Guide
$99 $10 90% Off

  • Lectures 42
  • Length 7 hours
  • Skill Levels All Levels
  • Languages English

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Description

Machine learning is changing the way software solutions are built world wide. From retail to banking and from software to automobile Machine learning is impacting the way businesses are run. TensorFlow the powerful tool for google is perfect for creating machine learning and AI solutions. This course will teach you everything you need to know to create solutions using TensorFlow. You will learn the following
Installation and Setup
Fundamental concepts and underlying design of TensorFlow
How to implement ML algorithms in TensorFlow
Machine learning fundamentals
Start now and build your next TensorFlow solution today!!

 

Eduonix - Learn Machine Learning By Building Projects [95% Off]

Eduonix - Learn Machine Learning By Building Projects 

Eduonix - Learn Machine Learning By Building Projects
$200 $9 95% Off

  • Lectures 48
  • Length 13.5 hours
  • Skill Levels All Levels
  • Languages English

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Description

A decade ago, machine learning was simply a concept but today it has changed the way we interact with our technology. Devices are becoming smarter, faster and better, with Machine Learning at the helm.
With Machine Learning becoming the next latest trend, we though it was time that learning machine learning should also shift from big companies to the hands of anyone who wanted to expand their careers in Machine Learning and AI.
For this reason, we have designed a complete and comprehensive Projects in Machine Learning course that offers a hands-on experience with ML and how to build actual projects using the Machine Learning algorithms. This course is a follow up to our Introduction to Machine Learning course and delves further deeper into the practical applications of Machine Learning.
Using 12 different projects, the course focuses on breaking down the important concepts, algorithms, and functions of Machine Learning. The course starts at the very beginning with the building blocks of Machine Learning and then progresses onto more complicated concepts. Each project adds to the complexity of the concepts covered in the project before it.
We have tried to take a more exciting approach to Machine Learning, by not working on simply the theory of it, but instead by using the technology to actually build real-world projects that you can use. You will learn how to write the codes and then see them in action and actually learn how to think like a machine learning expert.
The 12 projects that we will cover in this course includes:
Project 1 - Breast Cancer Detection - In this project, you will use the K-nearest neighbor algorithm to help detect breast cancer malignancies by using a support vector machine.
Project 2 - Board Game Review - You will learn how to perform a linear regression analysis by predicting the average reviews on a board game in this project.
Project 3 - Credit Card Fraud Detection - In this project, you are going to do a credit card fraud detection and going to focus on anomaly detection by using probability densities.
Project 4 - Stock Market Clustering Project - In this project, you will use a K-means clustering algorithm to identify related companies by finding correlations among stock market movements over a given time span.
Project 5 - Diabetes Onset Detection - In this project, you will fine-tune a deep learning neural network by performing a grid search to detect the onset of diabetes based on patient data.
Project 6 - Markov Models and K-Nearest Neighbor Approaches to Classifying DNA Sequences - In this project, you will learn about bioinformatics by using Markov models and K-nearest neighbor (KNN) algorithms to classify E. Coli DNA sequences.
Project 7 - Getting Started with Natural Language Processing In Python - This project will cover Natural Language Processing (NLP) methodology, including tokenizing words and sentences, part of speech identification and tagging, and phrase chunking.
Project 8 - Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning - This project will use the CIFAR-10 object recognition dataset as a benchmark and will implement a recently published deep neural network that can obtain similar results to state-of-the-art networks.
Project 9 - Image Super Resolution with the SRCNN - In this tutorial, we will implement and use a Tensorflow version of the Super Resolution Convolutional Neural Network (SRCNN) to improve the image quality of degraded images.
Project 10 - Natural Language Processing: Text Classification - This project will take an advance approach to Natural Language Processing by solving a text classification task using multiple classification algorithms, including a Naive Bayes classifier, SGD classifier, and linear support vector classifier (SVC). So, what are you waiting for? Become a machine learning magician with this extensive course!
Project 11 - K-Means Clustering For Image Analysis - In this project, you will learn how to use K-Means clustering in an unsupervised learning method to analyze and classify 28 x 28 pixel images from the MNIST dataset.
Project 12 - Data Compression & Visualization Using Principle Component Analysis - This project will show you how to compress our Iris dataset into a 2D feature set and how to visualize it through a normal x-y plot using k-means clustering.

