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Data Science Prime Pack

Hand-Picked courses & E-Books to become a Data Scientist
• Video
Mathematics for Data Science and Machine Learning using R
• Introduction
• Overview of R
• Overview of R Workspace &amp; Basic Commands 22:50 Preview
• LAB 1 Introduction 02:27 Preview
• LAB 1 Solution 11:10
• Linear Algebra
• Scalars Vectors and Matrices 12:15
• Application Scalars Vectors and Matrices 18:41
• LAB 1 Intro Scalars Vectors and Matrices 01:38
• LAB 1 Solution Scalars Vectors and Matrices 12:15
• Vector Operations 11:59
• Application Vector Operations 22:10
• LAB 2 Intro Vector Operations 01:54
• LAB 2 Solution Vector Operations 11:55
• Matrix Operations Addition Subtraction Multiplication 17:40
• Application Matrix Operations Addition Subtraction Multiplication 11:08
• LAB 3 Intro Matrix Operations Addition Subtraction Multiplication 01:12
• LAB 3 Solution Matrix Operations Addition Subtraction Multiplication 04:07
• Matrix Operations Transposes and Inverses 11:33
• Application Matrix Operations Transposes and Inverses 12:54
• LAB 4 Intro Matrix Operations Transposes and Inverses 01:00
• LAB 4 Solution Matrix Operations Transposes and Inverses 03:19
• What is Linear Regression 11:27
• Application What is Linear Regression 28:05
• LAB 5 Intro What is Linear Regression 02:17
• Lab 5 Solution What is Linear Regression 12:12
• Matrix Representation of Linear Regression 12:28
• Application Matrix Representation of Linear Regression 13:37
• Lab 6 Intro Matrix Representation of Linear Regression 03:21
• Lab 6 Solution Matrix Representation of Linear Regression 12:45
• Section Calculus
• Functions and Tangent Lines 15:31
• Application Functions and Tangent Lines 18:31
• Lab 1 Intro Functions and Tangent Lines 01:51
• Lab 1 Solution Functions and Tangent Lines 13:12
• Derivatives 09:50
• Application Derivatives 18:35
• Lab 2 Intro Derivatives 02:38
• Lab 2 Solution Derivatives 14:58
• Optimization Using Derivatives Single Variable Functions 11:58
• Application Optimization Using Derivatives Single Variable 10:22
• Intro Optimization Using Derivatives Single Variable Function 01:26
• Lab 3 Solution Optimization Using Derivatives Single Variable Function 08:15
• Optimization Using Derivatives Two Variable Functions 10:42
• Application Optimization Using Derivatives Two Variable 17:03
• Lab 4 Intro Optimization Using Derivatives Two Variable Functions 02:25
• Lab 4 Solution Optimization Using Derivatives Two Variable Function 05:02
• Linear Regression The Calculus Optimization Perspective 19:59
• Application Linear Regression The Calculus Optimization Perspective 16:41
• Lab 5 Intro Linear Regression The Calculus Optimization Perspective 02:56
• Lab 5 Solution Linear Regression The Calculus Optimization Perspective 14:26
• Tying it All Together Vector Calculus
• Orthogonal Vectors and Linear Independence 10:32
• Application Orthogonal Vectors and Linear Independence 13:15
• Lab 1 Intro Orthogonal Vectors and Linear Independence 02:47
• Lab 1 Solution Orthogonal Vectors and Linear Independence 12:07
• Eigenvectors and Eigenvalues 12:47
• Application Eigenvectors and Eigenvalues 09:50
• Lab 2 Intro Eigenvectors and Eigenvalues 00:49
• Lab 2 Solution Eigenvectors and Eigenvalues 04:42
• Application Vectors Gradient Descent 10:51
