Get upskilled. Join our immersive Aritficial Intelligence Program and give flight to you career in AI.
  • Overview
  • Syllabus
  • Instructor
  • Training Center
FEEN 200,000
  • Overview
  • Syllabus
  • Instructor
  • Training Center

Data Science and specifically Machine Learning roles are some of the most in-demand jobs in the world because of the rapid growth of the machine learning industry with a projected valuation of $400 billion by 2023. However, there is still a huge shortage of competent individuals to satisfy the growing demands for data scientists.

This course hopes to lend a hand in bridging the gap between the demand and the supply of Machine Learning engineers.

By taking this course, you will gain the skills necessary to become a competent Machine Learning engineer. You will understand deep learning and train a simple neural network to identify objects in images as part of the practical, hands-on experience.

What you will master:
  • Understand forward pass computation, the backward pass (backpropagation), Loss functions, and output functions of Deep Neural Networks.
  • Explore model optimization techniques such Stochastic Gradient Descent, Adam and RMSProp.
  • Understand model evaluation techniques such as MAE, MSE, Accuracy, Precision, Recall and AUC.
  • Become introduced to Computer Vision techniques and architectures such as AlexNet, GoogleNet, VGG and Resnet.
  • Use Transfer Learning and pre-trained architecture to train an image recognition system.
Pre-requisite Skills:
  • Be comfortable with math concepts such as Linear Algebra, Geometry and Calculus.
  • Be comfortable with statistical concepts such as probabilities, mean, median and variance
  • Have experience with python or be willing to learn quickly.
Unit 1
Introduction to Deep Learning
  • Introduction to Machine Learning
  • Understand classification and regression algorithms
  • Understand the Perceptron algorithm
  • Deep Learning Project Pipeline
Unit 2
Deep Learning Explored
  • The forward pass computation
  • The backward pass (backpropagation)
  • Loss functions
  • Output functions
Unit 3
Model Optimization
  • Gradient Descent
  • Variants of Gradient Descent
Unit 4
Model Evaluation
  • Mean Squared and Mean Absolute Errors
  • Confidence Interval
  • Accuracy
  • Area under Curve (AUC)
  • Confusion Matrix with Precision, Recall and F-score
Unit 5
Introduction to Computer Vision
  • Vision operations such as Convolution, Pooling and Dropout
  • Vision Project Pipeline
  • Vision Architectures (AlexNet, GoogleNet, VGG, Resnet)
  • Transfer Learning
Unit 6
Introduction to PyTorch
  • Set up GCP account with PyTorch Image
  • Use transfer learning to train an image classifier
  • Bonus: Deploy our model to a web page and test it
Chidera Mosanya

Chidera holds an MSc degree from the University of Nottingham UK where he specialized in Computer Vision and Machine Learning.

After graduating, he interned at the Computer Vision Lab in Nottingham where he developed deep learning systems for eye gaze estimation in video streams with sponsorship from Microsoft.

Since then, he has conducted research and developed products in computer vision, sequence models, recommender systems, and generative models.

His current interest is in deep learning for video analysis, Natural Language Processing, time-series forecasting, AI for social good and Big Data.

Chidera Mosanya
Training Center

We’ve set a place where you can feel at your very best to share and absorb knowledge.

Intelia Traning Centre
2A Adewole Kuku Street, Off Fola Osibo, Lekki Phase 1, Lagos