## Machine Learning Deep Learning in Python R

### 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

In-depth 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:Â· Confidently build predictive Machine Learning and Deep Learning models using R, Python to solve business problems and create business strategyÂ· Answer Machine Learning, Deep Learning, R, Python related interview questionsÂ· Participate and perform in online Data Analytics and Data Science competitions such as Kaggle competitionsCheck 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 – JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. – DaisyOur PromiseTeaching 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 AssignmentsWith 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 ContentsSection 1 – Python basicThis 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 basicThis 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 StatisticsThis 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 LearningIn 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 PreprocessingIn 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 ModelThis 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 ModelsThis 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 youunderstand where the concept is coming from and how it is important. But even if you don’t understandit, 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 treesIn 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 RSection 9 – Ensemble techniqueIn 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 MachinesSVM’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 ConceptsThis 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 RIn 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 ConceptsIn 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 RIn 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 RIn 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 DataIn 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 modelsSection 17 – Time Series ForecastingIn 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 hasnâ€™t always been, Python is the programming language of choice for data science. Hereâ€™s a brief history:In 2016, it overtook R on Kaggle, the premier platform for data science competitions.In 2017, it overtook R on KDNuggetsâ€™s annual poll of data scientistsâ€™ 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, itâ€™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 R1. Itâ€™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, itâ€™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 itâ€™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 â€“ and of course, itâ€™ll also make you a more flexible and marketable employee when youâ€™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 knowledgeâ€”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.

### Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Resources

Section 2: Setting up Python and Jupyter Notebook

Lecture 3 Installing Python and Anaconda

Lecture 4 This is a milestone!