• Lab 3 Intro Vectors Gradient Descent 01:21
• Lab 3 Solution Vectors Gradient Descent 12:50
• Linear Regression The Gradient Descent Perspective 04:17
• Application Linear Regression The Gradient Descent Perspective 17:55
• Lab 4 Intro Linear Regression The Gradient Descent Perspective 01:15
• Lab 4 Solution Linear Regression The Gradient Descent Perspective 07:20
• Video
The Data Science & Machine Learning Bootcamp in Python
• Introduction
• Understand Python for Data Science
• Python for Data Science 03:20 Preview
• Launch Notebook on Linux 00:57
• Launch Notebook on WIndows 02:10 Preview
• Folder Structure 00:51
• Python Comments &amp; Operations 03:02
• Python Data Types 01:54
• Python Lists 04:21
• Lists - Negative Indexing 02:44
• Python Dictionaries 02:17
• Python Tuples 02:36
• Python Sets 02:18
• Python Boolean Type 02:08
• Conditional Statements 03:38
• Python Functions 01:41
• Python For Loop 02:11
• Python While Loop 01:55
• Python Map Function 01:41
• Python Range Function 01:34
• Python Exercise 00:49
• Python Project Solutions 09:36
• Manage Packages in Python
• pip &amp; virtualenv Intuition 04:44
• pip &amp; virtualenv Practical 05:34
• Installing Packages using the Anaconda Navigator 01:44
• Perform Numerical Computation with NumPy
• NumPy Introduction 06:00
• NumPy Arrays 04:04
• Checking Documentation in Notebooks 00:38
• Indexing One Dimensional Array 02:27
• Indexing Multi-dimensional Array 07:23
• NumPy Operations 02:47
• NumPy Project 00:39
• NumPy Project Solutions 06:07
• Manipulate Data with Pandas
• Pandas Introduction 02:58
• Pandas DataFrame 00:59
• Resetting the Index 01:06
• Deleting Columns 00:45
• Dealing with Null Values 01:33
• Creating New Columns 00:33
• Selecting in Pandas 02:52
• Grouping Data 02:29
• Exporting a Pandas DataFrame 01:05
• Creating Pivot Tables 01:08
• Pandas Project 00:39
• Pandas Project Solutions
• Part 1 02:44
• Part 2 00:36
• Part 3 01:11
• Part 4 01:38
• Part 5 02:03
• Part 6 01:07
• Part 7 02:02
• Data Visualization in Matplotlib
• Matplotlib Vertical Bar Plot 03:33
• Matplotlib Horizontal Bar Plot 00:48
• Matplotlib Scatter Plot 01:57
• Matplotlib Histogram 00:31
• Matplotlib Pie Chart 02:03
• Matplotlib Line Plot 00:26
• Matplotlib Subplots 03:04
• Matplotlib Figure &amp; Axes Part one 01:33
• Matplotlib Figure &amp; Axes Part Two 01:42
• Matplotlib Project &amp; Solutions 00:50
• Data Visualization in Seaborn - Categorical Plots
• Seaborn Count Plot 01:25
• Seaborn Violin Plot 00:48
• Seaborn - Adding Hue 00:40
• Seaborn Strip Plot 00:27
• Swarm Plot with Hue 00:49
• Seaborn Order X Values 00:39
• Strip Plot with Hue 00:40
• Seaborn Boxplot 00:48
• Seaborn Boxen Plot 00:28
• Seaborn Barplot 01:19
• Data Visualization in Seaborn - Visualizing Distributions
• Joint &amp; Scatter Plots 01:20
• Seaborn Hexagonal Bins &amp; Kernel Density Estimation 00:36
• Seaborn Distplot 00:55
• Seaborn Pair Plot 00:34
• Seaborn Line Plot 00:23
• Seaborn with Matplotlib Subplots
• Subplots in Seaborn 02:37 Preview
• Seaborn Subplots with figure &amp; Axes 02:22
• Matrix Visualization in Seaborn
• Seaborn Heatmap 01:03
• Visualize Linear Relationships in Seaborn
• Regression Plots in Seaborn 01:44
• Seaborn Jointplot with Regression 00:14
• Seaborn Multi-Plot Grids
• Seaborn FacetGrid 03:52
• Seaborn PairGrid 00:58
• Word Cloud
