Machine Learning & Deep Learning in Python & R

Udemy

Learn the fundamental concepts and aspects of machine learning and deep learning using the Python and R programming languages.

Online

₹ 4099

particular

details

                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study

                                    Mode of Delivery
                                    Video and Text Based

Introduction

Introduction
Course Resources

Setting up Python and Jupyter Notebook

Installing Python and Anaconda
This is a milestone!
Opening Jupyter Notebook
Introducing to Jupyter
Arithmetic operators in Python: Python Basics
Strings in Python: Python Basics
Lists, Tuples and Directories: Python Basics
Working with Numpy Library of Python
Working with Pandas Library of Python
Working with Seaborn Library of Python

Setting up R Studio and R crash course

Installing R and R studio
Basics of R and R studio
Packages in R
Inputting data part 1: Inbuilt datasets of R
Inputting data part 2: Manual data entry
Inputting data part 3: Importing from CSV or Text files
Creating Barplots in R
Creating Histograms in R

Basics of Statistics

Types of Data
Types of Statistics
Describing data Graphically
Measures of Centers
Measures of Dispersion

Introduction to Machine Learning

Introduction to Machine Learning
Building a Machine Learning Model

Data Preprocessing

Gathering Business Knowledge
Data Exploration
The Dataset and the Data Dictionary
Importing Data in Python
Importing the dataset into R
Univariate analysis and EDD
EDD in Python
EDD in R
Outlier Treatment
Outlier Treatment in Python
Outlier Treatment in R
Missing Value Imputation
Missing Value Imputation in Python
Missing Value imputation in R
Seasonality in Data
Bi-variate analysis and Variable transformation
Variable transformation and deletion in Python
Variable transformation in R
Non-usable variables
Dummy variable creation: Handling qualitative data
Dummy variable creation in Python
Dummy variable creation in R
Correlation Analysis
Correlation Analysis in Python
Correlation Matrix in R
Quiz

Linear Regression

The Problem Statement
Basic Equations and Ordinary Least Squares (OLS) method
Assessing accuracy of predicted coefficients
Assessing Model Accuracy: RSE and R squared
Simple Linear Regression in Python
Simple Linear Regression in R
Multiple Linear Regression
The F - statistic
Interpreting results of Categorical variables
Multiple Linear Regression in Python
Multiple Linear Regression in R
Test-train split
Bias Variance trade-off
Test train split in Python
Test-Train Split in R
Regression models other than OLS
Subset selection techniques
Subset selection in R
Shrinkage methods: Ridge and Lasso
Ridge regression and Lasso in Python
Ridge regression and Lasso in R
Heteroscedasticity

Classification Models: Data Preparation

The Data and the Data Dictionary
Data Import in Python
Importing the dataset into R
EDD in Python
EDD in R
Outlier treatment in Python
Outlier Treatment in R
Missing Value Imputation in Python
Missing Value imputation in R
Variable transformation and Deletion in Python
Variable transformation in R
Dummy variable creation in Python
Dummy variable creation in R

The Three classification models

Three Classifiers and the problem statement
Why can't we use Linear Regression?

Logistic Regression

Logistic Regression
Training a Simple Logistic Model in Python
Training a Simple Logistic model in R
Result of Simple Logistic Regression
Logistic with multiple predictors
Training multiple predictor Logistic model in Python
Training multiple predictor Logistic model in R
Confusion Matrix
Creating Confusion Matrix in Python
Evaluating performance of model
Evaluating model performance in Python
Predicting probabilities, assigning classes and making Confusion Matrix in R

Linear Discriminant Analysis (LDA)

Linear Discriminant Analysis
LDA in Python
Linear Discriminant Analysis in R

K-Nearest Neighbors classifier

Test-Train Split
Test-Train Split in Python
Test-Train Split in R
K-Nearest Neighbors classifier
K-Nearest Neighbors in Python: Part 1
K-Nearest Neighbors in Python: Part 2
K-Nearest Neighbors in R

Comparing results from 3 models

Understanding the results of classification models
Summary of the three models

Simple Decision Trees

Basics of Decision Trees
Understanding a Regression Tree
The stopping criteria for controlling tree growth
The Data set for this part
Importing the Data set into Python
Importing the Data set into R
Missing value treatment in Python
Dummy Variable creation in Python
Dependent- Independent Data split in Python
Test-Train split in Python
Splitting Data into Test and Train Set in R
Creating Decision tree in Python
Building a Regression Tree in R
Evaluating model performance in Python
Plotting decision tree in Python
Pruning a tree
Pruning a tree in Python
Pruning a Tree in R

Simple Classification Tree

Classification tree
The Data set for Classification problem
Classification tree in Python : Preprocessing
Classification tree in Python : Training
Building a classification Tree in R
Advantages and Disadvantages of Decision Trees

Ensemble technique 1 - Bagging

Ensemble technique 1 - Bagging
Ensemble technique 1 - Bagging in Python
Bagging in R