Lecture 5 Opening Jupyter Notebook

Lecture 6 Introduction to Jupyter

Lecture 7 Arithmetic operators in Python: Python Basics

Lecture 8 Strings in Python: Python Basics

Lecture 9 Lists, Tuples and Directories: Python Basics

Lecture 10 Working with Numpy Library of Python

Lecture 11 Working with Pandas Library of Python

Lecture 12 Working with Seaborn Library of Python

Section 3: Setting up R Studio and R crash course

Lecture 13 Installing R and R studio

Lecture 14 Basics of R and R studio

Lecture 15 Packages in R

Lecture 16 Inputting data part 1: Inbuilt datasets of R

Lecture 17 Inputting data part 2: Manual data entry

Lecture 18 Inputting data part 3: Importing from CSV or Text files

Lecture 19 Creating Barplots in R

Lecture 20 Creating Histograms in R

Section 4: Basics of Statistics

Lecture 21 Types of Data

Lecture 22 Types of Statistics

Lecture 23 Describing data Graphically

Lecture 24 Measures of Centers

Lecture 25 Measures of Dispersion

Section 5: Introduction to Machine Learning

Lecture 26 Introduction to Machine Learning

Lecture 27 Building a Machine Learning Model

Section 6: Data Preprocessing

Lecture 28 Gathering Business Knowledge

Lecture 29 Data Exploration

Lecture 30 The Dataset and the Data Dictionary

Lecture 31 Importing Data in Python

Lecture 32 Importing the dataset into R

Lecture 33 Univariate analysis and EDD

Lecture 34 EDD in Python

Lecture 35 EDD in R

Lecture 36 Outlier Treatment

Lecture 37 Outlier Treatment in Python

Lecture 38 Outlier Treatment in R

Lecture 39 Missing Value Imputation

Lecture 40 Missing Value Imputation in Python

Lecture 41 Missing Value imputation in R

Lecture 42 Seasonality in Data

Lecture 43 Bi-variate analysis and Variable transformation

Lecture 44 Variable transformation and deletion in Python

Lecture 45 Variable transformation in R

Lecture 46 Non-usable variables

Lecture 47 Dummy variable creation: Handling qualitative data

Lecture 48 Dummy variable creation in Python

Lecture 49 Dummy variable creation in R

Lecture 50 Correlation Analysis

Lecture 51 Correlation Analysis in Python

Lecture 52 Correlation Matrix in R

Section 7: Linear Regression

Lecture 53 The Problem Statement

Lecture 54 Basic Equations and Ordinary Least Squares (OLS) method

Lecture 55 Assessing accuracy of predicted coefficients

Lecture 56 Assessing Model Accuracy: RSE and R squared

Lecture 57 Simple Linear Regression in Python

Lecture 58 Simple Linear Regression in R

Lecture 59 Multiple Linear Regression

Lecture 60 The F – statistic

Lecture 61 Interpreting results of Categorical variables

Lecture 62 Multiple Linear Regression in Python

Lecture 63 Multiple Linear Regression in R

Lecture 64 Test-train split

Lecture 65 Bias Variance trade-off

Lecture 66 Test train split in Python

Lecture 67 Test-Train Split in R

Lecture 68 Regression models other than OLS

Lecture 69 Subset selection techniques

Lecture 70 Subset selection in R

Lecture 71 Shrinkage methods: Ridge and Lasso

Lecture 72 Ridge regression and Lasso in Python

Lecture 73 Ridge regression and Lasso in R

Lecture 74 Heteroscedasticity

Section 8: Introduction to the classification Models

Lecture 75 Three classification models and Data set

Lecture 76 Importing the data into Python

Lecture 77 Importing the data into R

Lecture 78 The problem statements

Lecture 79 Why can’t we use Linear Regression?

Section 9: Logistic Regression

Lecture 80 Logistic Regression

Lecture 81 Training a Simple Logistic Model in Python

Lecture 82 Training a Simple Logistic model in R

Lecture 83 Result of Simple Logistic Regression

Lecture 84 Logistic with multiple predictors

Lecture 85 Training multiple predictor Logistic model in Python

Lecture 86 Training multiple predictor Logistic model in R

Lecture 87 Confusion Matrix

Lecture 88 Creating Confusion Matrix in Python

Lecture 89 Evaluating performance of model

Lecture 90 Evaluating model performance in Python

Lecture 91 Predicting probabilities, assigning classes and making Confusion Matrix in R

Section 10: Linear Discriminant Analysis (LDA)

