Artificial Intelligence (AI) is so pervasive today that you probably use it dozens of times a day without knowing it. In this class, you will learn about the most effective AI techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.
What You Will Learn
parametric/non-parametric algorithms, support vector machines, kernels, neural networks.
clustering, dimensionality reduction, recommender systems, deep learning.
Best practices in Artificial Intelligence
bias/variance theory; innovation process in machine learning and AI.
Watch our "5 min Quick Introduction"
- Difference Between Python 2 and Python 3
- Print function and strings
- Math function and programming basics.
- Variables and Loops introduction
- Loops detailed
- Functions and Function Parameters
- Global and Local Variables
- Packages and Modules with PIP
- Writing/Reading/Appending to a file
- Common pythonic errors
- Getting user Input
- Stats with python
- Module Import
- List and Multidimensional lists
- Reading from CSV
- Multi-Line Print
- Built-in functions
- Built-in Modules
- Regular expression
- cx freeze
- Matplotlib intro
- Introduction to pandas
- pandas basics
- concatenating and appending data frames.
- Joining and merging data frames.
- What is AI
- Difference between a rule-based algorithm and a machine learning algorithm.
- Supervised vs Unsupervised learning.
- Classification vs Regression
- Practical Machine Learning
- Training and Testing Data
- features and labels
- pickling and scaling
- Linear Regression
- Forecasting and prediction using regression
- Logistic Regression
- K-NN classification
- Support Vector Machines
- K-Means Clustering
- Random Forest
- Implementation of all the algorithms using SKlearn.
- Introduction to NLTK
- Named entity recognition
- Text classification
- Sentiment analysis