• Visualization using Word Clouds 02:38
• Seaborn &amp; Word Cloud Exercise and Solutions
• Seaborn &amp; Word Cloud Exercise and Solutions 00:47
• Build Interactive Visuals with Plotly
• Plotly &amp; Jupyter Notebooks 00:44
• Plotly Introduction 01:53
• Plotly Express 01:45
• Plotly Line Plot 00:55
• Plotly Bar Plot 01:30
• Plotly Animations 01:12
• Plotly Density Heatmap 00:48
• Visualizing on Maps using Plotly 03:47
• Subplots in Plotly 02:29
• Plotly Project &amp; Solutions 00:38
• Building Data Science Applications with Streamlit
• Section Introduction 00:12
• Streamlit Introduction 01:13
• Building the Data Science Application 13:21
• Host the Application on Heroku - Set up 00:31
• Host the Application on Heroku - Implementation 07:06
• Build Data Science Application Project 00:33
• Building Dashboards in Power BI Desktop
• Section Introduction 00:38
• Setting up a Virtual Machine 07:27
• File Sharing 01:01
• Dashboards and Power BI Overview 04:08
• Sacked Column Chart 03:53
• Clustered Bar Chart 02:56 Preview
• 100% Stacked Bar Chart 03:48
• 100% Stacked Column Chart 02:29
• Line chart - Drill Up and Down 03:19
• Area Chart 02:39
• Stacked Area Chart 02:43
• Line and Stacked Column Chart 05:03
• Ribbon Chart 03:07
• Waterfall Chart 05:14
• Funnel Chart 01:26
• Scatterplot 03:03
• Pie Chart 02:44
• Donut chart 02:00
• Treemap 02:24
• Maps in Power BI 01:36
• Gauge and Slicer 02:22
• Power Card 01:42
• Multi-row Card 01:01
• Matrix 05:36
• Table 01:42
• Power BI Dashboard Creation 11:31
• Power BI Project 00:42
• Supervised Machine Learning
• Introduction to Machine Learning 02:48
• Linear Regression Intuition 02:35
• Linear Regression in Scikit-Learn 05:02
• Linear Regression Exercise 00:10
• 2 Linear Regression Solutions 05:41
• Logistic Regression Intuition 03:52
• Logistic Regression in Python 03:39
• Logistic Regression Project 00:17
• Logistic Regression Solutions 02:23
• Decision Trees Intuition 02:40
• Random Forest Intuition 03:05
• Decision Tree &amp; Random Forest classifier in Scikit-Learn 03:35
• Decision Tree &amp; Random Forest Classification Project 00:07
• Decision Tree &amp; Random Forest Classifier Solutions 03:17
• Decision Tree &amp; Random Forest Regression Part 1 03:23
• Decision Tree &amp; Random Forest Regression Part 2 01:00
• Random Forest Regression Part 3 - Feature Importance 02:04
• Visualize Tree in Random Forest Regression 01:05
• Random Forest Regression Exercise 00:05
• Random Forest Regression Solutions 06:18
• K Nearest Neighbors - Getting Started 03:13
• Checking for Outliers 04:48
• More Exploratory Data Analysis 04:22
• Student and Income Plots 01:53
• Peasonr - Relationship between the income and balance 01:54
• Chi Square Test - Relationship Between Defaulting and Being a Student 03:09
• T-Test - Is the mean Income of both defaulters and non defaulters are the same? 01:37
• Feature Engineering 01:07
• KNN Implementation in Python 04:28
• Support Vector Classifier in Python 01:43
• Support Vector Machine Exercise &amp; Solutions 01:01
• Handling Imbalanced Data 01:16 Preview
• LightGBM Intuition 02:15 Preview
• LightGBM Classifier 02:17
• LightGBM Classifier Project 00:06
• LightGBM Classifier Project Solutions 02:39
• LightGBM Regressor 02:15
• LightGBM Regressor Project 00:07
• LightGBM Regressor Project Solutions 03:20
• XGBoost Classifier 02:19
