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Machine Learning & Deep Learning in Python & R

PUBLISHER:- Start-Tech Academy

LANGUAGE:- English

PRIZE:- 117.40$ 0$

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WHO CAN ACCESS THIS COURSE :-

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

WHAT ARE YOUR REQUIREMENTS FOR THIS COURSE ?

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

WHAT YOU ARE GOING TO LEARN FROM THIS COURSE ?

  • Figure out how to take care of genuine issue utilizing the Machine learning procedures
  • AI models, for example, Linear Regression, Logistic Regression, KNN, and so on
  • Progressed Machine Learning models, for example, Decision trees, XGBoost, Random Forest, SVM, and so on
  • Comprehension of fundamentals of insights and ideas of Machine Learning
  • Instructions to do fundamental measurable tasks and run ML models in Python
  • In-depth information on information assortment and information preprocessing for Machine Learning issue
  • Step by step instructions to change over business issue into a Machine learning issue

DESCRIPTION

You’re searching for a total Machine Learning and Deep Learning course that can assist you with dispatching a thriving vocation in the field of Data Science and Machine Learning, correct?

You’ve discovered the correct Machine Learning course!

In the wake of finishing this course you will have the option to:

· Confidently manufacture prescient Machine Learning and Deep Learning models to tackle business issues and make business procedure

· Answer Machine Learning related inquiries questions

· Participate and act in online Data Analytics rivalries, for example, Kaggle rivalries

Look at the chapter by chapter list beneath to perceive what all Machine Learning and Deep Learning models you will learn.

How this course will support you?

A Verifiable Certificate of Completion is introduced to all understudies who attempt this Machine learning fundamentals course.

In the event that you are a business administrator or a leader, or an understudy who needs to learn and apply AI in Real world issues of business, this course will give you a strong base for that by showing you the most mainstream methods of AI.

For what reason would it be advisable for you to pick this course?

This course covers all the means that one should take while tackling a business issue through straight relapse.

Most courses just spotlight on encouraging how to run the investigation however we accept that what occurs when running examination is significantly more significant for example prior to running investigation it is significant that you have the correct information and do some pre-preparing on it. Also, in the wake of running investigation, you ought to have the option to decide how great your model is and decipher the outcomes to really have the option to support your business.

What makes us qualified to educate you?

The course is instructed by Abhishek and Pukhraj. As chiefs in Global Analytics Consulting firm, we have helped organizations take care of their business issue utilizing AI procedures and we have utilized our experience to remember the down to earth parts of information investigation for this course

We are likewise the makers of the absolute most famous online courses – with more than 600,000 enlistments and a great many 5-star audits like these ones:

This is generally excellent, I love the reality the all clarification given can be perceived by a layman – Joshua

Much obliged to you Author for this brilliant course. You are the best and this course merits any cost. – Daisy

Our Promise

Instructing our understudies is our work and we are focused on it. On the off chance that you have any inquiries concerning the course content, practice sheet or anything identified with any subject, you can generally post an inquiry in the course or send us an immediate message.

Download Practice records, take Quizzes, and complete Assignments

With each talk, there are class notes joined for you to track. You can likewise take tests to check your comprehension of ideas. Each segment contains a training task for you to basically execute your learning.

Chapter by chapter list

Segment 1 – Python essential

This part kicks you off with Python.

This part will assist you with setting up the python and Jupyter climate on your framework and it’ll instruct

you how to play out some fundamental activities in Python. We will comprehend the significance of various libraries, for example, Numpy, Pandas and Seaborn.

Segment 2 – R fundamental

This part will assist you with setting up the R and R studio on your framework and it’ll show you how to play out some essential tasks in R.