Udemy - Python - Data mining and Machine learning [Free]

Udemy - Python - Data mining and Machine learning 

Udemy - Python - Data mining and Machine learning
Free

  • Lectures 16
  • Length 2 hours
  • Skill Levels All Levels
  • Languages English
  • Published 7/2018

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Description

Interested in machine learning or do you just want to make a recommender system on your own? Then this course is all you need! You will learn how to crawl data(data mining), setup a database for storing data and then use this data to recommend items to the users within your system. 

Udemy - Clustering & Classification With Machine Learning in Python [100% Off]

Udemy - Clustering & Classification With Machine Learning in Python 

Udemy - Clustering & Classification With Machine Learning in Python
$200 Free 100% Off

  • Lectures 56
  • Length 5.5 hours
  • Skill Levels Beginner Level
  • Languages English
  • Published 7/2018

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Description

CLUSTERING & CLASSIFICATION WITH MACHINE LEARNING IN PYTHON         
 With so many Python based Data Science & Machine Learning courses around, why this course?
As the title name suggests- this course your complete guide to both supervised & unsupervised learning using Python. This means, this course covers MAIN ASPECTS  of practical data science and if you take this course, you can do away with taking other courses or buying books on Python based data science.  In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised & supervised learning in Python, you can give your company a competitive edge –and boost your career to the next level.
BOOST YOUR CAREER TO THE NEXT LEVEL
LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE

But first things first. My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University. I have several years of experience in analyzing real life data from different sources  using data science techniques and producing publications for international peer reviewed journals.
 Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic . This course will give you a robust grounding in the main aspects of machine learning- clustering & classification.
Unlike other Python instructors, I dig deep into the machine learning features of Python and gives you a one-of-a-kind grounding in Python Data Science! You will go all the way from carrying out data reading & cleaning  to machine learning to finally implementing simple deep learning based models using Python
Inside this course, you’ll discover 7 complete sections addressing every aspect of Python Machine Learning:
• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda • Getting started with Jupyter notebooks for implementing data science techniques in Python  • Data Structures and Reading in Pandas, including CSV, Excel and HTML data • How to Pre-Process and “Wrangle” your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc. • Machine Learning, Supervised Learning, Unsupervised Learning in Python • Artificial neural networks (ANN) and Deep Learning. You’ll even discover how to use artificial neural networks and deep learning structures for classification! With such a rigorous grounding in so many topics, you will be an unbeatable data scientist
 With this course, you’ll have the keys to the entire Python Machine Learning Kingdom!
 You DO NOT need any prior Python or Statistics/Machine Learning Knowledge to get Started
You’ll start by absorbing the most valuable Python Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real -life
After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python. You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning.. I will even introduce you to deep learning and neural networks using the powerful H2o framework!
Most importantly, you will learn to implement these techniques practically using Python. You will have access to all the data and scripts used in this course. Remember, I am always around to support my students!!. 
What are the requirements?
  • Should be able to operate & install software on a computer
  • Interested in learning machine learning based techniques for analyzing data
  • Interested in implementing machine learning techniques in the Python environment
  • Prior exposure to common machine learning terms such as unsupervised and supervised learning
What am I going to get from this course?
  • Will be able to harness the power of Anaconda/iPython for practical data science
  • Read in data into the Python environment from different sources
  • Carry out basic data pre-processing and wrangling in Python
  • Implement unsupervised/clustering techniques such as k-means clustering
  • Implement dimensional reduction techniques (PCA) and feature selection
  • Implement supervised learning techniques/classification such as random forests in Python
  • Neural network and deep learning based classification

 

Udemy - Advanced Machine Learning & Data Analysis Projects Bootcamp [Free]

Udemy - Advanced Machine Learning & Data Analysis Projects Bootcamp 

Udemy - Advanced Machine Learning & Data Analysis Projects Bootcamp
Free

  • Lectures 133
  • Length 20.5 hours
  • Skill Levels All Levels
  • Languages English
  • Published 7/2018

