More on Artificial Intelligence/ AI is everywhere for All software Developers.
AI is now far easier to apply than ever before. The developers of AI has proven
that AI can deliver a lot of value to the world, without the help of a rocket
scientist to make it work. This is due to many powerful AI frameworks and libraries
that make AI methods more accessible to most data science practitioners.
In addition, AI has now diversified and matured enough to outperform conventional data science
methods in many applications. But one importat caveat is that traditional data science of using
just Visualization tools like Tablue or Spark on Hadoop etc is not the right choice now.
But a data practitioner should know how to embed the AI implementation inside an application and
thus it has become a software development job than just loading data to a tool and visualize or
generate report out of.
Also, now we must thank the increased computing resources available include GPUs.
Can you imaging any work that can be done with out cloud computing. At Expertzlab
we prvoide cloud computing AWS/AZure and Google Cloud free of cost with our AI and
Data Science course.
Data Science could happen without AI, but in many and more cases that wouldn't make
much sense especially software development and embedded analytics. It is now clear that
the world of data science has a lot of problems and limitations that AI can help address.
The overlap of AI with Data Science will only continue to grow as they both develop, so now
is the perfect time to jump into learning AI with both feet.
How Expertzlab is Different?:
At Expertzlab we covers various frameworks, focusing on the most promising ones. Also considering different AI algorithms that go beyond deep learning. Expertzlab's highly experienced faculties will give you a more holistic understanding of the field of AI, arming you with a wide variety of tools not just ones other centers cover, but with multiple tools used in industry thus it will equip you to have more choices to make your own decision about which one is the best for any given data-related problem when you working in a project/company. After all, a good data scientist must not only know how to use each and every tool in the toolbox, but which tool is right for the job at hand.
We train from scratch, you can learn if you have any degree:
Eventhough a good mathematical background is recommended for those content needed deeper understanding, but our faculties will help you to equip you with those knowledge when and where required while we buld various AI systems. Also AI is comparatively heavy on programming and coding, we will help you to understand those constructs to get them simple and manageable with various examples. A bit of experiments from your side will also make everything easier.
Learning Path:
We can start with Deep Learning frameworks, focusing on more optimization algorithms and fuzzy logic systems. That has the objective is to provide you with some frame of reference. Then a deep dive to Deep Learning. Feed forward Neural network or multi layer perceptrons, using Tensorflow and Keras. Particle Sworm Optimization (PSO), Genetic Algorithms (GAs), Simulated Anealing (SA) and projects to understand the application of these algorithms. Later Convolutional Neural Networks (CNN), Recurrent Nueral Networks (RNN). Optimizaton ensables, extreme learning machines (ELMS) and Capsule Networks (CapsNets). All this becomes more handy when you understand how this can be executed on Big Data pyspark systems. For Big Data we not only cover Hadoop and Spark but also three new gen frameworks. That are not Storm, Samza or Flink but new three framework like MuleSoft and two others. You will be also taken to the world of Transfer learning, Reinforcement learning (Robotics), Autoencoder systems, Generative Adversarial Networks (GAN) for Virtual reality, Cybersecurity with AI. IoT and AI with Edge computing etc with a live project implementation.
Why do we need to study AI:
As AI tries to understand patterns and behaviours of entities. With AI, we want to build smart systems and understand the concept of intelligence as well. The intelligent systems that we construct are very useful in understanding how an intelligent system like our brain goes about constructing other intelligent system.
