Learn how to create AI with machine learning using Python.
Course Overview View Sample Session Plan Start Sample Lesson 1 Start Sample Lesson 2 Course Demo VideoMachine-Learning/AI with Python is the second course of a two weeks series that introduces high school and adult students to machine-learning programming with Python. This course assumes students have a solid grasp of intermediate-to-advanced Python, as dataset analysis and machine-learning projects are introduced in the first two days. As all machine-learning models must be trained, trimmed, and corrected using clean and complete datasets, so this course begins with an introduction to data science, data classification, data analytics, and dataset compilation. A series of machine-learning/AI algorithms and techniques such as Random Forest, SVM, SVP, Naïve Bayes, nearest neighbor variants, and TensorFlow will be presented through discussions and projects. Moreover, a description and related projects will be assigned using a series of related open-source modules and libraries, such as Scikit-learn, NumPy, Matplotlib, Pandas, Pygame, Keras, NLTK, BeautifulSoup, and VADER. This course culminates in a capstone project to build and customize a neural-network machine-learning powered arcade space shooter.
In the exploring data session, you will:
In the Random Forest model session, you will:
In the Support Vector Machines session, you will:
In the Natual Language Processing session, you will:
In the Advanced NLP session, you will:
In the Neuroblast Game AI session, you will: