MACHINE LEARNING & NEURAL NETWORK

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Course Content

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Introduction

Course Overview

Setting Up the Environment

Setup for Machine Learning : Part-I

Setup for Machine Learning : Part-II

Setup Locally (Reading)

Brushing-up Pre-requisites

Brushing-up Python (Part-I)

Brushing-up Python (Part-II)

Python for Machine Learning

Pandas (Part-I)

Pandas (Part-II)

Getting Started with Machine Learning

Overview of Machine Learning Section

What is ML and AI | where is it used?

Applications of Machine Learning and Artificial Intelligence (Reading)

Defining Machine Learning

Defining Machine learning (Reading)

Classification of Machine Learning

Reading : Classification of machine learning

Linear Regression

Machine Learning Model: Hypothesis Function

Hypothesis Function (Reading)

Linear Regression

Cost Function (Reading)

Derivatives (Reading)

Gradient Descent

Gradient Descent 2

Debugging gradient descent

Mean Normalization

Mean Normalization (Reading)

Training a model

Training a model (Reading)

Debugging gradient descent (Reading)

Hands on: Linear Regression for housing price prediction

Hands on: Linear Regression with Multiple Variables

Linear Regression Assignment

Data Visualization

Seaborn (Part-I)

Seaborn (Part-II)

Artificial Neural Networks with TensorFlow

Matrix Multiplication

Artificial Neural Networks

Artificial Neural Networks (Reading)

Non-Linear Activation

Activation Function (Reading)

What are deep learning framework

Representation of Images

Representation of images (Reading)

Techniques used for training

Techniques for training the data (Reading)

Hands-on : Building Digit Recognizer with keras

Predicting digit present in your image

Creating Datasets with Google Images & Building Classificaion Model

Downloading URLs

Building Cat vs Dog classification model

What's Next?

Deep learning and computer vision

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