Learn Basics of Machine learning, Supervised , Unsupervised, FFNN, CNN, NLP, RNN
Welcome to this exciting and eye opening course on Artificial Intelligence(Part 1) . We believe that AI will touch everybody in some level, whether you are a technical or a non technical person and also that you can excel in many roles in AI with just a functional understanding of coding.
What you’ll learn
- Fundamental concepts of Artificial Intelligence.
- Be able to identify the positive and the negative impact that AI will create.
- Clearly define what is AI and Deep Learning.
- Test Feed Forward Neural Networks(Classification and Regression) on Tensor Flow simulator and Google Colab.
- Test Natural Language Processing (NLP) models using IBM Watson.
- Build Convolutional Neural Network(CNN) on IBM Watson for MNIST and CIFAR 10 Datasets (No coding).
- Build Supervised and Unsupervised Machine learning Models using IBM Watson (No coding).
- Test Recurrent Neural Network (RNN) on Mathworks.
Course Content
- Introduction –> 29 lectures • 2hr 8min.
- IBM Watson – Supervised and Unsupervised Machine Learning Models –> 8 lectures • 40min.
- Natural Language Processing (NLP) with IBM Watson –> 4 lectures • 21min.
- Feed Forward Neural Networks (FFNN) with Tensor Flow Simulator and Google Colab –> 11 lectures • 55min.
- Convolutional Neural Networks (CNN) with IBM Watson –> 10 lectures • 1hr 20min.
- Recurrent Neural Network (RNN) with Mathworks –> 3 lectures • 21min.
Requirements
Welcome to this exciting and eye opening course on Artificial Intelligence(Part 1) . We believe that AI will touch everybody in some level, whether you are a technical or a non technical person and also that you can excel in many roles in AI with just a functional understanding of coding.
We will start from the basics , break myths, clarify your understanding as to what is this mysterious term AI, (many are surprised to know that it encompasses, Machine Learning, NLP,Computer Vision, IOT, Robotics and more). We will also understand the current state of AI and its positive and negative impact in the near future.
Then we will apply the concepts we learnt with zero to little coding Involved.
– Machine learning (Supervised and Unsupervised) with IBM Watson
– Natural Language Processing (NLP) with IBM Watson
– Feed Forward Neural Networks (FFNN) with Tensor Flow Simulator
– Convolutional Neural Networks with (CNN) with IBM Watson
– Recurrent Neural Networks (RNN) with Mathworks
AI brings tremendous opportunity like higher economic growth, productivity and prosperity but the picture is not all rosy. lets look at some data points from the renowned Mckinsey&Company.
” 250 million new jobs are likely to be created by 2030″*
” In the midpoint adoption scenario 400 million Jobs are likely to be lost by 2030″*
” In the midpoint adoption scenario 75 million will need change occupational categories by 2030″*
AI is the top priority for Companies, governments and institutions alike. AI surpasses a certain product, or vertical, or function, or a specific industry , it encompasses everything. It is all prevalent.
Based on the report there will be considerable shortages in the IT sector and companies are looking to fill these gaps by retraining, hiring, redeploying, contracting and even hiring from non traditional sources. Technological skill is the TOP skill that will be required during this time and by one research they will need 250,000 data scientists by 2030. If you develop these skills and knowledge , you can take advantage of this revolution irrespective of your role, company or Industry you belong to.
So if you are “AI ready then you are future ready”
AI is here to stay and the ones who get on board fast and adapt to it will be in a much better position to face the exciting but uncertain future.
Choose Success , make yourself invaluable and irreplaceable. I will see “YOU” on the inside.
God Speed.