• XGBoost Classifier Project 00:08
• XGBoost classifier Project Solutions 02:32
• XGBoost Regressor 01:42
• XGBoost Regressor Project 00:08
• XGBoost Regressor Solutions 02:52
• Tuning &amp; Model Selection 03:20
• CatBoost Intuition 02:19
• CatBoost Classifier 02:37
• CatBoost Classifier Exercise 00:06
• CatBoost Classifier Project Solutions 02:58
• CatBoost Regression 04:02
• CatBoost Regression Exercise 00:06
• CatBoost Regression Project Solutions 03:37
• Time Series Analysis 04:04
• Time Series Exercise 00:06
• Time Series Project Solutions 02:29
• K-Means - Unsupervised Machine Learning
• Convert the Data to Dummy Variables 00:50
• Principal Component Analysis 01:34
• Data Scaling 00:54
• K-Means Implementation 02:59
• Selecting the Best Number of Clusters 03:38
• Cluster Analysis 01:22
• K-means Exercise 01:13
• K-Means Exercise Solutions 07:39
• Feature Ranking with Recursive Feature Elimination
• Feature Selection - Section Introduction 03:27
• Feature Selection Introduction 03:27
• Feature selection - Implement RFE 01:49
• Feature selection - Create a Pipeline 02:07
• Feature selection -Repeated Stratified K Fold 03:12
• Feature selection - Fit the Pipeline 04:56
• Feature selection 6 - Automatic Feature Selection 07:49
• Feature selection - Exercise 00:16
• Association Rule Mining - Apriori
• Association Rule Mining 05:25
• Apriori Data Preparation 12:52
• Apriori Implementation 06:05
• Apriori Exercise Solutions 09:26
• Natural Language Processing
• Installing the Natural Language Toolkit 00:59 Preview
• Import Packages Needed for NLP 05:29
• Load the Text Data 01:48
• Clean the Data 03:53
• Remove Stop Words 10:15
• Stemming VS Lemmatization 06:53
• Numerical Representation of Textual Data 10:49
• Occurrence Frequencies 08:58
• Fit the Text Data to Machine Learning Models 05:18
• Persist the Vectorizer and the Model 01:50
• Host the Model - Flask Intro 01:04
• Host the Model - Flask Logic 16:45
• Host the Model - The HTML 01:48
• Host the Model - The Procfile 00:57
• Host the Model - Heroku Deployment 06:58
• Host the Model - NLP Project 00:18
• Deep Learning &amp; Next Steps
• Core Concepts 11:51
• Data Preprocessing 03:32
• Building the Network 05:57
• Evaluating the Model 00:51
• Plotting the Model Loss 02:05
• Overfitting - Classification 07:40
• Keras Callbacks 03:18
• Custom Keras Callbacks 02:12
• Visualization in TensorFlow - Tensorboard 03:56
• Saving the Model 01:26
• Automated Machine Learning
• Section Introduction 00:21
• Automated Machine Learning Intuition 03:57
• Automated Machine Learning on Google Colab 05:36
• Automated Machine Learning Exercise 01:11
• File Resources
• File Resources
• Video
COMPLETE Machine Learning BOOTCAMP
• AZURE Machine Learning Overview
• Introduction to Azure Machine Learning 11:36 Preview
• Computer vision
• Computer Vision : Create Service 10:20 Preview
• Computer Vision : Analyze image , text in image and generate thumbnail 04:17
• Content Moderator
• Content Moderator:Text Moderation 08:11 Preview
• Content Moderator:Image and Video Moderation 05:37 Preview
• Content Moderator : Explore Dashboard 01:03
• Content Moderator : Create Instance 03:14
• Custom Vision
• Custom Vision : Create project 03:55
• Custom Vision : Train model and predict unlabeled image 03:10
• Custom Vision : Delete Resource 00:54
• Text Analytics