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Description

Dive into a world of data science and analysis with a wide range of examples including the CIFAR 100 image dataset, Xcode development for Apple, Swift coding, CoreML, image recognition, and structuring data with pandas.
This Mammoth Interactive course was funded by a #1 project on Kickstarter
Learn Android Studio, Java, app development, Pycharm, Python coding, Tensforflow and more with Mammoth Interactive.
Build advanced projects using machine learning including advanced the MNIST database with neuron functions. Build a text summarizer and learn object localization, object recognition and Tensorboard.
Machine learning is a machine’s ability to make decisions or predictions based on previous exposure to data and extensive training. In other words, if a machine (program, app, etc.) improves its prediction accuracy through training then it has “learned”.
Learn How Models Work
Computational graphs consist of a network of connected nodes (often called neurons). Each of these nodes typically has a weight and a bias that helps determine, given an input, which path is the most likely.
There are 4 main components to building a machine learning program: data gathering and formatting, model building, training, and testing and evaluating
Data Gathering and Formatting
You will learn to gather plenty of data for the model to learn from.
All data should be formatted pretty much the same (images same size, same color scheme, etc.) and should be labelled. Also divide data into mutually exclusive training and testing sets.
Model Building
You will learn to figure out which kind of model scheme works best and what kinds of algorithms work best for the problem you’re trying to solve.
Training, Testing and Evaluating
The model can choose paths through the neural network or computational graph based upon the inputs for a particular run, as well as the weights and biases of neurons in the network.
In supervised learning, we show the model what the correct outputs are for a given set of inputs and the model alters the weights and biases of neurons to minimize the difference between its output and the correct answer.
Enroll Now to Learn with Mammoth Interactive
What are the requirements?
  • PyCharm
What am I going to get from this course?
  • Code in 3 programming languages: Java, Python and Swift
  • Build nodes and data models for linear regression
  • Use summarizing mechanisms to handle text data
  • Test projects on mobile devices
  • Examine computational graphs
  • Analyze scalars and histograms
  • Build neuron functions
  • Load, convert, and display image and digit data
  • Describe data with statistics
  • And much more...

 

Udemy - Machine Learning In The Cloud With Azure Machine Learning [100% Off]

Udemy - Machine Learning In The Cloud With Azure Machine Learning 

Udemy - Machine Learning In The Cloud With Azure Machine Learning
$150 Free 100% Off

  • Lectures 36
  • Length 3 hours
  • Skill Levels Beginner Level
  • Languages English
  • Published 3/2018

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Description

The history of data science, machine learning, and artificial Intelligence is long, but it’s only recently that technology companies - both start-ups and tech giants across the globe have begun to get excited about it… Why? Because now it works. With the arrival of cloud computing and multi-core machines - we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data.
This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis.
You may have experienced various examples of Machine Learning in your daily life (in some cases without even realizing it). Take for example 
  • Credit scoring, which helps the banks to decide whether to grant the loans to a particular customer or not - based on their credit history, historical loan applications, customers’ data and so on
  • Or the latest technological revolution from right from science fiction movies – the self-driving cars, which use Computer vision, image processing, and machine learning algorithms to learn from actual drivers’ behavior. 
  • Or Amazon's recommendation engine which recommends products based on buying patterns of millions of consumers.
In all these examples, machine learning is used to build models from historical data, to forecast the future events with an acceptable level of reliability. This concept is known as Predictive analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling.
This progress in the field of machine learning is great news for the tech industry and humanity in general. 
But the downside is that there aren’t enough data scientists or machine learning engineers who understand these complex topics.
Well, what if there was an easy to use a web service in the cloud - which could do most of the heavy lifting for us? What if scaled dynamically based on our data volume and velocity?
The answer - is new cloud service from Microsoft called Azure Machine Learning. Azure Machine Learning is a cloud-based data science and machine learning service which is easy to use and is robust and scalable like other Azure cloud services. It provides visual and collaborative tools to create a predictive model which will be ready-to-consume on web services without worrying about the hardware or the VMs which perform the calculations.
The advantage of Azure ML is that it provides a UI-based interface and pre-defined algorithms that can be used to create a training model. And it also supports various programming and scripting languages like R and Python. 
In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset.
  • Do you know what it takes to build sophisticated machine learning models in the cloud? 
  • How to expose these models in the form of web services? 
  • Do you know how you can share your machine learning models with non-technical knowledge workers and hand them the power of data analysis? 

