Machine Learning & Deep Learning in Python & R

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

4.45 (4867 reviews)
Udemy
platform
English
language
Data Science
category
353,481
students
33.5 hours
content
Aug 2022
last update
$29.99
regular price

What you will learn

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

In-depth knowledge of data collection and data preprocessing for Machine Learning problem

How to convert business problem into a Machine learning problem

Description

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.

Screenshots

Machine Learning & Deep Learning in Python & R - Screenshot_01Machine Learning & Deep Learning in Python & R - Screenshot_02Machine Learning & Deep Learning in Python & R - Screenshot_03Machine Learning & Deep Learning in Python & R - Screenshot_04

Content

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

Reviews

Juan
July 6, 2022
Excelente curso, explican bien la teoría y se hace muy amigable la explicación del código, lo recomiendo!!
Pak
April 30, 2022
The course is organized in a very understandable way. I am really happy that I have found it. The topics covered here are crucially important.
Ali
April 1, 2022
All basics are explaining and it is clear. Just accent can be a little confuse but I understand and I am thanksfull for trainer
Ganesh
March 14, 2022
Not a good course and I won't recommend this to anyone... The whole programming tutorial is all about writing and running the code no details are shared like what the basic or the logic behind running those codes packages.. please create a video tutorial with atleast some knowledge or example what exactly those packages or code related to.
Vignesh
March 2, 2022
A great way of teaching. Thanks. Taught steps and methods clearly. Both in Python and R. Removed my hesitation on 2 new languages. Now can easily face these. Having 2 professors is very helpful. Because can learn from each other on a concept helps clarify much. Requests: 1. I am still not clear, which all columns need to be chosen on a dataset, for the X variable. Or is it always all columns except the dependent variable? 2. Please include Clustering methods in this course.
Ayinde
February 2, 2022
I learnt a lot from Start-Tech-Academy and I have recommended this course to my friends and coursemate here in Nigeria.
Sarmed
January 16, 2022
I am not big advocate of courses but this one is just an exception of that. Instructor knows where he has to lead us and he build the strategy on that starting from the preprocessing of data what other courses miss, other courses take this as for granted and within other modules mix the preprocessing. Thanks to the instructor, moreover I have learnt to not look at the reviews only for the course, just give it a try and it will speak itself if it is best fit for you. People may be wanting something else and then they blame the course.
Kshitij
January 13, 2022
This was one of the best courses I have come across to. I started from zero with no background knowledge, but eventually, I learned a lot of things. This course is highly recommended to all people who want to have good knowledge and practice ML and DL
Nwaneri
January 12, 2022
perfect course for me and for everyone seeking to kick start their career in machine learning. it gives you better understanding of the overall concept in each topic followed by a hands-on practical implementation
Muhammad
January 2, 2022
I know the basics of Python so I can see that the instructor is doing really well to teach the basics and instructor is taking the course in very Good and smooth way
Rameshwar
December 9, 2021
This is just an initial rating based on 2-3 lectures. The concept is well explained. Easy to understand.
Markus
December 1, 2021
course resources can't be downloaded. after seeing the free content i expected more hands-on chapters, but the paid content is mainly presentations.
Olusanya
November 19, 2021
I love this course because it's detailed and easy to understand. I just recommend that it is more data friendly.
Estifanos
October 10, 2021
well, I'm just getting started and the lecturers seem nice people. I feel like I will love this course if they keep explaining things as they are now.
Vayid
September 9, 2021
The mouse cursor has a block and it is always in front of the code being written as the instructor talks. I can only see after the code is run when the text moves up a line.

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