Ensemble technique 2 - Random Forests

Ensemble technique 2 - Random Forests
Ensemble technique 2 - Random Forests in Python
Using Grid Search in Python
Random Forest in R

Ensemble technique 3 - Boosting

Boosting
Ensemble technique 3a - Boosting in Python
Gradient Boosting in R
Ensemble technique 3b - AdaBoost in Python
AdaBoosting in R
Ensemble technique 3c - XGBoost in Python
XGBoosting in R

Maximum Margin Classifier

Content flow
The Concept of a Hyperplane
Maximum Margin Classifier
Limitations of Maximum Margin Classifier

Support Vector Classifier

Support Vector classifiers
Limitations of Support Vector Classifiers

Support Vector Machines

Kernel Based Support Vector Machines

Creating Support Vector Machine Model in Python

Regression and Classification Models
The Data set for the Regression problem
Importing data for regression model
X-y Split
Test-Train Split
Standardizing the data
SVM based Regression Model in Python
The Data set for the Classification problem
Classification model - Preprocessing
Classification model - Standardizing the data
SVM Based classification model
Hyper Parameter Tuning
Polynomial Kernel with Hyperparameter Tuning
Radial Kernel with Hyperparameter Tuning

Creating Support Vector Machine Model in R

Importing Data into R
Test-Train Split
More about test-train split
Classification SVM model using Linear Kernel
Hyperparameter Tuning for Linear Kernel
Polynomial Kernel with Hyperparameter Tuning
Radial Kernel with Hyperparameter Tuning
SVM based Regression Model in R

Introduction - Deep Learning

Introduction to Neural Networks and Course flow
Perceptron
Activation Functions
Python - Creating Perceptron model

Neural Networks - Stacking cells to create network

Basic Terminologies
Gradient Descent
Back Propagation
Some Important Concepts
Hyperparameter

ANN in Python

Keras and Tensorflow
Installing Tensorflow and Keras
Dataset for classification
Normalization and Test-Train split
Different ways to create ANN using Keras
Building the Neural Network using Keras
Compiling and Training the Neural Network model
Evaluating performance and Predicting using Keras
Building Neural Network for Regression Problem
Using Functional API for complex architectures
Saving - Restoring Models and Using Callbacks
Hyperparameter Tuning

ANN in R

Installing Keras and Tensorflow
Data Normalization and Test-Train Split
Building,Compiling and Training
Evaluating and Predicting
ANN with NeuralNets Package
Building Regression Model with Functional API
Complex Architectures using Functional API
Saving - Restoring Models and Using Callbacks

CNN - Basics

CNN Introduction
Stride
Padding
Filters and Feature maps
Channels
PoolingLayer

Creating CNN model in Python

CNN model in Python - Preprocessing
CNN model in Python - structure and Compile
CNN model in Python - Training and results
Comparison - Pooling vs Without Pooling in Python

Creating CNN model in R

CNN on MNIST Fashion Dataset - Model Architecture
Data Preprocessing
Creating Model Architecture
Compiling and training
Model Performance
Comparison - Pooling vs Without Pooling in R

Project : Creating CNN model from scratch in Python

Project - Introduction
Data for the project
Project - Data Preprocessing in Python
Project - Training CNN model in Python
Project in Python - model results

Project : Creating CNN model from scratch

Project in R - Data Preprocessing
CNN Project in R - Structure and Compile
Project in R - Training
Project in R - Model Performance
Project in R - Data Augmentation
Project in R - Validation Performance

Project : Data Augmentation for avoiding overfitting

Project - Data Augmentation Preprocessing
Project - Data Augmentation Training and Results

Transfer Learning : Basics

ILSVRC
LeNET
VGG16NET
GoogLeNet
Transfer Learning
Project - Transfer Learning - VGG16

Transfer Learning in R

Project - Transfer Learning - VGG16 (Implementation)
Project - Transfer Learning - VGG16 (Performance)

Time Series Analysis and Forecasting

Introduction
Time Series Forecasting - Use cases
Forecasting model creation - Steps
Forecasting model creation - Steps 1 (Goal)
Time Series - Basic Notations

Time Series - Preprocessing in Python

Data Loading in Python
Time Series - Visualization Basics
Time Series - Visualization in Python
Time Series - Feature Engineering Basics
Time Series - Feature Engineering in Python
Time Series - Upsampling and Downsampling
Time Series - Upsampling and Downsampling in Python
Time Series - Power Transformation
Moving Average
Exponential Smoothing

Time Series - Important Concepts

White Noise
Random Walk
Decomposing Time Series in Python
Differencing
Differencing in Python

Time Series - Implementation in Python

Test Train Split in Python
Naive (Persistence) model in Python
Auto Regression Model - Basics
Auto Regression Model creation in Python
Auto Regression with Walk Forward validation in Python
Moving Average model -Basics
Moving Average model in Python

Time Series - ARIMA model

ACF and PACF
ARIMA model - Basics
ARIMA model in Python
ARIMA model with Walk Forward Validation in Python

Time Series - SARIMA model

SARIMA model
SARIMA model in Python
Stationary time Series

Bonus Lecture

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