Get hands-on experience in building and deploying intelligent systems using PyTorch by using one of the most widely used deep learning frameworks in AI development.
In this practical course, you’ll gain job-ready skills in deep learning, machine learning, and neural networks, boosting your resume for roles like AI Engineer, Machine Learning Engineer, and Data Scientist.
During the course, you’ll implement logistic regression and softmax regression, train deep neural networks, and build convolutional neural networks (CNNs) for real-world image classification tasks. You’ll master core techniques such as gradient descent, backpropagation, and cross entropy loss, while improving performance with weight initialization, dropout regularization, and batch normalization. Additionally, you’ll leverage GPU acceleration, perform hyperparameter tuning, and apply transfer learning using pretrained models such as ResNet18.
Finally, you’ll complete a project, where you’ll design, train, and evaluate models using modern model optimization and data preprocessing workflows. Great to talk about in interviews!
Enroll today to accelerate your career in deep learning, AI, and machine learning.
In this module, you’ll explore logistic regression training and cross-entropy loss in PyTorch. You’ll examine why mean squared error performs poorly for classification and how maximum likelihood connects to cross-entropy loss. Additionally, you’ll explore loss behavior, optimization surfaces, and c…
In this module, you’ll explore Softmax regression for multi-class classification and examine how Softmax converts model scores into class probabilities and how argmax supports prediction selection. You’ll practice building Softmax classifiers in PyTorch and step through end-to-end classification wo…
In this module, you’ll build and train shallow neural networks using PyTorch model patterns such as nn.Module and nn.Sequential. You’ll work with hidden layers, forward-pass computations, and activation functions to see how networks form non-linear decision boundaries. You’ll also construct network…
In this module, you’ll construct deep neural networks using layered PyTorch architectures and flexible model patterns such as nn.ModuleList. You’ll configure multi-layer networks with different activation functions and layer sizes to examine how depth and structure affect training behavior. Further…
In this module, you’ll build convolutional neural networks for image classification using PyTorch CNN components. You’ll apply convolution operations, stride, padding, activation maps, and pooling layers to understand how spatial features are detected and reduced across layers. Additionally, you’ll…
In this module, you’ll complete a guided final project focused on convolutional neural network classification in PyTorch. You’ll build, configure, and train a CNN using a structured dataset workflow and apply model setup, forward-pass, and training patterns. You’ll move through project design, mode…
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IBM is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (84/100).
Instructor: Prof. Gary Gensler View the complete course: https://ocw.mit.edu/15-S12F18 This course is for students wishing to explore blockchain technology’s potential use - by entrepreneurs & incumbents - to change the world of money and finance. License: Creative Commons BY-NC-SA More information…
This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performa…
This is CS50P, CS50's Introduction to Programming with Python. Register for free at https://cs50.edx.org/python. Slides and source code at https://cs50.harvard.edu/python. Playlist at https://www.youtube.com/playlist?list=PLhQjrBD2T3817j24-GogXmWqO5Q5vYy0V. An introduction to programming using a la…
This is CS50, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming, for concentrators and non-concentrators alike, with or without prior programming experience. (Two thirds of CS50 students have never taken CS before.) This course teaches…
Instructor: Ana Bell View the complete course: https://ocw.mit.edu/courses/6-100l-introduction-to-cs-and-programming-using-python-fall-2022/ *Note: Lectures 6, 10 have been updated! This subject is aimed at students with little to no programming experience. It aims to provide students with an under…
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