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W31 - IIT Delhi - Data Analytics (31st August)
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Course Content
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Read Me
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Introduction
Course Overview
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Pre-requisites
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Setting Up the Environment
Setup for Machine Learning : Part-I
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Setup for Machine Learning : Part-II
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Setup Locally (Reading)
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Brushing-up Pre-requisites
Brushing-up Python (Part-I)
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Brushing-up Python (Part-II)
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Python for Machine Learning
NumPy
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Pandas (Part-I)
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Pandas (Part-II)
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Matplotlib
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Getting Started with Machine Learning
Overview of Machine Learning Section
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What is ML and AI | where is it used?
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Applications of Machine Learning and Artificial Intelligence (Reading)
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Defining Machine Learning
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Defining Machine learning (Reading)
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Classification of Machine Learning
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Reading : Classification of machine learning
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Linear Regression
Machine Learning Model: Hypothesis Function
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Hypothesis Function (Reading)
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Linear Regression
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Cost Function
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Cost Function (Reading)
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Derivatives
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Derivatives (Reading)
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Gradient Descent
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Gradient Descent 2
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Debugging gradient descent
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Mean Normalization
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Mean Normalization (Reading)
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Training a model
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Training a model (Reading)
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Debugging gradient descent (Reading)
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Hands on: Linear Regression for housing price prediction
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Hands on: Linear Regression with Multiple Variables
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Linear Regression Assignment
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Data Visualization
Seaborn (Part-I)
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Seaborn (Part-II)
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Plotly
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Artificial Neural Networks with TensorFlow
Matrix Multiplication
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Artificial Neural Networks
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Artificial Neural Networks (Reading)
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Non-Linear Activation
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Activation Function (Reading)
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What are deep learning framework
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Representation of Images
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Representation of images (Reading)
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Techniques used for training
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Techniques for training the data (Reading)
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Hands-on : Building Digit Recognizer with keras
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Predicting digit present in your image
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ANN Assignment
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Creating Datasets with Google Images & Building Classificaion Model
Downloading URLs
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Building Cat vs Dog classification model
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What's Next?
Deep learning and computer vision
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Form for Internship/Project Letter
Read Me
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Form
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