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.
- #1.Machine Learning, Data Science and Deep Learning with Python
- #2.Data Science: Supervised Machine Learning in Python
- #3.Introduction to Machine Learning for Data Science
- #5.Complete iOS Machine Learning Masterclass
- #6.The Complete Python Course for Machine Learning Engineers
- #7.Machine Learning for Apps
- #8.An Introduction to Machine Learning for Data Engineers
- #9.TensorFlow and the Google Cloud ML Engine for Deep Learning
- #10.Unleash Machine Learning: Build Artificial Neuron in Python
- #11.Machine Learning with Python from Scratch
- #12.Data Science : Master Machine Learning Without Coding
- #13.The Fun and Easy Guide to Machine Learning using Keras
- #14.Machine Learning In The Cloud With Azure Machine Learning
- #15.Machine Learning Basics – SQL Server 2017, R, Python & T-SQL
- #16.Machine Learning : A Beginner’s Basic Introduction
- #17.Machine learning with Scikit-learn
- #18.Machine Learning with Scala
- #19.Data Wrangling in Pandas for Machine Learning Engineers
- #20.How to learn machine learning and neural networks
- #21.Python Machine Learning Projects
- #22.Machine Learning for Data Science using MATLAB
- #23.VSD – Machine Intelligence in EDA/CAD
- #24.Decision Trees for Machine Learning From Scratch
- #25.Machine learning for beginners
Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks.
This comprehensive machine learning tutorial includes over 80 lectures spanning 12 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice.
Created by : Sundog Education by Frank Kane
- You’ll need a desktop computer (Windows, Mac, or Linux) capable of running Enthought Canopy 1.6.2 or newer. The course will walk you through installing the necessary free software.
- Some prior coding or scripting experience is required.
- At least high school level math skills will be required.
- This course walks through getting set up on a Microsoft Windows based desktop PC. While the code in this course will run on other operating systems, we cannot provide OS-specific support for them
- A passion to learn, and basic computer skills!
- Students should understand basic high-school level mathematics, but Statistics is not required to understand this course
- Advanced memory profiling to enhance the performance of your algorithms
- Build apps powered by the powerful Tensorflow JS library
- Develop programs that work either in the browser or with Node JS
- Write clean, easy to understand ML code, no one-name variables or confusing functions
- Pick up the basics of Linear Algebra so you can dramatically speed up your code with matrix-based operations. (Don’t worry, I’ll make the math easy!)
- Comprehend how to twist common algorithms to fit your unique use cases
- Plot the results of your analysis using a custom-build graphing library
- Data loading techniques, both in the browser and Node JS environments
- Basic understanding of terminal and command line usage
- Ability to read basic math equations
4.7 (378 ratings)
The most comprehensive course on Machine Learning for iOS development. Master building smart apps iOS Swift 4
Key topics that you’ll learn in this course:
- Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition
- Develop an intuitive sense for using Machine Learning in your iOS apps
- Create 7 projects from scratch in practical code-along tutorials
- Find pre-trained ML models and make them ready to use in your iOS apps
- Create your own custom models
- Add Image Recognition capability to your apps
- Integrate Live Video Camera Stream Object Recognition to your apps
- Add Siri Voice speaking feature to your apps
- Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit.
- Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience
- Get FREE unlimited hosting for one year
Created by : Yohann Taieb
- Basic understanding of programming
- Have access to a MAC computer or MACinCloud website
The First Course in a Series for Mastering Python for Machine Learning Engineers.
This course is an applied course on machine learning. Here’ are a few items you’ll learn:
- Python basics from A-Z
- Lab integrated. Please don’t just watch. Learning is an interactive event. Go over every lab in detail.
- Real world Interviews Questions
- Data Wrangling overview. What is it? Pay attention to the basics, it’s what you’ll be doing most of your time.
- Build a basic model build in SciKit-Learn. We call these traditional models to distinguish them from deep learning models.
- Build a basic Keras model. Keras is becoming the go to Python library for building deep learning models.
Created by: Mike West
- A basic understanding of programming
- Desire to learn Python
Start building more intelligent apps with Machine Learning. Take advantage of this new foundational framework!
Key topics that you’ll learn in this course:
- Learn about the foundation of Machine Learning and Core ML
- Learn foundational python
- Build a classification model allow your apps to make predictions
- Build a neural network for your app that can classify human writing
- Learn core ML concepts so you can build your own ML Model
- Utilize the power of Machine Learning and AI for use in iOS apps
- Learn how to pass in images to Apples pre trained model – MobileNet
Created by: Devslopes by Mark Price
- Must have a computer with OSX or macOS on it
4.6 (210 ratings)
A Prerequisite for Tensorflow on Google’s Cloud Platform for Data Engineers.