Area 3 – Basics of Statistics

This segment is separated into five distinct talks beginning from sorts of information at that point kinds of measurements

at that point graphical portrayals to depict the information and afterward a talk on proportions of focus like mean

middle and mode and ultimately proportions of scattering like reach and standard deviation

Segment 4 – Introduction to Machine Learning

In this part we will realize – What machines Learning mean. What are the implications or various terms related with AI? You will see a few models so you comprehend what AI really is. It additionally contains steps engaged with building an AI model, not simply straight models, any AI model.

Segment 5 – Data Preprocessing

In this part you will realize what moves you have to make a bit by bit to get the information and afterward

set it up for the examination these means are significant.

We start with understanding the significance of business information then we will perceive how to do information investigation. We figure out how to do uni-variate examination and bi-variate investigation then we spread subjects like anomaly treatment, missing worth ascription, variable change and connection.

Area 6 – Regression Model

This segment begins with straightforward direct relapse and afterward covers numerous direct relapse.

We have secured the fundamental hypothesis behind every idea without getting excessively numerical so you

comprehend where the idea is coming from and how it is significant. Yet, regardless of whether you don’t comprehend

it, it will be alright as long as you figure out how to run and decipher the outcome as educated in the useful talks.

We likewise see how to measure models exactness, what is the significance of F measurement, how clear cut factors in the free factors dataset are deciphered in the outcomes, what are different varieties to the conventional least squared technique and how would we at last decipher the outcome to discover the response to a business issue.

Area 7 – Classification Models

This segment begins with Logistic relapse and afterward covers Linear Discriminant Analysis and K-Nearest Neighbors.

We have secured the essential hypothesis behind every idea without getting excessively numerical so you

comprehend where the idea is coming from and how it is significant. Yet, regardless of whether you don’t comprehend

it, it will be alright as long as you figure out how to run and decipher the outcome as instructed in the pragmatic talks.

We additionally see how to evaluate models execution utilizing disarray network, how all out factors in the autonomous factors dataset are deciphered in the outcomes, test-train part and how would we at last decipher the outcome to discover the response to a business issue.

Area 8 – Decision trees

In this segment, we will begin with the fundamental hypothesis of choice tree then we will make and plot a straightforward Regression choice tree. At that point we will grow our insight into relapse Decision tree to characterization trees, we will likewise figure out how to make a grouping tree in Python and R

Segment 9 – Ensemble strategy

In this part, we will begin our conversation about cutting edge group procedures for Decision trees. Outfits methods are utilized to improve the strength and precision of AI calculations. We will talk about Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.

Segment 10 – Support Vector Machines

SVM’s are extraordinary models and hang out regarding their idea. In this part, we will conversation about help vector classifiers and backing vector machines.

Area 11 – ANN Theoretical Concepts

This part will give you a strong comprehension of ideas engaged with Neural Networks.

In this segment you will find out about the single cells or Perceptrons and how Perceptrons are stacked to make an organization design. Whenever engineering is set, we comprehend the Gradient plunge calculation to discover the minima of a capacity and figure out how this is utilized to upgrade our organization model.

Area 12 – Creating ANN model in Python and R

In this part you will figure out how to make ANN models in Python and R.

We will begin this segment by making an ANN model utilizing Sequential API to tackle a characterization issue. We figure out how to characterize network engineering, design the model and train the model. At that point we assess the exhibition of our prepared model and use it to anticipate on new information. Ultimately we figure out how to spare and reestablish models.

We likewise comprehend the significance of libraries, for example, Keras and TensorFlow in this part.

Segment 13 – CNN Theoretical Concepts

In this part you will find out about convolutional and pooling layers which are the structure squares of CNN models.

In this part, we will begin with the fundamental hypothesis of convolutional layer, step, channels and highlight maps. We additionally clarify how dark scale pictures are unique in relation to hued pictures. In conclusion we examine pooling layer which get computational effectiveness our model.

Area 14 – Creating CNN model in Python and R

In this part you will figure out how to make CNN models in Python and R.

