Machine Learning

  • Different Models of Raspberry Pi

  • Why Raspberry Pi

  • Peripherals of Raspberry Pi.

  • Applications of Raspberry Pi.

  • Future of Micro Computing.

  • History

  • Features

  • Setting up path

  • Working with Python

  • Basic Syntax

  • Variable and Data Types

  • Operator

  • If

  • If- else & Nested if-else

  • For

  • While

  • Nested loops

  • Break

  • Continue Pass

  • Pass

  • Defining a function

  • Calling a function

  • Types of functions

  • Function Arguments

  • Anonymous functions

  • Global and local variables

  • Defining a function

  • Calling a function

  • Types of functions

  • Function Arguments

  • Anonymous functions

  • Global and local variables

  • Accessing Strings

  • Basic Operations

  • String slices

  • Function and Methods

  • Introduction

  • Accessing tuples

  • Operations

  • Working

  • Functions and Methods

  • Importing module

  • Math module

  • Random module

  • Packages & Composition

  • Opening and closing file

  • Reading and writing files

  • Functions

  • What is image?

  • Conversation of RGB into Gray scale

  • Conversation of Gray scale into Black white

  • Importing openCV

  • Interfacing Camera with Python

  • Face Detection Techniques – Haar cascade algorithm

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning

  • Get the dataset

  • Importing the libraries

  • Importing the dataset

  • Missing data

  • Splitting dataset into Training set and Test set

  • Feature scaling

  • Linear Regression

  • Logistic Regression

  • Support Vector Machines

  • Random Forest

  • Naïve Bayes Classification

  • Ordinary Least Square Regression

  • K-means

  • Ensemble Methods

  • Apriori Algorithm

  • Principal Component Analysis

  • Singular Value Decomposition

  • Reinforcement or Semi-Supervised Machine

  • Learning

  • Independent Component Analysis

DEEP LEARNING

  • Introduction to Deep Learning

  • Deep Learning Models

  • Additional Deep Learning Models

  • Deep Learning Platforms & Libraries

  • Introduction to Tensor Flow

  • Computational Graph

  • Key highlights

  • Creating a Graph

  • Regression example

  • Gradient Descent

  • Tensor Board

  • Modularity

  • Sharing Variables

  • Artificial Neural Networks

  • Gradient Descent and Back propagation

  • Optimization and Regularization

  • Intro to Convolutional Neural Networks

  • Artificial Neural Networks

  • Gradient Descent and Back propagation

  • Optimization and Regularization

  • Intro to Convolutional Neural Networks

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