Artificial intelligence applications enable programs to run without any manual programming. One such AI application is machine learning. The main aim of the machine learning application is to allow computer programs to learn how to access and use data by themselves, without human intervention, allowing them to adjust their actions as they learn.

Machine learning dates back to the late 1950’s, and the field is becoming increasingly broad as the years go by. Machine learning can be daunting to some people, especially those getting started. It is very diverse and there is much to learn. However, machine learning is a lot of fun, and those that grasp the courses make very fast progress.

The web has good resources on many different machine learning courses and tutorials. There are also a large number of sites where you can source freelance services to get the information you need on machine learning. YouTube, which gives free access, is one of the best sources for machine learning tutorials. You can learn all the new concepts of machine learning without waiting for new methods of operational concepts.

The list below is not exhaustive, but these are some of the best tutorials and courses relating to machine learning. These tutorials do not in any way match the broader coverage of chapters from books and research papers. Tutorials are, however, very important for anyone trying to learn specific topics, or to get different perspectives on machine learning.

How to become a data scientist

Beginners have a hard time wondering where to start when it comes to changing careers, or beginning a career as a data scientist. The tutorial will give you great ideas and have your questions answered. Some of the FAQ about starting a career as a data scientist are:

  • What is the definition of data science?

  • Why do I need data science?

  • Who is a data scientist and what are their roles in a business?

  • How are problems solved in data science?

  • What are the data science job trends?

  • What components make up data science?

To answer all the above questions watch a tutorial on how to become a data scientist here.

Statistical Machine Learning tutorial

This statistical machine learning tutorial integrates the deep learning of Python packages like Nump, matplotlib and SciPy to grasp the techniques of machine learning. The main goal is to draw conclusions of statistical inference on the data at hand. This course is best suited for students with a firm background in statistics and mathematics. This sci-kit learn aims at learning, and choosing, estimators and their objects and parameters:

  • Seeking Datasets representations

  • Pipelining data

  • Prediction of all variables from different dimensional observations

  • Finding help with mailing lists, and tending towards Q&A’s with practitioners of machine learning. 

Python for data science

Understanding Python packages is very useful in machine learning as it helps with better analysis of data, creates clear visualization and uses powerful machine learning algorithms. The tutorial introduces the course, and answers the most FAQ’s.

Below are a few of the many courses you learn in Python data science.

  • The environmental set-up

  • Python data analysis using NumPy, Pandas, Matplotlib, Pandas exercise

  • Python for data visualization using Seaborn, Matplotlib, and Pandas built in for data visualization.

  • A crash course on Python

  • Overview on Jupiter

Machine learning recipes

Machine learning recipes tutorial with Josh Gordon helps you understand the recipes of machine learning. These are:

  • Introduction to Python

  • Visualization of a decision tree

  • What makes a good dataset?

  • Writing and testing data

  • Conclusion and writing the first classifier

Machine learning specialization

This tutorial has a series of practical case studies that will introduce you to the high demand field of machine learning. The study of machine learning specialization is both exciting and enlightening. The areas you learn in this tutorial are:

  • Predictions

  • Classifications

  • Information retrieval

  • Clustering

  • How to analyze complex and large databases

  • Create systems that improve and adapt with time

  • Build applications that are intelligent enough to make predictions

Decision trees- Machine Learning made easy

Decision trees are easy to use and extensively used in machine learning. Operating and interpreting algorithms in machine learning is much easier by using decision trees. Decision trees contain the following features:

  • Continuous inputs support

  • Important variables clearly indicated

  • For classification, very little computation is needed

  • Missing values automatically handled

To make it easier, the basics of this tutorial use Talend and Apache Spark.

Machine learning with big data

If you are looking for a way to incorporate data-driven decisions into all your processes, then this course will show you how to do it. It is a good course for beginners and the best part is you do not need any prior programming experience. You will, however, need to have the ability to utilize a virtual machine and install applications.

Neural networks for Machine Learning

A Neural network for machine learning is a powerful branch of machine learning that is responsible for the introduction and ushering in of a new era of artificial intelligence. It requires deep learning in the modeling of basic algorithms to analyze input data, and apply it to speech and object recognition.