• Text Analytics :Create Service 02:36
• Text Analytics :Language Detection 05:16
• Text Analytics :Sentiment Analysis 04:33
• Text Analytics :Key Phrase Extraction 02:54
• Text Analytics :Entity Recognition 03:57
• Translate
• Translate :Creating Service 02:33
• Translate :Deploy 09:24
• AWS Machine Learning
• Introduction to AWS Machine Learning 10:11
• Amazon Comprehend
• Amazon Comprehend 07:24
• Practical:Amazon Comprehend 05:54
• (PythonBoto3)Comprehend 1 02:04
• (PythonBoto3)Comprehend 2 01:54
• (PythonBoto3)Comprehend 3 02:56
• Amazon Lex and Amazon Polly
• Amazon Lex 08:05
• Amazon Lex Part-2 07:06
• Amazon Polly 06:57
• Practical:Chatbot using Amazon Lex 11:54
• Practical:Amazon Polly 09:56
• (PythonBoto3)Polly 02:31
• Amazon Rekognition
• Amazon Rekognition 06:12
• Object and scene detection(Overview) 03:33
• Object and scene detection(Practical) 02:45
• (PythonBoto3)Detect Label 02:38
• Facial analysis(Overview) 03:31
• Facial analysis(Practical) 04:38
• (PythonBoto3)Detect Face 02:00
• Celebrity recognition(Overview) 02:42
• (PythonBoto3)Celebrity Recognition 03:00
• Amazon SageMaker and AWS DeepLens
• Amazon SageMaker 09:49
• AWS DeepLens 05:37
• Machine Learning (Using Amazon ML to Predict Responses to a Marketing Offer)
• ML 1 -Prepare Your Data 02:02
• ML 2-Create a Training Datasource 04:27
• ML 3-Create an ML Model 00:54
• ML 4-Use the ML Model to Generate Predictions 12:28
• ML 5-Clean Up 03:38
• Amazon Transcribe and Translate
• Amazon Transcribe 06:12
• Amazon Translate 04:12
• Practical:Amazon Transcribe 10:11
• (PythonBoto3)Transcribe 06:49
• Practical:Amazon Translate 03:22
• (PythonBoto3)Translate 02:09
• Video
Python & Data Science: Data Science with Real Life Problems
• Introduction
• Python Introduction 02:06 Preview
• Python Installation 02:28
• Datai Team Github and Resources 01:00
• Python Basics
• Variables 08:14 Preview
• Strings 07:27 Preview
• Numbers 02:47
• Built-in Functions 04:19
• User Defined Functions 09:05
• Default and Flexible Functions 11:33
• Practice 1 06:59
• Lambda Function 04:31
• List 10:01
• Tuple 03:22
• Dictionary 05:58
• Conditionals (if-else Statements) 12:37
• Practice 2 12:21
• For Loop 06:26
• While Loop 05:39
• Practice 3 04:16
• Object Oriented Programming
• Class and Constructor 09:42
• Class Variables 08:04
• Class Example 04:32
• Dealing with Programming Errors
• Syntax Error 03:03
• Try - Except 1 04:03
• Try - Except 2 05:15
• Numpy
• Numpy Basics 11:21 Preview
• Numpy Basic Operations 12:58
• Indexing and Slicing 06:53
• Shape Manipulation 06:21
• Stacking Arrays 03:11
• Convert and Copy Array 05:51
• Pandas
• Introduction to Pandas 06:14 Preview
• Pandas Basic Methods 04:34
• Indexing and Slicing Data Frames 08:34
• Filtering Pandas Data Frame 05:10
• List Comprehension 09:52
• Concatenating Data 06:09
• Transforming Data 02:47
• Visualization with Matplotlib
• Pandas Review 08:29 Preview
• Line Plot 09:37
• Scatter Plot 01:48
• Histogram 03:31
• Bar Plot 02:24
• Subplots 03:27
• Python Conclusion
• Python Conclusion 01:00
• Data Science Introduction
• Data Science Introduction 01:38
• Kaggle and Data Science
• Kaggle Description 1 10:12
• Kaggle Description 2 07:25
• What is Kaggle Notebook (Kernel) ? 14:04
• Kaggle Profile Page 03:36
• Being Successful in Kaggle 03:04
• Introduction to Data Science
