AI: Machine Learning with Python - Hands On!
Learn to create Machine Learning Algorithms in Python and unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics
Machine learning and predictive analytics are transforming the way that businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, and is becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data. Its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
In this course, you will learn:
- Discover the different types of machine learning and know when to use them
- Explore machine learning algorithms and implement them in Python
- Use powerful open source machine learning libraries to train predictive models
- Use pandas, NumPy, and matplotlib to manipulate data
- Evaluate and fine-tune machine learning models
This course gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science course is invaluable. It covers a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and features guidance and tips on everything from sentiment analysis to neural networks. With this course,you’ll soon be able to answer some of the most important questions facing you and your organization.
This step-by step guide will walk you through connecting the fundamental theory of machine learning with practical tips for implementation using Python, complete with visualizations and hands-on code examples.
StartLecture 4 Implementing a Perceptron Algorithm in Python (11:35)
StartLecture 5 The Iris Dataset (10:58)
StartLecture 6 Training the Perceptron (3:33)
StartLecture 7 Improving the Visualization (7:55)
StartLecture 8 Adaline in Python (15:06)
StartLecture 9 Feature Standardization (9:16)
StartLecture 10 Implementing Adaline (14:32)
StartLecture 11 Scikit-Learn Perceptron (15:32)
StartLecture 12 Logistic Regression in Scikit-Learn (7:30)
StartLecture 13 Predicting Class Probabilities (8:50)
StartLecture 14 Training a Support Vector Machine in Scikit-Learn (10:27)
StartLecture 15 The Effect of Gamma (6:26)
StartLecture 16 Decision Trees (21:02)
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."