AI: Building Machine Learning Systems From Scratch
Engaging projects that will teach you how complex data can be exploited to gain the most insight
This Course, with the help of practical projects, highlights how TensorFlow can be used in different scenarios—this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with tensors. Simply pick a project in line with your environment and get stacks of information on how to implement TensorFlow in production.
In this course, you'll learn
- Load, interact, dissect, process, and save complex datasets
- Solve classification and regression problems using state-of-the-art techniques
- Predict the outcome of a simple time series using Linear Regression modeling
- Use a Logistic Regression scheme to predict the future result of a time series
- Classify images using deep neural network schemes
- Tag a set of images and detect features using a deep neural network, including a Convolutional Neural Network (CNN) layer
- Resolve character-recognition problems using the Recurrent Neural Network (RNN) model
This course is a practical guide to implementing TensorFlow in production. It explores various scenarios in which you can use TensorFlow and shows you how to use it in the context of real-world projects. This will not only give you the upper hand in the field, but shows the potential for innovative uses of TensorFlow in your environment. This course opens the door to second- generation machine learning and numerical computation.
StartLecture 7 Learning from Data –Unsupervised Learning (2:09)
StartLecture 8 Mechanics of k-Means (3:28)
StartLecture 9 k-Nearest Neighbor (5:29)
StartLecture 10 Project 1 – k-Means Clustering on Synthetic Datasets (3:55)
StartLecture 11 Project 2 – Nearest Neighbor on Synthetic Datasets (1:39)
Bill Gates Says These Are the Jobs He Would Drop Out of College for Today
If he were to go into computer sciences today, Gates said, the area that he thought had the most potential was artificial intelligence. Gates brought up a recent victory by Google DeepMind over the top player in the world at Go, a game some predicted a computer could never master. He called it a remarkable achievement that signaled there is more to come in advancement in artificial intelligence. And he said the research being done in the field now is "profound" and on the verge of making new breakthroughs. "The ability for artificial agents to read and understand material is going to be phenomenal," says Gates. "Anything connected with that would be an exciting lifetime career."