This course will show you the basics of machine learning for data engineers. The course is geared towards answering questions for the Google Certified Data Engineering exam.
This is NOT a general course or introduction to machine learning. This is a very focused course for learning the concepts you’ll need to know to pass the Google Certified Data Engineering Exam.
At this juncture, the Google Certified Data Engineer is the only real world certification for data and machine learning engineers.
Created by: Mike West
- You should be familiar with any programming language.
- A basic understanding of the concepts of machine learning will be helpful but isn’t required.
- Install scikit learn (for windows use anaconda)
- Python 2.7.X working
- ipython notebook working
3.9 (130 ratings)
Mastering Machine Learning Algorithms including Neural Networks with Numpy, Pandas, Matplotlib, Seaborn and Scikit-Learn.
This course will help you to understand the main machine learning algorithms using Python, and how to apply them in your own projects.
For starters, you will learn about the main scientific libraries in Python for data analysis such as Numpy, Pandas, Matplotlib and Seaborn. You’ll then learn about artificial neural networks and how to work with machine learning models using them.
You obtain a solid background in machine learning and be able to apply that knowledge directly in your own programs.This course helps you to understand the difficult concepts of Machine learning in a unique way. Rather than just focusing on complex maths explanaitons, simpler explanations with charts, and info displays are included.
Many examples and genuinely useful code snippets are also included to make it even easier to learn and understand.
Created by: Tim Buchalka’s Learn Programming Academy, CARLOS QUIROS
- Basic knowledge of Python
- Basic knowledge of Linear Algebra
- No previous experience in Machine learning, or any of the various libraries are needed.
- Able to use a Windows computer and install software on it
- High school math
4.1 (111 ratings)
Learn 16 Machine Learning Algorithms in a Fun and Easy along with Practical Python Labs using Keras.
This is the ONLY course on Udemy which will get you implementing some of the most common machine learning algorithms on real data in Python. Plus, you will gain exposure to neural networks (using the H2o framework) and some of the most common deep learning algorithms with the Keras package.
We designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations. Each theoretically lecture is uniquely designed using whiteboard animations which can maximize engagement in the lectures and improves knowledge retention. This ensures that you absorb more content than you would traditionally would watching other theoretical videos and or books on this subject.
This is how the course is structured:
- Regression – Linear Regression, Decision Trees, Random Forest Regression,
- Classification – Logistic Regression, K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes,
- Clustering – K-Means, Hierarchical Clustering,
- Association Rule Learning – Apriori, Eclat,
- Dimensionality Reduction – Principle Component Analysis, Linear Discriminant Analysis,
- Neural Networks – Artificial Neural Networks, Convolution Neural Networks, Recurrent Neural Networks.
Created by : Augmented Startups, Minerva Singh
- PC/ Laptop to implement the Practical Labs, running Windows or Mac.
- High school knowledge in mathematics.
- Willingness to Learn and Open Mind.
- Background in engineering, data science, computer science and statistics is recommended (but not a requirement)
- Basic Python or Programming Background recommended (but not a requirement).
4.2 (87 ratings)
Introduction to machine learning in the cloud with 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.
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.
Created by: TetraNoodle Team, Manuj Aggarwal, Ruchika Dare
- Access to a free or paid account for Azure
- Basic knowledge about cloud computing and data science
- Basic knowledge about IT infrastructure setup
- Desire to learn something new and continuous improvement
4.5 (62 ratings)
SQL Server, R, Python, TSQL, Data Analysis, Machine Learning Services, Data Science, Data Visualization, Statistics.
Machine Learning Basics with SQL Server 2017, R and Python is a course in which a student having no experience / awareness of Machine Learning / R / Python / SQL Server 2017 Machine Learning Services would be trained step by step to a level where the student is confident to independently work independently with each of them.
Course includes practical hands-on queries with explanation and analysis, and theoretical coverage of key concepts. This is a fast track course to learn practical query development on 2 programming languages i.e. R and Python with T-SQL in the scope of SQL Server, using the latest version of SQL Server – 2017. No prior experience of working with R / Python is required. Even installation of SQL Server 2017 Machine Learning Services, R, Python, and Visual Studio 2017 Data Science Applications is covered in the course.
PS: This course does not teach development of machine learning models and/or algorithms. This course teaches fundamental theory and data science using R and Python in SQL Server Machine Learning Services so that a student can pursue Machine Learning confidently and comfortably.
