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Lecture 1: Overview of Tensorflow

Lecture 2: Machine Learning Refresher

Lecture 3: Steps in Machine Learning Process

Lecture 4: Loss Functions in Machine Learning

Lecture 5: Gradient Descent

Lecture 6: Gradient Descent Variations

Lecture 7: Model Selection and Evaluation

Lecture 8: Machine Learning Visualization

Lecture 9: Deep Learning Refresher

Lecture 10: Introduction to Tensors

Lecture 11: Mathematical Foundations of Deep Learning - Contd.

Lecture 12A: Building Data Pipelines for Tensorflow - Part 1

Lecture 12B: Building Data Pipelines for Tensorflow - Part 2

Lecture 12C: Building Data Pipelines for Tensorflow - Part 3

Lecture 13: Text Processing with Tensorflow

Lecture 14: Classify Images

Lecture 15: Regression

Lecture 16: Classify Structured Data

Lecture 17: Text Classification

Lecture 18: Underfitting and Overfitting

Lecture 19: Save and Restore Models

Lecture 20: CNNs-Part 1

Lecture 21: CNNs-Part 2

Lecture 22: Transfer learning with pretrained CNNs

Lecture 23: Transfer learning with TF hub

Lecture 24: Image classification and Visualization

Lecture 25: Estimator API

Lecture 26: Logistic Regression

Lecture 27: Boosted Trees

Lecture 28: Introduction to word embeddings

Lecture 29: Recurrent Neural Networks Part 1

Lecture 30: Recurrent Neural Networks Part 2

Lecture 31: Time Series Forecasting with RNNs

Lecture 32: Text Generation with RNNs

Description:

COURSE OUTLINE: This will be an applied Machine Learning Course jointly offered by Google and IIT's. We will cover the basics of Tensorflow and Machine Learning in the initial sessions and advanced topics in the latter part. After this course, the students will be able to build ML models using Tensorflow.

Practical Machine Learning with Tensorflow

NPTEL

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#Computer Science #Machine Learning #Deep Learning #TensorFlow #Artificial Intelligence #Computer Vision #Image Classification #Transfer Learning #Data Science #Data Engineering #Data Pipelines

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