Machine Learning & Deep Learning in Python & R

Machine Learning & Deep Learning in Python & R

Machine Learning & Deep Learning in Python & R

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

Language: english

Note: 4.5/5 (4,734 notes) 348,342 students

Instructor(s): Start-Tech Academy

Last update: 2022-01-15

What you’ll 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
  • Indepth knowledge of data collection and data preprocessing for Machine Learning problem
  • How to convert business problem into a Machine learning problem

 

Requirements

  • Students will need to install Anaconda software but we have a separate lecture to guide you install the same

 

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.

 

Who this course is for

  • People pursuing a career in data science
  • Working Professionals beginning their Data journey
  • Statisticians needing more practical experience

 

Course content

  • Introduction
    • Introduction
    • Course Resources
  • Setting up Python and Jupyter Notebook
    • Installing Python and Anaconda
    • This is a milestone!
    • 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
    • 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
    • The final milestone!
  • Congratulations & About your certificate
    • Bonus Lecture

 

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