We will take a similar issue of perceiving style protests and apply CNN model to it. We will analyze the presentation of our CNN model with our ANN model and notice that the exactness increments by 9-10% when we use CNN. Nonetheless, this isn’t its finish. We can additionally improve exactness by utilizing certain strategies which we investigate in the following part.

Segment 15 – End-to-End Image Recognition venture in Python and R

In this segment we fabricate a total picture acknowledgment venture on shaded pictures.

We take a Kaggle picture acknowledgment rivalry and manufacture CNN model to illuminate it. With a straightforward model we accomplish almost 70% exactness on test set. At that point we learn ideas like Data Augmentation and Transfer Learning which assist us with improving precision level from 70% to almost 97% (in the same class as the champs of that opposition).

Segment 16 – Pre-handling Time Series Data

In this part, you will figure out how to picture time arrangement, perform include designing, do re-testing of information, and different devices to investigate and set up the information for models

Area 17 – Time Series Forecasting

In this segment, you will learn basic time arrangement models, for example, Auto-relapse (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX.

Before the finish of this course, your trust in making a Machine Learning or Deep Learning model in Python and R will take off. You’ll have a careful comprehension of how t

COURSE CONTENT

  • 42 sections • 279 lectures • 34h 56m total length

Introduction2 lectures • 4min

  • IntroductionPreview04:12
  • Course Resources00:05

Setting up Python and Jupyter Notebook9 lectures • 1hr 38min

  • Installing Python and AnacondaPreview03:04
  • Opening Jupyter NotebookPreview09:06
  • Introduction to JupyterPreview13:26
  • Arithmetic operators in Python: Python BasicsPreview04:28
  • Strings in Python: Python BasicsPreview19:07
  • Lists, Tuples and Directories: Python Basics18:41
  • Working with Numpy Library of Python11:54
  • Working with Pandas Library of Python09:15
  • Working with Seaborn Library of Python08:57

Setting up R Studio and R crash course8 lectures • 1hr 2min

  • Installing R and R studio05:52
  • Basics of R and R studio10:47
  • Packages in R10:52
  • Inputting data part 1: Inbuilt datasets of R04:21
  • Inputting data part 2: Manual data entry03:11
  • Inputting data part 3: Importing from CSV or Text files06:49
  • Creating Barplots in R13:43
  • Creating Histograms in R06:01

Basics of Statistics5 lectures • 30min

  • Types of Data04:04
  • Types of Statistics02:45
  • Describing data Graphically11:37
  • Measures of Centers07:05
  • Measures of Dispersion04:37

Introduction to Machine Learning2 lectures • 25min

  • Introduction to Machine Learning16:03
  • Building a Machine Learning Model08:42

Data Preprocessing25 lectures • 2hr 52min

  • Gathering Business Knowledge03:26
  • Data Exploration03:19
  • The Dataset and the Data Dictionary07:31
  • Importing Data in Python06:04
  • Importing the dataset into R03:00
  • Univariate analysis and EDD03:34
  • EDD in Python12:11
  • EDD in R12:43
  • Outlier Treatment04:15
  • Outlier Treatment in Python14:18
  • Outlier Treatment in R04:49
  • Missing Value Imputation03:36
  • Missing Value Imputation in Python04:57
  • Missing Value imputation in R03:49
  • Seasonality in Data03:35
  • Bi-variate analysis and Variable transformation16:14
  • Variable transformation and deletion in Python09:21
  • Variable transformation in R09:37
  • Non-usable variables04:44
  • Dummy variable creation: Handling qualitative data04:50
  • Dummy variable creation in Python05:45
  • Dummy variable creation in R05:01
  • Correlation Analysis10:05
  • Correlation Analysis in Python07:07
  • Correlation Matrix in R08:09