Lecture 92 Linear Discriminant Analysis

Lecture 93 LDA in Python

Lecture 94 Linear Discriminant Analysis in R

Section 11: K-Nearest Neighbors classifier

Lecture 95 Test-Train Split

Lecture 96 Test-Train Split in Python

Lecture 97 Test-Train Split in R

Lecture 98 K-Nearest Neighbors classifier

Lecture 99 K-Nearest Neighbors in Python: Part 1

Lecture 100 K-Nearest Neighbors in Python: Part 2

Lecture 101 K-Nearest Neighbors in R

Section 12: Comparing results from 3 models

Lecture 102 Understanding the results of classification models

Lecture 103 Summary of the three models

Section 13: Simple Decision Trees

Lecture 104 Introduction to Decision trees

Lecture 105 Basics of Decision Trees

Lecture 106 Understanding a Regression Tree

Lecture 107 The stopping criteria for controlling tree growth

Lecture 108 Importing the Data set into Python

Lecture 109 Importing the Data set into R

Lecture 110 Missing value treatment in Python

Lecture 111 Dummy Variable creation in Python

Lecture 112 Dependent- Independent Data split in Python

Lecture 113 Test-Train split in Python

Lecture 114 Splitting Data into Test and Train Set in R

Lecture 115 Creating Decision tree in Python

Lecture 116 Building a Regression Tree in R

Lecture 117 Evaluating model performance in Python

Lecture 118 Plotting decision tree in Python

Lecture 119 Pruning a tree

Lecture 120 Pruning a tree in Python

Lecture 121 Pruning a Tree in R

Section 14: Simple Classification Tree

Lecture 122 Classification tree

Lecture 123 The Data set for Classification problem

Lecture 124 Classification tree in Python : Preprocessing

Lecture 125 Classification tree in Python : Training

Lecture 126 Building a classification Tree in R

Lecture 127 Advantages and Disadvantages of Decision Trees

Section 15: Ensemble technique 1 – Bagging

Lecture 128 Ensemble technique 1 – Bagging

Lecture 129 Ensemble technique 1 – Bagging in Python

Lecture 130 Bagging in R

Section 16: Ensemble technique 2 – Random Forests

Lecture 131 Ensemble technique 2 – Random Forests

Lecture 132 Ensemble technique 2 – Random Forests in Python

Lecture 133 Using Grid Search in Python

Lecture 134 Random Forest in R

Section 17: Ensemble technique 3 – Boosting

Lecture 135 Boosting

Lecture 136 Ensemble technique 3a – Boosting in Python

Lecture 137 Gradient Boosting in R

Lecture 138 Ensemble technique 3b – AdaBoost in Python

Lecture 139 AdaBoosting in R

Lecture 140 Ensemble technique 3c – XGBoost in Python

Lecture 141 XGBoosting in R

Section 18: Support Vector Machines

Lecture 142 Introduction to SVM’s

Lecture 143 The Concept of a Hyperplane

Lecture 144 Maximum Margin Classifier

Lecture 145 Limitations of Maximum Margin Classifier

Section 19: Support Vector Classifier

Lecture 146 Support Vector classifiers

Lecture 147 Limitations of Support Vector Classifiers

Section 20: Support Vector Machines

Lecture 148 Kernel Based Support Vector Machines

Section 21: Creating Support Vector Machine Model in Python

Lecture 149 Regression and Classification Models

Lecture 150 Importing and preprocessing data in Python

Lecture 151 Standardizing the data

Lecture 152 SVM based Regression Model in Python

Lecture 153 Classification model – Preprocessing

Lecture 154 Classification model – Standardizing the data

Lecture 155 SVM Based classification model

Lecture 156 Hyper Parameter Tuning

Lecture 157 Polynomial Kernel with Hyperparameter Tuning

Lecture 158 Radial Kernel with Hyperparameter Tuning

Section 22: Creating Support Vector Machine Model in R

Lecture 159 Importing and preprocessing data in R

Lecture 160 More about test-train split

Lecture 161 Classification SVM model using Linear Kernel

Lecture 162 Hyperparameter Tuning for Linear Kernel

Lecture 163 Polynomial Kernel with Hyperparameter Tuning

Lecture 164 Radial Kernel with Hyperparameter Tuning

Lecture 165 SVM based Regression Model in R

Section 23: Introduction – Deep Learning

Lecture 166 Introduction to Neural Networks and Course flow

Lecture 167 Perceptron

Lecture 168 Activation Functions

Lecture 169 Python – Creating Perceptron model

Section 24: Neural Networks – Stacking cells to create network

Lecture 170 Basic Terminologies

Lecture 171 Gradient Descent

Lecture 172 Back Propagation

Lecture 173 Some Important Concepts

Lecture 174 Hyperparameter

Section 25: ANN in Python

Lecture 175 Keras and Tensorflow

Lecture 176 Installing Tensorflow and Keras

Lecture 177 Dataset for classification

Lecture 178 Normalization and Test-Train split

Lecture 179 Different ways to create ANN using Keras

Lecture 180 Building the Neural Network using Keras

Lecture 181 Compiling and Training the Neural Network model