These are some of the fundamental problems data scientists and engineers struggle with on a daily basis.
This course teaches you how to design, deploy, configure and manage your machine learning models with Azure Machine Learning. The course will start with an introduction to the Azure ML toolset and features provided by it and then dive deeper into building some machine learning models based on some real-world problems 
If you’re serious about building scalable, flexible and powerful machine learning models in the cloud, then this course is for you. 
These data science skills are in great demand, but there’s no easy way to acquire this knowledge. Rather than rely on hit and trial method, this course will provide you with all the information you need to get started with your machine learning projects. 
Startups and technology companies pay big bucks for experience and skills in these technologies They demand data science and cloud engineers make sense of their dormant data collected on their servers  -  and in turn, you can demand top dollar for your abilities. 
You may be a data science veteran or an enthusiast - if you invest your time and bring an eagerness to learn, we guarantee you real, actionable education at a fraction of the cost you can demand as a data science engineer or a consultant. We are confident your investment will come back to you many-fold in no time. 
So, if you're ready to make a change and learn how to build some cool machine learning models in the cloud, click the "Add to Cart" button below. 
Look, if you're serious about becoming an expert data engineer and generating a greater income for you and your family, it’s time to take action.  
Imagine getting that promotion which you’ve been promised for the last two presidential terms. Imagine getting chased by recruiters looking for skilled and experienced engineers by companies that are desperately seeking help. We call those good problems to have. 

Imagine getting a massive bump in your income because of your newly-acquired, in-demand skills. 
That’s what we want for you. If that’s what you want for yourself, click the “Add to Cart” button below and get started today with our “Machine Learning In The Cloud With Azure Machine Learning”. 
Let’s do this together!

 

Udemy - Machine Learning : A Beginner's Basic Introduction [Free]

Udemy - Machine Learning : A Beginner's Basic Introduction 

Udemy - Machine Learning : A Beginner's Basic Introduction
Free

  • Lectures 16
  • Length 2 hours
  • Skill Levels All Levels
  • Languages English
  • Published 3/2018

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Description

Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we'll explore some basic machine learning concepts and load data to make predictions.
Value estimation—one of the most common types of machine learning algorithms—can automatically estimate values by looking at related information. For example, a website can determine how much a house is worth based on the property's location and characteristics.
In this course, we will  use machine learning to build a value estimation system that can deduce the value of a home.   Although the tool  we will build in this course focuses on real estate, you can use the same approach to solve any kind of value estimation.
What you'll learn include:
  • Basic concepts in machine learning
  • Supervised versus Unsupervised learning
  • Machine learning frameworks
  • Machine learning using Python and scikit-learn
  • Loading sample dataset
  • Making predictions based on dataset
  • Setting up the development environment
  • Building a simple home value estimator
The examples in this course are basic but should give you a solid understanding of the power of machine learning and how it works.

 

Udemy - Neural Networks: Deep Learning, Machine Learning, AI & NLP [100% Off]

Udemy - Neural Networks: Deep Learning, Machine Learning, AI & NLP 

Udemy - Neural Networks: Deep Learning, Machine Learning, AI & NLP
$185 Free 100% Off

  • Lectures 32
  • Length 2.5 hours
  • Skill Levels Beginner Level
  • Languages English
  • Published 2/2018

