TRAINING


Curriculum of Robotics/AI:

Students are considered to have completed our program (course) if they complete five (sequential) classes of the program. These students will receive a Robotics Certificate from CBD Robotics. Students who completed only a few classes will be issued a paper Certificate of Completion from CBD Robotics. Classes will be highly specialized and focus on practical projects, students must complete new projects that are approved upon completing the course. Each class lasts 4 months or 16 weeks. Each week, students will attend 2 sessions. Each session lasts 2 hours, This includes 45 minutes of theory and 1 hour of 15 minutes of lab practice. Students will develop practical projects under guidance of lecturers in lab time.


Python programming

Unit 1 Unit 2
Data types, control flow, functions, objects & classes Postgresql database, data modeling, sqlalchemy and model relationships in Python
Unit 3 Unit 4
Flask and the basics, building blog, authentication using flask-login, Unit test Api and web services, sending data API with POST, uploading files via API
Unit 5
Capstone project
Machine Learning in Python
Unit 1 Unit 2
Sqlite database, Pandas, Statistics, Probability, Hypothesis testing, Probability Distributions Acquiring data using json format, acquiring data from an API, scraping data from HTML websites
Unit 3 Unit 4
Linear Regression, Logistic Regression, Multivariate Regression, Time Series Regression Classification, Fitting and Overfitting, Cross Validation, Random Forest, Bayes, KNN, Clustering, SVM, LDA, PCA
Unit 5
Capstone project
Natural Language Processing
Unit 1 Unit 2
Language processing and Python, Accessing Corpora and Lexical resources Processing Raw text, Categorizing and Tagging words
Unit 3 Unit 4
Classifying text, extracting information from text Analyzing Sentence Structure, Analyzing the meaning of sentences
Unit 5
Capstone project
Deep Learning
Unit 1 Unit 2
Tensorflow and the basics, MNIST Neural network, hidden layer, convolutional layer, maxpooling layer, sub-sampling layer
Unit 3 Unit 4
Convolutional Neural Network for Image Processing Recurrent Neural Networks for language processing
Unit 5
Capstone project
Computer Vision/Robotics
Unit 1 Unit 2
Machine Learning for Machine Vision, binary classification, Expectation Maximization Gaussian Mixtures, Factor Analysis, Face Recognition
Unit 3 Unit 4
Image Processing and feature extraction, the pinhole camera, multiple cameras Models for style and identity, Temporal Models, Kalman Filter, Moving Objects
Unit 5
Capstone project