Lecture 182 Evaluating performance and Predicting using Keras

Lecture 183 Building Neural Network for Regression Problem

Lecture 184 Using Functional API for complex architectures

Lecture 185 Saving – Restoring Models and Using Callbacks

Lecture 186 Hyperparameter Tuning

Section 26: ANN in R

Lecture 187 Installing Keras and Tensorflow

Lecture 188 Data Normalization and Test-Train Split

Lecture 189 Building,Compiling and Training

Lecture 190 Evaluating and Predicting

Lecture 191 ANN with NeuralNets Package

Lecture 192 Building Regression Model with Functional API

Lecture 193 Complex Architectures using Functional API

Lecture 194 Saving – Restoring Models and Using Callbacks

Section 27: CNN – Basics

Lecture 195 CNN Introduction

Lecture 196 Stride

Lecture 197 Padding

Lecture 198 Filters and Feature maps

Lecture 199 Channels

Lecture 200 PoolingLayer

Section 28: Creating CNN model in Python

Lecture 201 CNN model in Python – Preprocessing

Lecture 202 CNN model in Python – structure and Compile

Lecture 203 CNN model in Python – Training and results

Lecture 204 Comparison – Pooling vs Without Pooling in Python

Section 29: Creating CNN model in R

Lecture 205 CNN on MNIST Fashion Dataset – Model Architecture

Lecture 206 Data Preprocessing

Lecture 207 Creating Model Architecture

Lecture 208 Compiling and training

Lecture 209 Model Performance

Lecture 210 Comparison – Pooling vs Without Pooling in R

Section 30: Project : Creating CNN model from scratch in Python

Lecture 211 Project – Introduction

Lecture 212 Data for the project

Lecture 213 Project – Data Preprocessing in Python

Lecture 214 Project – Training CNN model in Python

Lecture 215 Project in Python – model results

Section 31: Project : Creating CNN model from scratch

Lecture 216 Project in R – Data Preprocessing

Lecture 217 CNN Project in R – Structure and Compile

Lecture 218 Project in R – Training

Lecture 219 Project in R – Model Performance

Lecture 220 Project in R – Data Augmentation

Lecture 221 Project in R – Validation Performance

Section 32: Project : Data Augmentation for avoiding overfitting

Lecture 222 Project – Data Augmentation Preprocessing

Lecture 223 Project – Data Augmentation Training and Results

Section 33: Transfer Learning : Basics

Lecture 224 ILSVRC

Lecture 225 LeNET

Lecture 226 VGG16NET

Lecture 227 GoogLeNet

Lecture 228 Transfer Learning

Lecture 229 Project – Transfer Learning – VGG16

Section 34: Transfer Learning in R

Lecture 230 Project – Transfer Learning – VGG16 (Implementation)

Lecture 231 Project – Transfer Learning – VGG16 (Performance)

Section 35: Time Series Analysis and Forecasting

Lecture 232 Introduction

Lecture 233 Time Series Forecasting – Use cases

Lecture 234 Forecasting model creation – Steps

Lecture 235 Forecasting model creation – Steps 1 (Goal)

Lecture 236 Time Series – Basic Notations

Section 36: Time Series – Preprocessing in Python

Lecture 237 Data Loading in Python

Lecture 238 Time Series – Visualization Basics

Lecture 239 Time Series – Visualization in Python

Lecture 240 Time Series – Feature Engineering Basics

Lecture 241 Time Series – Feature Engineering in Python

Lecture 242 Time Series – Upsampling and Downsampling

Lecture 243 Time Series – Upsampling and Downsampling in Python

Lecture 244 Time Series – Power Transformation

Lecture 245 Moving Average

Lecture 246 Exponential Smoothing

Section 37: Time Series – Important Concepts

Lecture 247 White Noise

Lecture 248 Random Walk

Lecture 249 Decomposing Time Series in Python

Lecture 250 Differencing

Lecture 251 Differencing in Python

Section 38: Time Series – Implementation in Python

Lecture 252 Test Train Split in Python

Lecture 253 Naive (Persistence) model in Python

Lecture 254 Auto Regression Model – Basics

Lecture 255 Auto Regression Model creation in Python

Lecture 256 Auto Regression with Walk Forward validation in Python

Lecture 257 Moving Average model -Basics

Lecture 258 Moving Average model in Python

Section 39: Time Series – ARIMA model

Lecture 259 ACF and PACF

Lecture 260 ARIMA model – Basics

Lecture 261 ARIMA model in Python

Lecture 262 ARIMA model with Walk Forward Validation in Python

Section 40: Time Series – SARIMA model

Lecture 263 SARIMA model

Lecture 264 SARIMA model in Python

Lecture 265 Stationary time Series

Lecture 266 The final milestone!

Section 41: Congratulations & About your certificate

Lecture 267 Bonus Lecture

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

#### Course Information:

Udemy | English | 33h 5m | 11.99 GB

Created by: Start-Tech Academy

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