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Description

Neural networks are computing systems inspired by the biological neural networks. Such systems improve themselves by considering examples, generally without task-specific programming. Instead, they evolve their own set of relevant characteristics from the learning material that they process, but if you don’t master neural networks, you will miss the opportunity to apply and work with neural networks.
What if you could change that?
My complete Neural Network course will show you the exact techniques and strategies you need to apply the intuition behind artificial neural networks, apply convolutional neural networks, do recurrent neural networks, have the Solutions to exploding and vanishing gradient.
For less than a movie ticket, you will get over 2.5 hours of video lectures and the freedom to ask me any questions regarding the course as you go through it. :)
What Is In This Course?
Your Neural Network Skills Will Be Much Easier.
Except if you’re an expert at Neural Network, know Types Of Neural Networks, work with Elman Networks And Jordan Networks, master RNN With Python, Numpy, And Theano, know Adaline (Adaptive Linear Neuron) and use Backpropagation Networks Architecture, you are going to lose many job/career opportunities or even miss working with conventional and recurrent neural networks.
As what Howard Rheingold, a critic, writer, and teacher on modern communication media such as the Internet, says “The neural network is this kind of technology that is not an algorithm, it is a network that has weights on it, and you can adjust the weights so that it learns. You teach it through trials.”
You can try it with no financial risk.
In This Neural Network Training, You'll Learn:
  • Types Of Neural Networks.   
  • The Neural Network Architecture
  • Applications Of Neural Networks     
  • Recurrent Neural Networks
  • Hopfield Network      
  • Elman Networks And Jordan Networks        
  • Long-Term And Short-Term Memory           
  • Gated Recurring Unit
  • Application Of Recurrent Neural Networks 
  • RNN With Python, Numpy, And Theano      
  • Training Of Network With Theano And The GPU   
  • Challenges And Solutions For Recurrent Neural Networks Training           
  • Challenges Of Recurrent Neural Training
  • Solutions To Exploding And Vanishing Gradient
  • Xor Problem & Network       
  • Perceptron Network   
  • Multilayer Perceptron
  • Adaline (Adaptive Linear Neuron)   
  • Propagation And Stochastic Gradient Descent         
  • Backpropagation Networks Architecture      
  • Stochastic Gradient Descent 
  • Sgd Implementation  
  • Backpropagation Through Time (BPTT)      
  • Implementation Of A Neural Network          
  • Optical Character Recognition (OCR)          
------------------------------------------------------------------------------------------------------
Is This For You?
  • Do you want to apply the intuition behind artificial neural networks?
  • Are you wondering how to apply convolutional neural networks?
  • Do you know how to do recurrent neural networks?
Then this course will definitely help you.
This course is essential to all developers, programmers, coders, data analysts, computer scientists and anyone looking to master Neural Network.
I will show you precisely what to do to solve these situations with simple and easy techniques that anyone can apply.
------------------------------------------------------------------------------------------------------
Why To Master Neural Network?
Let Me Show You Why To Master Neural Network:
1. You will apply the intuition behind artificial neural networks.
2. You will apply convolutional neural networks.
3. You will do recurrent neural networks.
4. You will have the Solutions to exploding and vanishing gradient.      
Thank you so much for taking the time to check out my course. You can be sure you're going to absolutely love it, and I can't wait to share my knowledge and experience with you inside it! 
Why wait any longer?
Click the green "Buy Now" button, and take my course 100% risk free now!

 

Udemy - Classification-Based Machine Learning for Finance [100% Off]

Udemy - Classification-Based Machine Learning for Finance 

Udemy - Classification-Based Machine Learning for Finance
$100 Free 100% Off

  • Lectures 27
  • Length 4.5 hours
  • Skill Levels Beginner Level
  • Languages English
  • Published 8/2017

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Description

Finally, a comprehensive hands-on machine learning course with specific focus on classification based models for the investment community and passionate investors. 
In the past few years, there has been a massive adoption and growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information on and application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices and get you started in this space.
In this course, we are first going to provide some background information to machine learning. To ease you into the machine learning lingo, we start will something that most people are familiar with – Logistic Regression. The assumptions of financial time series as well as the stylized facts are introduced and explained at length due to its importance. The assumptions of linear regression are also highlighted to demonstrate the challenges and danger of blindly applying machine learning to investment without proper care and considerations to the nuances of financial time series.
After covering the basics of classification based machine learning using logistic regression, we then move on to more advanced topics covering other classification machine learning algorithms such as Linear Discriminant Analysis, Quadratic Discriminant Analysis, Stochastic Gradient Descent classifier, Nearest Neighbors, Gaussian Naive Bayes and many more. We follow the foundations that we started in the first regression based machine learning course covering cross-validation, model validation, back test, professional Quant work flow, and much more.
This course not only covers machine learning techniques, it also covers in depth the rationale of investing strategy development. 
This course is the second of the Machine Learning for Finance and Algorithmic Trading & Investing Series. The courses in the series includes:
  • Regression-Based Machine Learning for Algorithmic Trading
  • Classification-Based Machine Learning for Algorithmic Trading 
  • Ensemble Machine Learning for Algorithmic Trading 
  • Unsupervised Machine Learning: Hidden Markov for Algorithmic Trading 
  • Clustering and PCA for Investing
If you are looking for a course on applying machine learning to investing, the Machine Learning for Finance and Algorithmic Trading & Investing Series is for you. With over 30 machine learning techniques test cases, which included popular techniques such as Lasso regression, Ridge regression, SVM, XGBoost, random forest, Hidden Markov Model, common clustering techniques and many more, to get you started with applying Machine Learning to investing quickly.