Linear Regression22 lectures • 3hr 18min

  • The Problem Statement01:25
  • Basic Equations and Ordinary Least Squares (OLS) method08:13
  • Assessing accuracy of predicted coefficients14:40
  • Assessing Model Accuracy: RSE and R squared07:19
  • Simple Linear Regression in Python14:07
  • Simple Linear Regression in R07:40
  • Multiple Linear Regression04:57
  • The F – statistic08:22
  • Interpreting results of Categorical variables05:04
  • Multiple Linear Regression in Python14:13
  • Multiple Linear Regression in R07:50
  • Test-train split09:32
  • Bias Variance trade-off06:01
  • Test train split in Python10:19
  • Test-Train Split in R08:44
  • Regression models other than OLS04:18
  • Subset selection techniques11:34
  • Subset selection in R07:38
  • Shrinkage methods: Ridge and Lasso07:14
  • Ridge regression and Lasso in Python23:50
  • Ridge regression and Lasso in R12:52
  • Heteroscedasticity02:30

Classification Models: Data Preparation13 lectures • 1hr 31min

  • The Data and the Data Dictionary08:14
  • Data Import in Python04:56
  • Importing the dataset into R03:00
  • EDD in Python18:01
  • EDD in R11:26
  • Outlier treatment in Python09:53
  • Outlier Treatment in R04:49
  • Missing Value Imputation in Python04:49
  • Missing Value imputation in R03:49
  • Variable transformation and Deletion in Python04:55
  • Variable transformation in R06:28
  • Dummy variable creation in Python05:45
  • Dummy variable creation in R05:19

The Three classification models2 lectures • 8min

  • Three Classifiers and the problem statement03:17
  • Why can’t we use Linear Regression?04:32

Logistic Regression12 lectures • 1hr 9min

  • Logistic Regression07:54
  • Training a Simple Logistic Model in Python12:25
  • Training a Simple Logistic model in R03:34
  • Result of Simple Logistic Regression05:11
  • Logistic with multiple predictors02:22
  • Training multiple predictor Logistic model in Python06:05
  • Training multiple predictor Logistic model in R01:48
  • Confusion Matrix03:47
  • Creating Confusion Matrix in Python09:55
  • Evaluating performance of model07:40
  • Evaluating model performance in Python02:22
  • Predicting probabilities, assigning classes and making Confusion Matrix in R06:23

Linear Discriminant Analysis (LDA)3 lectures • 21min

  • Linear Discriminant Analysis09:42
  • LDA in Python02:30
  • Linear Discriminant Analysis in R09:10

K-Nearest Neighbors classifier7 lectures • 56min

  • Test-Train Split09:30
  • Test-Train Split in Python06:46
  • Test-Train Split in R09:27
  • K-Nearest Neighbors classifier08:41
  • K-Nearest Neighbors in Python: Part 105:51
  • K-Nearest Neighbors in Python: Part 207:00
  • K-Nearest Neighbors in R08:50

Comparing results from 3 models2 lectures • 11min

  • Understanding the results of classification models06:06
  • Summary of the three models04:32

Simple Decision Trees18 lectures • 1hr 54min

  • Basics of Decision Trees10:10
  • Understanding a Regression Tree10:17
  • The stopping criteria for controlling tree growth03:15
  • The Data set for this part02:59
  • Importing the Data set into Python05:40
  • Importing the Data set into R06:26
  • Missing value treatment in Python03:38
  • Dummy Variable creation in Python04:58
  • Dependent- Independent Data split in Python04:02
  • Test-Train split in Python06:04
  • Splitting Data into Test and Train Set in R05:30
  • Creating Decision tree in Python03:47
  • Building a Regression Tree in R14:18
  • Evaluating model performance in Python04:10
  • Plotting decision tree in Python04:58
  • Pruning a tree04:16
  • Pruning a tree in Python10:37
  • Pruning a Tree in R09:18

Simple Classification Tree6 lectures • 40min

  • Classification tree06:06
  • The Data set for Classification problem01:38
  • Classification tree in Python : Preprocessing08:25
  • Classification tree in Python : Training13:13
  • Building a classification Tree in R08:59
  • Advantages and Disadvantages of Decision Trees01:34

