Comprehensive Guide to Artificial Intelligence(AI) for All
Comprehensive Guide to Artificial Intelligence(AI) for All
Learn ML, NLP, Deep, Transfer and Reinforcement learning with IBM Watson, Tensorflow Sim, Keras, OpenAI Gym and more
This course has over 11 hours of content with 100+ easy to consume, high quality, Visually engaging, condensed and edited videos, over 10 Quizzes to check your understanding, reference material and code for further study.
This course is designed keeping in mind, the folks who are new to coding or are not coders, with sections where zero to little coding is used. To encourage folks to pick up coding. We have sections on learning how to code with python and with sections on Pandas, Numpy and Matplotlib, you will learn how to use these popular libraries to work with different data sets.
This could be a game changer for you, in putting yourself ahead of the crowd.
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.
Technological skill is the TOP skill that will be required during this time and 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"
Choose Success , make yourself invaluable and irreplaceable.
I will see "YOU" on the inside.
What Will I Learn?
Clearly define what is AI and Deep Learning
Build Convolutional Neural Network on IBM Watson for MNIST and CIFAR 10 Datasets (No coding)
Build Supervised and Unsupervised Machine learning Models using IBM Watson (No coding)
Test Natural Language Processing (NLP) models using IBM Watson
Build VGG like nets, Stateful RNN nets, reuse ResNet50 using Keras
Test Reinforcement Learning with Keras and OpenAI Gym
Test Recurrent Neural Network (RNN) on Mathworks
Learn to code with Python the easy way
Test Feed Forward Neural Networks(Classification and Regression) on Tensor Flow simulator and Google Colab
Solve popular data sets like MNIST, CIFAR 10, with CNN using Keras
Learn a few useful and important application of popular libraries like Numpy, Pandas, Matplotlib
Migrate Deep Neural Network models from IBM Watson to run on local your Jupyter notebook
Apply Transfer Learning techniques such as Reusing, Retraining with keras