A Mathematical and Programming Course on Machine Learning

In this course the mathematical concepts of machine learning will be taught to learners, with python in Google Colab

4.50 (2 reviews)
Udemy
platform
English
language
Engineering
category
instructor
47
students
33.5 hours
content
Jun 2022
last update
$84.99
regular price

What you will learn

In depth knowledge of mathematics behind building ML models

How to prepare data for feeding into models

In depth analysis of support vector machines and their kernels

Concepts of Ensemble methods in machine learning

Building Recommendation system by using concepts of machine learning

Building Recommendation System

Implementation of CNN models

Implementation of Fashion MNIST

Recurrent Neural network

Quiz at the end of each Section to test the concepts you have learned

Natural Language Processing

Active Learning

Implementation of Cost Estimation functions using TensorFlow from scratch

Description

This course of "A Comprehensive Course on Machine Learning using python"  is a very comprehensive and unique course in itself. Machine Learning is a revolution now days but we cannot master machine learning without getting the mathematical insight, and this course is designed for the same. Our course starts from very basic to advance concepts of machine learning. We have divided the course into different modules which start from the introduction of python its programming basic and important programming constructs which are extensively used in ML programming.

The mathematics involved in Machine learning is normally being not discussed and being left out in , but in our course we have put lot of emphasis in mathematical formulation of algorithms used in ML. We have also designed modules of pandas, sklearn, scipy, seaborn and matplotlib for gearing the students with all important tools which are needed in dealing with data and building the model. The machine learning module focuses on the mathematical derivation on white board through video lectures because we believe that white box view of every concept is very important for becoming an efficient ML expert.

In Machine Learning the cost estimation function also called loss functions are very important to understand and in our course we have explained Cross Categorical Entropy, Sparse Categorical Cross Entropy, and other important cost functions using TensorFlow.

Concepts like gradient descent algorithm, Restricted Boltzmann Algorithm, Perceptron, Multiple Layer Perceptron, Support Vector Machine, Radial Basis Function , Naïve Bayes Classifier,  Ensemble Methods, recommendation system and many more are being implemented with examples using Google Colab.

Further I wish best of luck to learners for their sincere efforts in advance…

  • Use of various components of statistics in analyzing data

  • Graphical representation of data to get deep insight of the patterns

  • Mathematical analysis of algorithms to remove the black box view

  • Practical implementation of all important ML Algorithms

  • Building various models from scratch using advance algorithms

  • Understanding the use of ML in research

  • Quiz at the end of each section

Screenshots

A Mathematical and Programming  Course on Machine Learning - Screenshot_01A Mathematical and Programming  Course on Machine Learning - Screenshot_02A Mathematical and Programming  Course on Machine Learning - Screenshot_03A Mathematical and Programming  Course on Machine Learning - Screenshot_04

Content

A mathematical approach to Machine learning : An Overview

Detail Overview of Course
About the course from instructor's classroom
How to connect Google CoLab with .csv file
Basics of Python : Part1(Basic Constructs, IF Else)
Basics of Python : Part2(Loops in python)
Basics of Python : Part3(List)
Basics of Python : Part4(Dictionary and Tuples)
Basics of Python : Part5(Functions)
Concepts of Numpy in Google Colab
Concepts of Pandas part1
Concepts of Pandas part2
Exploratory data analysis using test case-part1
Data Cleaning using Test case part-2

Statistics using Python

Descriptive Statistics and its importance
Different types of Probability Distributions

Machine Learning using Python

Different Types of Learning
Bayesian Learning and NB classifier
Naïve Bayes Classifier
Bayesian Network
Implementation of Naïve Bayesian Classifier in Python
Practical Implementation of Naïve Bayes Classifier
Concept of different type of Regularization
Regularization Theory : Practical Implementation of Ridge and Lasso Regression
Principal Component Analysis and LDA : part1
Principal Component Analysis and LDA : part2
Implementation of Linear and Quadratic Discriminant Analysis
Building PCA from Scratch using Python
KNN Algorithm using Python
Quiz onMachine learning