 

Udemy - Meeshkan: Machine Learning the GitHub API [Free]

Udemy - Meeshkan: Machine Learning the GitHub API 

Udemy - Meeshkan: Machine Learning the GitHub API
Free

  • Lectures 15
  • Length 1.5 hours
  • Skill Levels All Levels
  • Languages English
  • Published 1/2018

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Description

In this course, Meeshkan C.E.O. Mike Solomon will teach you how to do Machine Learning on Meeshkan.
Meeshkan is an easy and inexpensive platform where people can explore ideas in AI, Machine Learning and Deep Learning.
This course starts with a simple AI question: can a machine predict if a GitHub project will be successful by analyzing only the first few commits of that project?
The first section of the course will run the Machine Learning project on Meeshkan.  You'll see how quick and easy it is to do Machine Learning on Meeshkan.
The second section of the course will delve into each step of the process in detail, covering data collection, data egress, infrastructure deployment, model design, model executing and result analysis.
By the end of the course, you will be able to adapt the course materials to design, run, and explore your own Machine Learning models using public APIs and the Meeshkan Machine Learning service.

 

Udemy - Introduction to Machine Learning for Data Science [100% Off]

Udemy - Introduction to Machine Learning for Data Science 

Udemy - Introduction to Machine Learning for Data Science
$150 Free 100% Off

  • Lectures 41
  • Length 3 hours
  • Skill Levels Beginner Level
  • Languages English
  • Published 11/2017

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Description

Course Most Recently Updated Nov/2017! 
Thank you all for the huge response to this emerging course!  We are delighted to have over 300 students in over 145 different countries.  I'm genuinely touched by the overwhelmingly positive and thoughtful reviews.  It's such a privilege to share and introduce this important topic with everyday people in a clear and understandable way. 
I'm also excited to announce that I have created real closed captions for all course material, so weather you need them due to a hearing impairment, or find it easier to follow long (great for ESL students!)... I've got you covered. 
Unlock the secrets of understanding Machine Learning for Data Science!
In this introductory course, the “Backyard Data Scientist” will guide you through wilderness of Machine Learning for Data Science.  Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the “techno sphere around us”, why it’s important now, and how it will dramatically change our world today and for days to come.
Our exotic journey will include the core concepts of:
  • The train wreck definition of computer science and one that will actually instead make sense. 
  • An explanation of data that will have you seeing data everywhere that you look!
  • One of the “greatest lies” ever sold about the future computer science.
  • A genuine explanation of Big Data, and how to avoid falling into the marketing hype.
  • What is Artificial intelligence?  Can a computer actually think?  How do computers do things like navigate like a GPS or play games anyway?
  • What is Machine Learning?  And if a computer can think – can it learn? 
  • What is Data Science, and how it relates to magical unicorns!
  • How Computer Science, Artificial Intelligence, Machine Learning, Big Data and Data Science interrelate to one another. 
We’ll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science:
  • How a perfect storm of data, computer and Machine Learning algorithms have combined together to make this important right now.
  • We’ll actually make sense of how computer technology has changed over time while covering off a journey from 1956 to 2014.  Do you have a super computer in your home?  You might be surprised to learn the truth.
  • We’ll discuss the kinds of problems Machine Learning solves, and visually explain regression, clustering and classification in a way that will intuitively make sense.
  • Most importantly we’ll show how this is changing our lives.  Not just the lives of business leaders, but most importantly…you too!
To make sense of the Machine part of Machine Learning, we’ll explore the Machine Learning process:
  • How do you solve problems with Machine Learning and what are five things you must do to be successful?
  • How to ask the right question, to be solved by Machine Learning.
  • Identifying, obtaining and preparing the right data … and dealing with dirty data!
  • How every mess is “unique” but that tidy data is like families! 
  • How to identify and apply Machine Learning algorithms, with exotic names like “Decision Trees”, “Neural Networks” “K’s Nearest Neighbors” and “Naive Bayesian Classifiers”
  • And the biggest pitfalls to avoid and how to tune your Machine Learning models to help ensure a successful result for Data Science.
Our final section of the course will prepare you to begin your future journey into Machine Learning for Data Science after the course is complete.  We’ll explore:
  • How to start applying Machine Learning without losing your mind.
  • What equipment Data Scientists use, (the answer might surprise you!)
  • The top five tools Used for data science, including some surprising ones. 
  • And for each of the top five tools – we’ll explain what they are, and how to get started using them. 
  • And we’ll close off with some cautionary tales, so you can be the most successful you can be in applying Machine Learning to Data Science problems.
So I invite you to join me, the Backyard Data Scientist on an exquisite journey into unlocking the secrets of Machine Learning for Data Science.... for you know - everyday people... like you!
Sign up right now, and we'll see you – on the other side!

 

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