AI News, 10 Best Machine Learning & Deep Learning Courses [2019 ... artificial intelligence
10 Best Programming Languages to Learn in 2019 (for Job & Future)
The most important skill to learn in today’s world is to know how to write a computer program.
Be it the autopilot in an aircraft or digital speedometer in your bike, computers in various forms surround us.
Various new programming languages are coming up that are suited for different categories of developers (beginners, intermediate, and experts) as well as for different use cases (web application, mobile applications, game development, distributed system, etc).
Let us take a look at best Programming Languages to learn in 2019 for a job and for future prospects:
Python is fast, easy-to-use, and easy-to-deploy programming language that is being widely used to develop scalable web applications.
A lot of startups are using Python as their primary backend stack and so, this opens up a huge opportunity for full-stack Python developers.
If you are looking for a development based job at a large organization, Java is the language that you should learn.
This opens up a huge opportunity for Java developers given the fact that Google has created an excellent Java-based Android development framework –
C++ is also widely used by competitive programmers owing to the fact that it is extremely fast and stable.
STL is a pool of ready-to-use libraries for various data structures, arithmetic operations, and algorithms.
The library support and speed of the language make it a popular choice in the High-frequency trading community as well.
Go provides excellent support for multithreading and so, it is being used by a lot of companies that rely heavily on distributed systems.
Those who wish to join a reasonably well old organization as a backend developer should aim to learn PHP programming.
C# is widely used for backend programming, building games (using Unity), building Window mobile phone apps and lots of other use cases.
Top 10 Machine Learning Tools You Need to Know About
The era of Machine Learning is here and it’s making a lot of progress in the Technological field and according to a Gartner Report, Machine Learning and AI is going to create 2.3 million Jobs by 2020 and this massive growth has led to the evolution of various Machine Learning Tools that we will discuss in this article.
Machine learning is a type of Artificial Intelligence that allows software applications to learn from the data and become more accurate in predicting outcomes without human intervention.
KNIME (Konstanz Information Miner), is a free and open-source data analytics, reporting, and integration platform built for powerful analytics on a GUI based workflow.
TensorFlow provides an accessible and readable syntax which is essential for making these programming resources easier to use and being a low-level library provides more flexibility and with the new v2.0, it’s just going to be on the top for any Machine Learning or Deep Learning purpose.
The support for CUDA ensures that the code can run on the GPU, thereby decreasing the time needed to run the code and increasing the overall performance of the system.
RapidMiner is a data science platform for teams that unites data prep, machine learning, and predictive model deployment.
It has a powerful and robust graphical user interface that enables users to create, deliver, and maintain predictive analytics.
With RapidMiner, uncluttered, disorganized, and seemingly useless data becomes very valuable as it simplifies data access andlets you structure them in a way that it is easy for you and your team to comprehend.
Google’s human labeling service can put a team of people to work annotating or cleaning your labels to make sure your models are being trained on high-quality data.
It is a complete framework for building production-grade computer vision, computer audition, signal processing, and statistics applications.
- On 20. oktober 2021
Deep Learning Frameworks 2019
Which deep learning framework should you use? In this video I'll compare 10 deep learning frameworks across a wide variety of metrics. PyTorch, Tensorflow ...
What laptop/desktop should you buy to work on Machine Learning and Deep Learning problems?
For More information please visit
Machine Learning and Artificial Intelligence for every developer with ML.NET and Visual Studio 2019
Who needs fancy data scientists? See how you can infuse custom AI/ML into any .NET application with ease and how Visual Studio 2019 improves your ...
Is this the BEST BOOK on Machine Learning? Hands On Machine Learning Review
Hands On Machine Learning with Scikit Learn and Tensorflow published by O'Reilly and written by Aurelien Geron could just be the best practical book on ...
Artificial Intelligence Full Course | Artificial Intelligence Tutorial for Beginners | Edureka
Machine Learning Engineer Masters Program: This Edureka video on "Artificial ..
Best Laptop for Machine Learning
What kind of laptop should you get if you want to do machine learning? There are a lot of options out there and in this video i'll describe the components of an ...
AI in 2019
2018 has been an eventful year for AI to say the least! We've seen advances in generative models, the AlphaGo victory, several data breach scandals, and so ...
Machine Learning vs Deep Learning vs Artificial Intelligence | ML vs DL vs AI | Simplilearn
This Machine Learning vs Deep Learning vs Artificial Intelligence video will help you understand the differences between ML, DL and AI, and how they are ...
AI vs Machine Learning vs Deep Learning | Machine Learning vs Artificial Intelligence | AI vs ML
Intellipaat Artificial Intelligence Master's course: In this video you will learn about the ..
Complete Machine Learning Course | Learn Machine Learning | Machine Learning Tutorial | Edureka
Machine Learning Masters Program: Topics Wise Machine Learning Podcast ..