In computer science, Machine learning and artificial intelligence are the fastest-growing areas. Those who are working with these technologies are in the win. In recent times, more and more industries and businesses are inheriting machine learning...
In computer science, Machine learning and artificial intelligence are the fastest-growing areas. Those who are working with these technologies are in the win. In recent times, more and more industries and businesses are inheriting machine learning and artificial intelligence. ML and AI are providing a world of endless opportunities. Almost every other business is using machine learning services in any way.
According to the recent report, there has been a 34% growth in AI/machine learning patents. Apple, Google, Microsoft, and many other tech giants are pouring money in AI and ML. International Data Corporation (IDC) forecasts that spending on AI and ML will grow from $12 billion in 2017 to $57.6 billion by 2021.(source)
Programming Languages For Machine Learning
There are thousands of programming languages. But you need not study all of them. Before learning ML it is important to know which language is best for ML. Here we will discuss the top programming languages for machine learning.
Python is the best machine learning language to learn for beginners. Syntax of Python is so easy. Apart from machine learning services, it can be used for various purposes. It is a high level, open-source, general-purpose programming language. It supports imperative, functional, object-oriented, and procedural development paradigms as it is a dynamic language.
Scala is a popular name in big data. Scala runs way faster than Python as it uses Java virtual machine at runtime.It has a library called "Aerosolve" for machine learning which is especially designed for human beings. Apache Spark includes tools like Microsoft machine learning.
These tools are designed to use with distributed computing framework.
C++ is one of the most widely accepted popular and oldest programming languages. Including Tensorflow, most of the machine learning platform supports C++. C++ is an object-oriented, general-purpose programming language. There are a lot of machine learning libraries in C++ like mlpack and Shark. Both are open-source libraries used to highlight ease of use, speed and scalability.
Golang is a widely used machine learning language developed by Google. It is a safe, open-source, statically typed, general-purpose programming language. Syntax of Go is similar to C. It has a lot of rich standard libraries like GoLearn, Gorgonia, Goml. Go is a compiled programming languages like C and C++. Go is easy to learn a language because of its syntax.
R programming language build environment for graphics and statical computing. R programming language offers a wide range of graphical and statical techniques like classical statistical tests, linear and nonlinear modelling, classification, time-series analysis, clustering, etc. R offers some packages for machine learning like Caret, MLR, and H2O.
Java is the widely used programming language in all over the world. It s also an open-source general-purpose programming language. The first implementation of Java as Java 1.0 was developed by Sun Microsystems. Later it was acquired by Oracle. Some Java libraries used for machine learning services are JDMP, MLlib(spark) and WEKA.
Julia first appeared in the market in the year 2012. It is a high performance, dynamic programming language. Julia combines features from other programming languages like speed from Java and C++ and other functionalities from R, Python, and Stata languages. Machine learning libraries that Julia have are ScikitLearn.jl, MLBase.jl, and MachineLearning.jl.
C# is a simple, easy, flexible, modern, safe, open-source, and object-oriented programming language. C# is one of the most versatile programming languages in the world. C# allows application developers to build all kind of applications including consoles, mobile apps, Web apps, Windows clients, and backend systems. C# in machine learning can be used with the help of .NET.
Haskell is a robust static typing language. Haskell offers support for embedded domain-specific languages, which is crucial for AI research. It uses common algebraic structures, such as monoids and modules for enhancing the efficiency of Machine Learning algorithms. Haskell is much popular in academics circle however many reputed organizations use Haskell in their projects.
Language is the most relevant form of APIs used by global AI and machine learning developers as of 2019, as 55.9% of surveyed AI and machine learning developers said that their organizations relied on language APIs. (source)
Learning a machine learning programming language can benefit you in various ways. Nowadays there is a huge demand for machine learning software providers and hire machine learning developers in the market.
Complete hands-on Machine Learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks. Introducing Tensorflow, Using Tensorflow, Introducing Keras, Using Keras, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Learning Deep Learning, Machine Learning with Neural Networks, Deep Learning Tutorial with PythonMachine Learning, Data Science and Deep Learning with Python
Explore the full course on Udemy (special discount included in the link): http://learnstartup.net/p/BkS5nEmZg
In less than 3 hours, you can understand the theory behind modern artificial intelligence, and apply it with several hands-on examples. This is machine learning on steroids! Find out why everyone’s so excited about it and how it really works – and what modern AI can and cannot really do.
In this course, we will cover:
• Deep Learning Pre-requistes (gradient descent, autodiff, softmax)
• The History of Artificial Neural Networks
• Deep Learning in the Tensorflow Playground
• Deep Learning Details
• Introducing Tensorflow
• Using Tensorflow
• Introducing Keras
• Using Keras to Predict Political Parties
• Convolutional Neural Networks (CNNs)
• Using CNNs for Handwriting Recognition
• Recurrent Neural Networks (RNNs)
• Using a RNN for Sentiment Analysis
• The Ethics of Deep Learning
• Learning More about Deep Learning
At the end, you will have a final challenge to create your own deep learning / machine learning system to predict whether real mammogram results are benign or malignant, using your own artificial neural network you have learned to code from scratch with Python.
Separate the reality of modern AI from the hype – by learning about deep learning, well, deeply. You will need some familiarity with Python and linear algebra to follow along, but if you have that experience, you will find that neural networks are not as complicated as they sound. And how they actually work is quite elegant!
This is hands-on tutorial with real code you can download, study, and run yourself.
Python tutorial for beginners - Learn Python for Machine Learning and Web Development. Can Python be used for machine learning? Python is widely considered as the preferred language for teaching and learning ML (Machine Learning). Can I use Python for web development? Python can be used to build server-side web applications. Why Python is suitable for machine learning? How Python is used in AI? What language is best for machine learning?Python tutorial for beginners - Learn Python for Machine Learning and Web Development
TABLE OF CONTENT
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This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.
Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial
It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.
Below topics are explained in this Machine Learning course for beginners:
Basics of Machine Learning - 01:46
Why Machine Learning - 09:18
What is Machine Learning - 13:25
Types of Machine Learning - 18:32
Supervised Learning - 18:44
Reinforcement Learning - 21:06
Supervised VS Unsupervised - 22:26
Linear Regression - 23:38
Introduction to Machine Learning - 25:08
Application of Linear Regression - 26:40
Understanding Linear Regression - 27:19
Regression Equation - 28:00
Multiple Linear Regression - 35:57
Logistic Regression - 55:45
What is Logistic Regression - 56:04
What is Linear Regression - 59:35
Comparing Linear & Logistic Regression - 01:05:28
What is K-Means Clustering - 01:26:20
How does K-Means Clustering work - 01:38:00
What is Decision Tree - 02:15:15
How does Decision Tree work - 02:25:15
Random Forest Tutorial - 02:39:56
Why Random Forest - 02:41:52
What is Random Forest - 02:43:21
How does Decision Tree work- 02:52:02
K-Nearest Neighbors Algorithm Tutorial - 03:22:02
Why KNN - 03:24:11
What is KNN - 03:24:24
How do we choose 'K' - 03:25:38
When do we use KNN - 03:27:37
Applications of Support Vector Machine - 03:48:31
Why Support Vector Machine - 03:48:55
What Support Vector Machine - 03:50:34
Advantages of Support Vector Machine - 03:54:54
What is Naive Bayes - 04:13:06
Where is Naive Bayes used - 04:17:45
Top 10 Application of Machine Learning - 04:54:48
How to become a Machine Learning Engineer - 04:59:46
Machine Learning Interview Questions - 05:09:03