• Data Science Notebook Introduction 07:05
• Import and First Look Data 11:11
• Matplotlib 06:57
• Dictionary, Pandas and Logic Control 08:23
• Loop Data Structures (while and for) 06:54
• Python Data Science Tool Box
• User Defined Function and Scope 05:56
• Nested, Default, Flexible, Lambda, Anonymous Functions 09:12
• Iterators and List Comprehension 10:32
• Cleaning Data
• Diagnose Data for Cleaning 05:05
• Exploratory Data Analysis (EDA) 09:51
• Visual Exploratory Data Analysis 03:18
• Tidy and Pivoting Data 04:30
• Concatenating Data and Data Types 06:38
• Missing Data and Testing with Assert 05:37
• Pandas Foundation
• Review of Pandas, Building Data Frames from Scratch,Visual and Statistical EDA 08:19
• Indexing and Resampling Pandas Time Series 12:00
• Manipulating Data Frames with Pandas
• Indexing, Slicing, Filtering and Transforming Data Frames 11:23
• Index Objects, Hierarchical Indexing, Pivoting, Stacking-Unstacking and Melting 10:57
• Titanic Project
• Titanic Project Introduction 03:01 Preview
• Overview and Load Data 11:32
• Variable Description 09:00
• Univariate Variable Analysis: Categorical Variables 14:26
• Univariate Variable Analysis: Numeric Variables 07:13
• Exploratory Data Analysis (EDA) 10:08
• Outlier Detection 08:57
• Missing Value 10:05
• Titanic Project Conclusion 01:54
• Data Science Conclusion
• Data Science Conclusion 01:05
• Video
Neural Networks (ANN) using Keras and TensorFlow in Python
• Introduction
• Welcome to the course 02:59 Preview
• Introduction to Neural Networks and Course flow 04:38
• Course resources
• Setting up Python and Jupyter Notebook 9 lectures
• Installing Python and Anaconda 03:04 Preview
• Opening Jupyter Notebook 09:06 Preview
• Introduction to Jupyter 13:26 Preview
• Arithmetic operators in Python: Python Basics 04:28 Preview
• Strings in Python: Python Basics 19:07
• Lists, Tuples and Directories: Python Basics 18:41
• Working with Numpy Library of Python 11:54
• Working with Pandas Library of Python 09:15
• Working with Seaborn Library of Python 08:57
• Single Cells - Perceptron and Sigmoid Neuron
• Perceptron 09:47
• Activation Functions 07:30
• Python - Creating Perceptron model 14:10
• Neural Networks - Stacking cells to create network
• Basic Terminologies 09:47
• Back Propagation 22:27
• Important concepts: Common Interview questions
• Some Important Concepts 12:44
• Standard Model Parameters
• Hyperparameters 08:19
• Tensorflow and Keras
• Keras and Tensorflow 03:04
• Installing Tensorflow and Keras 04:04
• Python - Dataset for classification problem
• Dataset for classification 07:19
• Normalization and Test-Train split 05:59
• Python - Building and training the Model
• Different ways to create ANN using Keras 01:58
• Building the Neural Network using Keras 12:24
• Compiling and Training the Neural Network model 10:34
• Evaluating performance and Predicting using Keras 09:21
• Python - Solving a Regression problem using ANN
• Building Neural Network for Regression Problem 22:10
• Complex ANN Architectures using Functional API
• Using Functional API for complex architectures 12:40
• Saving and Restoring Models
• Saving - Restoring Models and Using Callbacks 19:49
• Data Exploration 03:19
• The Dataset and the Data Dictionary 07:31
• Importing Data in Python 06:03
• Univariate analysis and EDD 03:33
