Python Associate Programmer with AI/ML
Details
This course summarizes machine learning models from an implementation perspective using python language. The course starts from the basics and advances to the more complex problems; hence anyone with a basic understanding of programming can attend this course.
Duration
12 weeks
Topics covered
| Topics Covered | |
| Lecture 1 | Introduction: Artificial intelligence, Data analysis, Machine learning, Deep learning |
| Lecture 2 | Overview of Python and its libraries for data manipulation and visualization: Pandas, Numpy, Matplotlib |
| Lecture 3 | Introduction to machine learning: Supervised(classification, regression)and Unsupervised (clustering, dimension reduction) |
| Lecture 4 | Introduction of shallow machine learning models and python library for shallow learning: scikit-learn |
| Lecture 5 | Implementation of Classification model for visual object identification |
| Lecture 6 | Implementation of Regression models for time series forecasting |
| Lecture 7 | Implementation of Clustering algorithms for unlabeled data clustering |
| Lecture 8 | Implementation of dimension reduction for visualization of high-dimensional data |
| Lecture 9 | Introduction to Deep Neural networks and python libraries for deep learning: Tensorflow.Keras and PyTorch |
| Lecture 10 | Implementation of Fully connected neural network for simple classification in Tensorflow.keras and PyTorch |
| Lecture 11 | Implementation of Recurrent neural network for time series classification in Tensorflow.keras and PyTorch |
| Lecture 12 | Implementation of Convolutional neural network for image classification in Tensorflow.keras |
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