## Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R

☑ Learn how to solve real life problem using the Machine learning techniques

☑ Machine Learning models such as Linear Regression, Logistic Regression, KNN etc.

☑ Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.

☑ Understanding of basics of statistics and concepts of Machine Learning

☑ How to do basic statistical operations and run ML models in Python

☑ Indepth knowledge of data collection and data preprocessing for Machine Learning problem

☑ How to convert business problem into a Machine learning problem

You’re looking for a complete **Machine Learning and Deep Learning course** that can help you launch a flourishing career in the field of Data Science, Machine Learning, Python, R or Deep Learning, right?

**You’ve found the right Machine Learning course!**

After completing this course **you will be able to**:

· Confidently build predictive Machine Learning and Deep Learning models using R, Python to solve business problems and create business strategy

· Answer Machine Learning, Deep Learning, R, Python related interview questions

· Participate and perform in online Data Analytics and Data Science competitions such as Kaggle competitions

Check out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn.

**How this course will help you?**

A **Verifiable Certificate of Completion** is presented to all students who undertake this Machine learning basics course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning and deep learning concepts in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning and deep learning. You will also get exposure to data science and data analysis tools like R and Python.

**Why should you choose this course?**

This course covers all the steps that one should take while solving a business problem through linear regression. It also focuses Machine Learning and Deep Learning techniques in R and Python.

Most courses only focus on teaching how to run the data analysis but we believe that what happens before and after running data analysis is even more important i.e. before running data analysis it is very important that you have the right data and do some pre-processing on it. And after running data analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. Here comes the importance of machine learning and deep learning. Knowledge on data analysis tools like R, Python play an important role in these fields of Machine Learning and Deep Learning.

**What makes us qualified to teach you?**

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course. We have an in-depth knowledge on Machine Learning and Deep Learning techniques using data science and data analysis tools R, Python.

We are also the creators of some of the most popular online courses – with over 600,000 enrollments and thousands of 5-star reviews like these ones:

*This is very good, i love the fact the all explanation given can be understood by a layman – Joshua*

*Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy*

**Our Promise**

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. We aim at providing best quality training on data science, machine learning, deep learning using R and Python through this machine learning course.

**Download Practice files, take Quizzes, and complete Assignments**

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts on data science, machine learning, deep learning using R and Python. Each section contains a practice assignment for you to practically implement your learning on data science, machine learning, deep learning using R and Python.

**Table of Contents**

**Section 1 – Python basic**

This section gets you started with Python.

This section will help you set up the python and Jupyter environment on your system and it’ll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Python basics will lay foundation for gaining further knowledge on data science, machine learning and deep learning.

**Section 2 – R basic**

This section will help you set up the R and R studio on your system and it’ll teach you how to perform some basic operations in R. Similar to Python basics, R basics will lay foundation for gaining further knowledge on data science, machine learning and deep learning.

**Section 3 – Basics of Statistics**

This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation. This part of the course is instrumental in gaining knowledge data science, machine learning and deep learning in the later part of the course.

**Section 4 – Introduction to Machine Learning**

In this section we will learn – What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

**Section 5 – Data Preprocessing**

In this section you will learn what actions you need to take step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bivariate analysis then we cover topics like** outlier treatment, missing value imputation, variable transformation and correlation.**

**Section 6 – Regression Model**

This section starts with simple linear regression and then covers multiple linear regression.

We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don’t understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.

**Section 7 – Classification Models**

This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors.

We have covered the basic theory behind each concept without getting too mathematical about it so that you

understand where the concept is coming from and how it is important. But even if you don’t understand

it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem.

**Section 8 – Decision trees**

In this section, we will start with the basic theory of decision tree then we will create and plot a simple Regression decision tree**.** Then we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python and R

**Section 9 – Ensemble technique**

In this section, we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. We will discuss Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.

**Section 10 – Support Vector Machines**

SVM’s are unique models and stand out in terms of their concept**. **In this section, we will discussion about support vector classifiers and support vector machines.

**Section 11 – ANN Theoretical Concepts**

This part will give you a solid understanding of concepts involved in Neural Networks.

In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

**Section 12 – Creating ANN model in Python and R**

In this part you will learn how to create ANN models in Python and R.

We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.

We also understand the importance of libraries such as Keras and TensorFlow in this part.

**Section 13 – CNN Theoretical Concepts**

In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.

In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.

**Section 14 – Creating CNN model in Python and R**

In this part you will learn how to create CNN models in Python and R.

We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.

**Section 15 – End-to-End Image Recognition project in Python and R**

In this section we build a complete image recognition project on colored images.

We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).

**Section 16 – Pre-processing Time Series Data**

In this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models

**Section 17 – Time Series Forecasting**

In this section, you will learn common time series models such as Auto-regression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX.

By the end of this course, your confidence in creating a Machine Learning or Deep Learning model in Python and R will soar. You’ll have a thorough understanding of how to use ML/ DL models to create predictive models and solve real world business problems.

Below is a list of popular** FAQs **of students who want to start their Machine learning journey-

**What is Machine Learning?**

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

**Why use Python for Machine Learning?**

Understanding Python is one of the valuable skills needed for a career in Machine Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

**Why use R for Machine Learning?**

Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R

1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.

2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.

3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.

4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.

5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.

**What is the difference between Data Mining, Machine Learning, and Deep Learning?**

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

## English

Language

Setting up Python and Jupyter Notebook

Course resources: Notes and Datasets (Part 1)

Installing Python and Anaconda

Opening Jupyter Notebook

Introduction 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

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

Linear 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

Course resources: Notes and Datasets

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

Course resources: Notes and Datasets

Importing the Data set into Python

Importing the Data set into R

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

Course resources: Notes and Datasets

The Data set for the Regression problem

Importing data for regression model

Missing value treatment

Dummy Variable creation

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

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

Course Resources: Neural Networks’ sections

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 AP

Complex Architectures using Functional API

Saving – Restoring Models and Using Callbacks

CNN – Basics

CNN Introduction

Stride

Padding

Filters and Feature maps

Channels

PoolingLayer

Course Resources: CNN

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

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

Course Resources: Time Series Analysis

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