• EDD in Python 12:11
• Outlier Treatment 04:15
• Outlier Treatment in Python 14:18
• Missing Value Imputation 03:36
• Missing Value Imputation in Python 04:57
• Seasonality in Data 03:34
• Bi-variate analysis and Variable transformation 16:14
• Variable transformation and deletion in Python 09:21
• Non-usable variables 04:44
• Dummy variable creation: Handling qualitative data 04:50
• Dummy variable creation in Python 05:45
• Correlation Analysis 10:05
• Correlation Analysis in Python 07:07
• Add-on 2: Classic ML models - Linear Regression
• The Problem Statement 01:25
• Basic Equations and Ordinary Least Squares (OLS) method 08:13
• Assessing accuracy of predicted coefficients 14:40
• Assessing Model Accuracy: RSE and R squared 07:19
• Simple Linear Regression in Python 14:06
• Multiple Linear Regression 04:57
• The F - statistic 08:22
• Interpreting results of Categorical variables 05:04
• Multiple Linear Regression in Python 14:13
• Test-train split 09:32
• Test train split in Python 10:19
• Video
Data Analysis using NumPy and Pandas
• Data Analysis using NumPy and Pandas
• Introduction 01:09 Preview
• NumPy Introduction 34:09 Preview
• Python Numpy Array 22:32 Preview
• Indexing &amp; Slicing - 1 19:29
• Indexing &amp; Slicing - 2 30:21
• Statistical Functions, Operators &amp; Random Numbers 20:12
• Introduction Series &amp; DataFrame 41:00
• Date Range &amp; Inspecting Data 29:36
• Indexing &amp; Slicing on DataFrame - 1 30:13
• loc &amp; iloc 31:18
• Indexing &amp; Slicing on DataFrame - 2 18:24
• Concatenation &amp; Descriptive Statistics 31:54
• Merging DataFrames 29:28
• Working with Text Data 18:40
• Data Visualization using Pandas 19:33
• What is Data Science 30:42
• What is Machine Learning 25:06
• Video
Practical Deep Learning with Tensorflow 2 and Keras
• Introduction
• About the Instructor 01:37 Preview
• Dive into Machine Learning 13:10 Preview
• Making Predictions 07:02
• A Bit of Theory
• Machine Learning Pipeline 09:13
• Regression 13:01
• Binary and Multi-class Classification 14:29
• Recap and a Link to More Theory 02:43
• Installation and Setup
• Environment setup for Windows (and some issues with it) 05:37 Preview
• Environment setup for Mac and Linux 05:05
• Say Hi to Keras
• Data Preparation 10:11
• Training and Testing 10:32
• Using TensorBoard to Visualize Learning 03:17
• Real World Case Study: Predicting Protein Functions
• Problem Description and Data View 08:32
• Pre-processing the Data 15:52
• Train, Test Split 03:11
• Shapes in Depth (or how not to have headaches for days) 04:32
• Sequential Model 08:58
• Functional API 05:25
• Convolutional Neural Networks (CNN)
• Basics and Rationale 10:13
• CNN in Keras (or why Keras is better than your ML tool) 08:30
• Pooling (and why it&#039;s not that important) 04:25
• Dropout (and why you should always consider it) 03:51 Preview
• Graph-based Models
• Functional API for CNN 04:27
• Inception Module 09:36 Preview
• Residual Connections 05:09
• Finishing Touches
• Parting Words 03:56
• Video
12 Real World CaseStudies for Machine Learning
• Introduction
• Introduction 03:37 Preview
• Data and NoteBook Resources
• REGRESSION CASE STUDY : Retail Store Sales Prediction
• Intro and Business Challenge 02:14 Preview
• General Overview on Regression Metrics 11:06 Preview
• Basic Data imports 05:07
• Visualization and EDA 06:46
