TOP 25 Udemy Machine Learning courses (Level – Beginner)
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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.
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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
Requirements:
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
Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Sci-Kit Learn.
This course focuses on “how to build and understand“, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
Created by : Lazy Programmer Inc.
Requirements:
Python, Numpy, and Pandas experience
Probability and statistics (Gaussian distribution)
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
Requirements :
You should be familiar with any programming language.
A basic understanding of the concepts of machine learning will be helpful but isn’t required.
CNNs, RNNs and other neural networks for unsupervised and supervised deep learning.
This is a comprehensive, from-the-basics course on TensorFlow and building neural networks. It assumes no prior knowledge of Tensorflow, all you need to know is basic Python programming.
What’s covered:
Deep learning basics: What a neuron is; how neural networks connect neurons to ‘learn’ complex functions; how TF makes it easy to build neural network models
Using Deep Learning for the famous ML problems: regression, classification, clustering and autoencoding
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
Requirements :
Basic knowledge of Python
Basic knowledge of Linear Algebra
No previous experience in Machine learning, or any of the various libraries are needed.
Learn Fundamentals Of Data Science & Machine Learning With Rapidminer (No Coding). Dataset & Solutions Included.
The course will teach you those fundamental concepts of machine learning by implementing practical exercises which are based on real world examples. You will learn the theory, but get hands on practice building these machine learning algorithms
You’ll also get access to:
The datasets used in all the exercises.
The solution files of the completed exercises.
Cheat sheets to help you remember the fundamental concepts.
Created by: Ram Prasad
Requirements :
Able to use a Windows computer and install software on it
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,
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
Requirements :
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
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
Requirements :
No prior knowledge of Machine Learning / SQL Server 2017 Machine Learning Services is required
Working knowledge of any version of SQL Server is required
Learn Machine Learning Basics with a Practical Example. 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.
Created by: Bluelime Learning Solutions
Requirements :
You should be able to use a PC at beginner level
Basic knowledge of Python would help but not mandatory
Learn the most important machine learning techniques using the best machine learning library available.
The objective of this course is to provide you with a good understanding of scikit-learn (being able to identify which technique you can use for a particular problem). If you follow this course, you should be able to handle quite well a machine learning interview. Even though in that case you will need to study the math with more detail.
Created by: Francisco Juretig
Requirements :
Some Python and statistics knowledge is required: Being able to code loops, functions, classes in Python is necessary. Understanding what are random variables, what is a Gaussian distribution, and the underlying concepts behind linear regression are necessary as well.
Explore the most innovative and cutting edge machine learning techniques with Scala.
The course starts with a general introduction to the Scala programming language. From there, you’ll be introduced to several practical machine learning algorithms from the areas of exploratory data analysis. You’ll look at supervised learning machine learning models for prediction and classification tasks, and unsupervised learning techniques such as clustering and dimensionality reduction and neural networks.
By the end, you will be comfortable applying machine learning algorithms to solve real-world problems using Scala.
Created by : Packt Publishing
Requirements :
Prior knowledge of one of the JVM languages and basic knowledge in math and statistics is required.
The Second Course in a Series for Mastering Python for Machine Learning Engineers.
In this course we are going to learn Pandas using a lab integrated approach. Programming is something you have to do in order to master it. You can’t read about Python and expect to learn it.Pandas is the single most important library for data wrangling in Python.
Data wrangling is the process of programmatically transforming data into a format that makes it easier to work with.
Created by: Mike West
Requirements :
If you haven’t already please take The Complete Python Course for Machine Learning Engineers
This is the second course in a series of courses. Each course builds on one another from a knowledge perspective.
A basic understanding of machine learning would be beneficial.
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.
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
Requirements:
This video is a combination of six independent projects, each taking a unique dataset, a different problem statement, and a different solution.
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
Requirements:
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
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
Requirements :
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
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.
By joining this course you get the chance to create and optimize your own machine learning algorythms. Again this course is not designed for students who like to learn theory. Those should rather turn to a university professor.
But if you want to actually practise python machine learning and create your own models in python, then this beginner’s course is the right way to start!
Created by: Daniel We
Requirements :
Please note that the videos were reuploaded in a bigger screensize in Part 2
Install Python and Modules (e.g. via Pip)
Being familiar with basic Python syntax
This is a hands-on approach -> You are coding
Ratings :
3.4 (24 ratings)
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