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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