Machine Learning & Neural Networks

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Course Curriculum

Course Content

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Introduction
Course Overview
Pre-requisites
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
NumPy
Pandas (Part-I)
Pandas (Part-II)
Matplotlib
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
Cost Function (Reading)
Derivatives
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)
Plotly
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
ANN Assignment
Creating Datasets with Google Images & Building Classificaion Model
Downloading URLs
Building Cat vs Dog classification model
What's Next?
Deep learning and computer vision
Form for Internship/Project Letter
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