Created by: Siddharth Mehta
- No prior knowledge of Machine Learning / SQL Server 2017 Machine Learning Services is required
- Working knowledge of any version of SQL Server is required
- No prior knowledge of R / Python is required
This course provides you with more than 10 hours of highly valuable content. Together we address theory as well as apply this knowlege in practice because only applied knowlege is real knowledge.
Together we will apply various machine learning algorithms and deep learning neural networks in practice. All other resources you need to follow along can be acquired for free and will be shown at the beginning of the couse.
After finishing the course you have acquired a solid foundation which you could leverage in your future career.
Created by : Daniel We
- Basic knowledge in python is helpful
- Your personal interest and commitment
- An open mindset
3.3 (45 ratings)
Get up-and-running via Machine Learning with Python’s insightful projects.
This course video is a unique blend of projects that teach you what Machine Learning is all about and how you can implement machine learning concepts in practice. Six different independent projects will help you master machine learning in Python. The video will cover concepts such as classification, regression, clustering, and more, all the while working with different kinds of databases. By the end of the course, you will have learned to apply various machine learning algorithms and will have mastered Python’s packages and libraries to facilitate computation. You will be able to implement your own machine learning models after taking this course.
Created by: Packt Publishing
- This video is a combination of six independent projects, each taking a unique dataset, a different problem statement, and a different solution.
3.0 (40 ratings)
Learn to implement classification and clustering algorithms using MATLAB with practical examples, projects and datasets.
This course is for you if you want to have a real feel of the Machine Learning techniques without having to learn all the complicated maths. Additionally, this course is also for you if you have had previous hours and hours of machine learning theory but could never got a change or figure out how to implement and solve data science problems with it.
The approach in this course is very practical and we will start everything from very scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal. All the coding will be done in MATLAB which is one of the fundamental programming languages for engineer and science students and is frequently used by top data science research groups world wide.
Below is the brief outline of this course.
Segment 1: Introduction to course
Segment 2: Data preprocessing
Segment 3: Classification Algorithms in MATLAB
Segment 4: Clustering Algorithms in MATLAB
Segment 5: Dimensionality Reduction
Segment 6: Project: Malware Analysis
Created by: Nouman Azam
- MATLAB 2017a or heigher version. No prior knowledge of MATLAB is required
- In version below 2017a there might be some functions that will not work
4.5 (35 ratings)
This webinar was conducted on 31st March 2018 with Rohit, CEO Paripath Inc.
We start with Electronic design automation and what is machine learning. Then we will give overall introduction to categories of machine learning (supervised and unsupervised learning) and go about discussing that a little bit. Then we talk about the frameworks which are available today, like general purpose, big data processing and deep-learning, and which one is suitable for design automation. This is Machine Learning in general with a focus on CAD, EDA and VLSI flows.
Then we talk about Applied Theory (data sets, data analysis like data augmentation, exploratory data analysis, normalization, randomization), as to what are the terms and terminologies and what do we do with that, accuracy, how do we develop the algorithm, essentially the things that are required to develop the solution flow, lets say, you as the company wants to add a feature in your product using machine learning, what you would be doing, and what your flow will look like and this is what is shown as pre-cursor of flight theory as what you should be looking out.
And then we start with regression, which is first in supervised learning. In the regression, we will give couple of example, like first is resistance estimation, second is polynomial regression which is capacitance estimation. For resistance estimation, we have the dataset from 20nm technology. And finally, we go on to create a linear classifier using logistic regression.
Next will be dimensional reduction, meaning, you have a large dataset and how to you reduce the size of that so that you can run on a laptop or even on your cell phone. Then there is a big example of that. Everything has mathematics behind that, this wont be a part of the webinar.
Created by: Kunal Ghosh, Rohit Sharma
- Be familiar to basic VLSI chip design flow
- Be familiar with standard nomenclature of VLSI and chip design
- Basic knowledge on Python and Tesnsor Flow is nice to have, but will be anyways covered in the course
4.0 (30 ratings)
Learn to build decision trees for applied machine learning from scratch in Python.
This course covers both fundamentals of decision tree algorithms such as ID3, C4.5, CART, Regression Trees and its hands-on practical applications. We will create our own decision tree framework from scratch in Python. Meanwhile, step by step exercises guide you to understand concepts clearly.
This course appeals to ones who interested in Machine Learning, Data Science and Data Mining.
Created by: Sefik Ilkin Serengil
- Basic python