Neural networks are fed with vast amounts of training data, and the networks break this data into its basic components. The tutorial video is in segments, and explains the whole complexity of neural networks for machine learning. It is worth the watch as the course is a very big part of machine learning.

Principles of Machine Learning

This principles of machine learning course gives you intensive insights into deriving and building machine learning by using Azure machine learning, R, and Python. It is partly a data science course that gives the learner very clear explanations of machine learning theories. The theories, combined with practical scenarios and hands-on experience validating, building and the deployment of machine learning models makes it easier to understand the principles. Some of the topics in this course are:

  • Classification

  • Regression

  • Improvement of supervised models

  • Non-linear modelling

  • Clusters

  • Hands on elements

Python tricks and tutorials

Python is one of the courses and tutorials taught in machine learning. This is because Python is in very high demand. Python is extremely popular in machine learning, and there are hundreds of courses to learn. It can be confusing to choose which course best suits you. There are both free and expensive Python courses.

The Udemy course on Python and R in data science is designed to give learners in-depth knowledge of all the Python tricks you can use in machine learning.  Every tutorial enables you to develop a better understanding of this sub topic of data science.

Anyone looking for tutorials and courses on machine learning can find Udemy on freelance.com.

Machine learning algorithms using MATLAB

This course covers areas of machine learning called classifications. In this topic, the learner systematically learns about algorithms using MATLAB.  The specific toolbox used in MATLAB is called the statistic and machine learning toolbox. The tutorial is easy to learn, so even a person with no mathematics background can comprehend what algorithms are all about. Some of the sub topics include:

  • K-Nearest

  • MATLAB crash course

  • Importing and grabbing dataset

  • Naïve Bayes

  • Decision Trees

Pandas

Pandas is another Python application program mainly used for data manipulation. Pandas has inbuilt functions that are very intelligent, and make the task of manipulating and summarizing data easy. This Pandas tutorial video is best suited for Python programming beginners. The topics to learn from this video are

  • Data selection

  • Grouping

  • Aggregation

  • Plots etc

Machine learning with imbalanced data sets

Biasing data towards one class leads to poor performance of the classification algorithm. In the real world, these issues are very prominent especially in cases like fraud or cancer detection. There are several methods used to address this problem. These are one-class learning, re-sampling and cost sensitive learning.

Machine learning with imbalanced data sets tutorial helps you with different approaches on how to handle unbalanced data sets when faced with a problem like fraud detection.

Machine learning at Pinterest

Pinterest is one of the most popular social media platforms. Machine learning is transforming how things are done in social media, and especially on Pinterest. Different segments of Pinterest are driven by machine learning, as is explained in this video by Jure. The video is a revelation on how machine learning has made life easier by changing our day-to-day lives.

Machine learning used by grab taxi

Grab is not falling behind in the use of machine learning to solve their business problems. The taxi company Grab uses machine learning to tackle the availability of taxis by bidding for rides with the drivers. The fastest bidder wins the ride and is assigned the job. Watch the informative video here to see how real time is used to solve problems.

Deep learning tutorial

In this 2-hour video tutorial, the learner is taken through all the breakthroughs brought about by understanding machine learning in recent years. The tutors explain in detail how machine learning has transformed for the better because of how deep learning has allowed the computational models to learn data representation. You will learn how speech recognition, visual object recognition, and object recognition have transformed. The video also discusses challenges deep learning poses, and how to use its various applications.

Data science skills for every programmer

There are many different kinds of tools used for the exploration, modeling, and visualization of data. They are difficult tools to learn but extremely important to upgrade the style used for coding. Programmers using Python, especially beginners, are highly advised to use these tools, and this video as it should prove very useful to them. 

Besides the tutorials listed above, there are many more tutorials and courses you can learn. Some of the videos are short, while others are quite lengthy. There is a lot more on the internet you can read about courses on machine learning. Whatever you read or watch, you will find what you need to know about machine learning, and how it has transformed our way of life - especially in the world of technology.

Has this article been of any help to you? If you have a comment or any suggestions, we would like to read about them in the comments box below.

Oprettet 7 september, 2017

LucyKarinsky

Software Developer

Lucy is the Development & Programming Correspondent for Freelancer.com. She is currently based in Sydney.

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