Ensemble technique 1 – Bagging3 lectures • 24min

  • Ensemble technique 1 – Bagging06:39
  • Ensemble technique 1 – Bagging in Python11:05
  • Bagging in R06:20

Ensemble technique 2 – Random Forests4 lectures • 26min

  • Ensemble technique 2 – Random Forests03:56
  • Ensemble technique 2 – Random Forests in Python06:06
  • Using Grid Search in Python12:14
  • Random Forest in R03:58

Ensemble technique 3 – Boosting7 lectures • 1hr

  • Boosting07:10
  • Ensemble technique 3a – Boosting in Python05:08
  • Gradient Boosting in R07:10
  • Ensemble technique 3b – AdaBoost in Python04:00
  • AdaBoosting in R09:44
  • Ensemble technique 3c – XGBoost in Python11:07
  • XGBoosting in R16:08

Maximum Margin Classifier4 lectures • 12min

  • Content flow01:34
  • The Concept of a Hyperplane04:55
  • Maximum Margin Classifier03:18
  • Limitations of Maximum Margin Classifier02:28

Support Vector Classifier2 lectures • 12min

  • Support Vector classifiers10:00
  • Limitations of Support Vector Classifiers01:34

Support Vector Machines1 lecture • 7min

  • Kernel Based Support Vector Machines06:45

Creating a Support Vector Machine Model in Python14 lectures • 1hr 20min

  • Regression and Classification Models00:46
  • The Data set for the Regression problem02:59
  • Importing data for regression model05:40
  • X-y Split04:02
  • Test-Train Split06:04
  • Standardizing the data06:28
  • SVM based Regression Model in Python10:08
  • The Data set for the Classification problem01:38
  • Classification model – Preprocessing08:25
  • Classification model – Standardizing the data01:57
  • SVM Based classification model11:28
  • Hyper Parameter Tuning09:47
  • Polynomial Kernel with Hyperparameter Tuning04:07
  • Radial Kernel with Hyperparameter Tuning06:31

Creating Support Vector Machine Model in R7 lectures • 1hr 4min

  • Importing Data into R08:00
  • Test-Train Split05:30
  • Classification SVM model using Linear Kernel16:11
  • Hyperparameter Tuning for Linear Kernel06:28
  • Polynomial Kernel with Hyperparameter Tuning10:19
  • Radial Kernel with Hyperparameter Tuning06:31
  • SVM based Regression Model in R11:14

Introduction – Deep Learning4 lectures • 36min

  • Introduction to Neural Networks and Course flow04:38
  • Perceptron09:47
  • Activation Functions07:30
  • Python – Creating Perceptron model14:10

Neural Networks – Stacking cells to create network5 lectures • 1hr 6min

  • Basic Terminologies09:47
  • Gradient Descent12:17
  • Back Propagation22:27
  • Some Important Concepts12:44
  • Hyperparameter08:19

ANN in Python12 lectures • 1hr 58min

  • Keras and Tensorflow03:04
  • Installing Tensorflow and Keras04:04
  • Dataset for classification07:20
  • Normalization and Test-Train split05:59
  • Different ways to create ANN using Keras01:58
  • Building the Neural Network using Keras12:24
  • Compiling and Training the Neural Network model10:34
  • Evaluating performance and Predicting using Keras09:21
  • Building Neural Network for Regression Problem22:10
  • Using Functional API for complex architectures12:40
  • Saving – Restoring Models and Using Callbacks19:49
  • Hyperparameter Tuning09:05

ANN in R8 lectures • 1hr 29min

  • Installing Keras and Tensorflow02:54
  • Data Normalization and Test-Train Split12:00
  • Building,Compiling and Training14:57
  • Evaluating and Predicting09:46
  • ANN with NeuralNets Package08:07
  • Building Regression Model with Functional API12:34
  • Complex Architectures using Functional API08:50
  • Saving – Restoring Models and Using Callbacks20:16

