AI Training
# build your AI skills: beginner to expert;
aiplus.build(_name)
aiplus.train(_name)
aiplus.test(_name)
LIVE TRAINING: OCTOBER 6TH
This hands-on course is organized into four lessons. In lesson one, we will provide an introduction to NLP, reviewing its evolution over the past 70 years. In lesson two, we will introduce two more popular open-source NLP libraries (fast.ai and Hugging Face) and perform state-of-the-art NLP. In lesson three, we will retrace how NLP advanced over the last decade and experienced its breakout moment in 2018. In lesson four, we will put this theory to practice and develop our own named entity recognition and text classification models using spaCy.
LIVE TRAINING: OCTOBER 12TH
In this live training you will understand object model of Python and get familiar with basic data structures. You will also build functions and classes to aid in data science work in this 4-hour hands-on live session.
LIVE TRAINING: OCTOBER 21ST
By the end of this live, hands-on, online course, you’ll understand: What MLOps is, how to get started using MLOps and best practices for MLOps. You’ll be able to: Perform Continuous Integration for Python ML Projects, use the AWS Cloud for MLOps development, create Containerized workflows for MLOps, create Flask and CLI Services for Python ML Projects.
LIVE TRAINING: OCTOBER 28TH
You’ll understand in detail how Gradient Boosting models are fit as an ensemble of decision trees and apply that understanding to the feature engineering process, the various parameters of Gradient Boosting and their relative importance and how to appropriately choose them and gain familiarity with the various Gradient Boosting packages and the capabilities, strengths, and weaknesses of each. You will also learn how to interpret, understand, and evaluate a model: both qualitatively and quantitatively.
LIVE TRAINING: DECEMBER 14TH
After this 4-hour session,you will have a foundation to carry out PRACTICAL, real-life social media mining. By taking this course, you are taking an important step forward in your data science journey to become an expert in harnessing the power of social media for deriving insights and identifying trends. This course will help you gain fluency both in the different aspects of text analysis and NLP working through a real-life example of cryptocurrency tweets, Wall Street Bet Reddit posts, restaurant reviews and financial news using a powerful clouded based python environment called GoogleColab.
Certificates & Bootcamps
Live training student experience
Time Series Forecasting with Python
All clear and well explained by our very knowledgable instructor, Marta. This was all extremely applicable to my job. Wish this was a full day instead as Marta had a lot of amazing content. Thanks to the instructor and organizers.
Hands-On Intro to Unsupervised Learning
I had a bit of previous experience with some of the techniques and was looking for more in-depth. Got all I wanted. Ankur has a great presentation style and excellent examples. I particularly appreciated Ankur’s answers to my numerous questions. Those answers alone were worth the price because they provided insight into real-world applications of these models. I also liked the use of live demo in the TensorFlow projector to visualize the results of every algorithm. Will adopt it right away.
NLP Fundamentals in Python
Matt took us through difficult terrain with ease, making sure everybody was on the same page throughout. Really great teaching! Incredibly useful!
Advanced Fraud Modeling
Aric is a great teacher and the entire class was structured very well. I learned a lot of new interesting techniques. A mix of lecture and live coding with real-time incorporation of student feedback and questions into the class. Best for intermediate to advanced data folks because it assumes you know some Python. I would highly recommend if you have an interest to learn fraud.
Practical Advanced Pandas
I like the hands-on approach to teaching the additional techniques of Pandas, and I greatly appreciate the availability of the course’s code for me to review later.