Linear Regression and Logistic Regression using Python

Concept of Linear and Logistic Regression
Implementation of linear regression from scratch
An example of Ordinary Least Square
Introduction to Multiple Linear Regression
Importance of significance test in multiple linear regression
Practical Implementation of Multiple Linear regression
Maximum Likelihood estimation importance in Logistic Regression
Logistic Regression and analysis
Implementation of Logistic Regression in python
Loan Prediction algorithm using Logistic Regression
Feature Engineering on continuous data : House Prediction
Feature Engineering on Categorical Variable : House Prediction
Quiz on Linear and Logistic Regression

Decision Trees and its implementation in Python

Overview of Section 4 Decision Tree and its implementation
Introduction to Decision tree and ID3
Introduction to ID3 and its concepts
Concept of Entropy, Overfitting and Information Gain
Practical Implementation of ID3and their limitations
C4.5 Decision and its advancement over ID3 Decision tree
Building a Decision tree with data using python from scratch
Practical Implementation of CART Algorithm using Python
CHAID Algorithm and its importance in Data Analytics
Implementation of CHAID using Python

Recommendation System using Python

Introduction to Recommendation System (RecSys)
Memory based Collaborative Filtering (CF)
Matrix Decomposition based Collaborative Filtering
Item based Recommendation System
Movie Recommendation using Content Based Filtering
Collaborative Filtering based Movie Recommendation
Hybrid Collaborative filtering
Implementation of clustering in Recommendation System
Implementation of Machine Learning in Recommendation System
Quiz of Recommendation System

Support Vector Machines and its types

Introduction to Support Vector Machines
The Constrained Optimization Problem
The dual formulation concept of SVM part1
The dual formulation concept of SVM part2
Maximum margin with the introduction of noise
Non Linear SVM Classifier
Implementation of Linear and Non Linear Support Vector Classifier
Implementation of SVR(Support Vector Regressor
Multi SVM and their applications
Case Study: Implementation of Support Vector Classifier
Case Study : Implementation of Support Vector Regressor
Support Vector Machine Quiz

Neural Network and Deep Neural Network

Introduction of Tensor flow in Neural Network and Deep Networks
Data representation in the form of Tensors
Concept of Artificial Neural Network
The Perceptron
Practical implementation of Perceptron
Gradient Descent Algorithm using python
Stochastic gradient Descent Algorithm using Python
Mini Batch Gradient Descent Algorithm
Practical implementation of MLP
Back Propagation Algorithm
Batch Normalization
Building a Classifier for Fashion MNIST dataset
Implementation of Linear regression using Tensor flow on Housing data
Practical implementation of Convolution Neural Network
A brief introduction of Computer vision using openCV
Quiz on ML and Deep Learning

Un Supervised Learning and Concepts of Clustering

Introduction to the concepts of Unsupervised Learning and clustering
The Mathematics of K-Means Clustering
The math of k means part 2
Practical Implementation of K-Means Clutering
Concept of Silhouette Coefficient to measure quality of Clusters
The Mathematics of Hierarchal Agglomerative Clustering
Implementation of Hierarchal Agglomerative Clustering using PCA
Fuzzy C Means Algorithm-FAANY
Fuzzy C Means Algorithm : Practical Implementation
Mean Shift Clustering
DBSCAN- Density Based Spatial Clustering
Gaussian Mixed Model with Expectation Maximizing Clustering
GMM Clustering - Practical Implemtation
Quiz based on concept of Clustering

Ensemble methods and Random Forest

Introduction to Random Forest
Voting Classifier and Bagging Classifier
Random Forest Classifier
Random Forest Regressor and its comparison with Linear Regression model -part3
Ada Boost Implementation : Test Case 1
Gradient Boosting Regression Trees

Natural Language Processing

Introduction to Natural language Processing
Statistical Properties of words
Lexical Resources in NLP
Term Frequency and IDF for measuring distance between two documents
Implementation of Bag of Words
Type Token Ratio
Co Occurrence Matrix and Concepts of N-Grams
Text Normalization
Implementation of Text Classification

Introduction to Deep Learning

Implementation of Tensor Flow in deep learning
Building a Sequential Model using tensor flow
Linear Regression Model using Tensorflow
A Movie Classifier using Tensor flow

Bonus Implementation : Churn Prediction Algorithm

A churn prediction algorithm using Python and ML algorithms

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4229518
udemy ID
8/8/2021
course created date
1/2/2022
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