CNN – Basics6 lectures • 36min

  • CNN Introduction07:43
  • Stride02:51
  • Padding05:07
  • Filters and Feature maps07:48
  • Channels06:31
  • PoolingLayer05:32

Creating CNN model in Python4 lectures • 25min

  • CNN model in Python – Preprocessing05:42
  • CNN model in Python – structure and Compile06:24
  • CNN model in Python – Training and results06:50
  • Comparison – Pooling vs Without Pooling in Python06:20

Creating CNN model in R6 lectures • 29min

  • CNN on MNIST Fashion Dataset – Model Architecture02:04
  • Data Preprocessing07:08
  • Creating Model Architecture06:05
  • Compiling and training02:54
  • Model Performance06:26
  • Comparison – Pooling vs Without Pooling in R04:33

Project : Creating CNN model from scratch in Python5 lectures • 29min

  • Project – Introduction07:05
  • Data for the project00:01
  • Project – Data Preprocessing in Python09:19
  • Project – Training CNN model in Python09:05
  • Project in Python – model results03:07

Project : Creating CNN model from scratch6 lectures • 30min

  • Project in R – Data Preprocessing10:28
  • CNN Project in R – Structure and Compile04:59
  • Project in R – Training02:57
  • Project in R – Model Performance02:22
  • Project in R – Data Augmentation07:12
  • Project in R – Validation Performance02:24

Project : Data Augmentation for avoiding overfitting2 lectures • 13min

  • Project – Data Augmentation Preprocessing06:46
  • Project – Data Augmentation Training and Results06:26

Transfer Learning : Basics6 lectures • 35min

  • ILSVRC04:10
  • LeNET01:31
  • VGG16NET02:00
  • GoogLeNet02:52
  • Transfer Learning05:15
  • Project – Transfer Learning – VGG1619:40

Transfer Learning in R2 lectures • 21min

  • Project – Transfer Learning – VGG16 (Implementation)12:44
  • Project – Transfer Learning – VGG16 (Performance)08:02

Time Series Analysis and Forecasting5 lectures • 22min

  • Introduction02:10
  • Time Series Forecasting – Use cases02:25
  • Forecasting model creation – Steps02:46
  • Forecasting model creation – Steps 1 (Goal)06:03
  • Time Series – Basic Notations09:02

Time Series – Preprocessing in Python10 lectures • 1hr 56min

  • Data Loading in Python17:51
  • Time Series – Visualization Basics09:28
  • Time Series – Visualization in Python27:10
  • Time Series – Feature Engineering Basics11:03
  • Time Series – Feature Engineering in Python18:01
  • Time Series – Upsampling and Downsampling04:17
  • Time Series – Upsampling and Downsampling in Python16:45
  • Time Series – Power Transformation02:32
  • Moving Average07:12
  • Exponential Smoothing02:07

Time Series – Important Concepts5 lectures • 38min

  • White Noise02:29
  • Random Walk04:23
  • Decomposing Time Series in Python09:41
  • Differencing06:16
  • Differencing in Python15:07

Time Series – Implementation in Python7 lectures • 54min

  • Test Train Split in Python11:28
  • Naive (Persistence) model in Python07:54
  • Auto Regression Model – Basics03:29
  • Auto Regression Model creation in Python09:22
  • Auto Regression with Walk Forward validation in Python08:20
  • Moving Average model -Basics04:33
  • Moving Average model in Python08:58

Time Series – ARIMA model4 lectures • 31min

  • ACF and PACF08:07
  • ARIMA model – Basics04:43
  • ARIMA model in Python13:15
  • ARIMA model with Walk Forward Validation in Python05:24

Time Series – SARIMA model3 lectures • 20min

  • SARIMA model07:26
  • SARIMA model in Python10:40
  • Stationary time Series01:42

Bonus Section1 lecture • 1min

  • Congratulations & About your certificate00:35

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