• Feature Engineering 11:39
• Model Building and Evaluation 07:38
• Conclusion 01:09
• CLASSIFICATION CASE STUDY : Telstra Telecom Network Disruptions Challenge
• Intro and Business Challenge 05:56 Preview
• General Overview on Classification Metrics 22:06
• Data import and Data engineering 07:54
• Feature engineering 07:39
• Feature engineering ( Part 2) 14:18
• Feature engineering ( Part 3) 04:10
• Feature Selection 03:52
• Model prediction and Evaluation 03:48
• Balancing the dataset and RePredicting 07:07
• Conclusion 01:30
• REGRESSION CASE STUDY : Restaurant Sales Prediction
• Intro and Business Challenge 04:14
• General Overview on Regression Metrics 11:06
• Basic Data Imports 04:31
• Visualization and EDA 08:06
• Feature Engineering 03:26
• Model fitting and Evaluation ( Part 1 ) 05:23
• Model fitting and Evaluation ( Part 2 ) 02:06
• Semi-Supervised Learning 15:03
• Conclusion 01:39
• CLASSIFICATION CASE STUDY : Credit Card Fraud Detection
• Intro and Business Challenge 12:51
• General Overview on CLASSIFICATION Metrics 22:06
• Importing Data 07:11
• Feature Engineering and Model prediction 09:03
• Balancing Dataset by Under Sampling 08:40
• Balancing Dataset by Over Sampling 11:33
• Conclusion 02:55
• REGRESSION CASE STUDY : Inventory Prediction
• Intro and Business Challenge 03:20
• General Overview on Regression Metrics 11:06
• Intro and Basic Data Cleaning 09:36
• Feature Engineering and Visualization 16:25
• Feature Engineering and Visualization ( Part 2 ) 10:25
• Model Prediction and Evaluation 07:44
• Conclusion 01:04
• CLASSIFICATION CASE STUDY : Diabetes Prediction
• Intro and Business Challenge 02:50
• General Overview on Classification Metrics 22:06
• Data Import and Some Basic Checks 03:02
• Visualization and EDA 03:30
• Feature Engineering 03:34
• Model Building and Evaluation Process 05:13
• Balancing the Dataset 03:19
• Conclusion 01:14
• REGRESSION CASE STUDY : Caterpillar Tube Assembly Pricing
• Intro and Business Challenge 05:25
• General Overview on Regression Metrics 11:06
• Data import and Feature Engineering 08:15
• Feature Engineering 08:07
• Feature Engineering ( Part 2) 04:05
• Feature Engineering ( Part 3) 06:12
• Model Building and Evaluation 03:03
• Model Building (Part 2) 04:02
• Conclusion 01:38
• CLASSIFICATION CASE STUDY : Breast Cancer Prediction
• Intro and Business Challenge 04:19
• General Overview on CLASSIFICATION Metrics 22:06
• Data Import and Basic Data Clearning 07:09
• Visualization, Feature Scaling and Encoding 10:15
• Model Fitting and checking the Feature Importance 12:56
• Balancing the Dataset and Feature Selection 10:17
• Conclusion 01:12
• REGRESSION CASE STUDY : Coal Production Estimation
• Intro and Business Challenge 02:04
• General Overview on Regression Metrics 11:06
• Data Import and Some Basic Cleaning 04:05
• Visualization and EDA 05:06
• Feature Engineering 13:01
• Model Building and Evaluation 07:37
• Conclusion 01:05
• CLASSIFICATION CASE STUDY : Heart Diseases Prediction
• Intro and Business Challenge 03:10
• General Overview on CLASSIFICATION Metrics 22:06
• Data import and Basic Data Cleaning 04:11
• Visualization and EDA 08:01
• Feature Engineering 03:20
• Model Building and Evaluation 06:33
• Some Bug Fixes 03:28
• Balancing the Dataset and Refitting the Models 04:42
• Conclusion 01:04
• CLASSIFICATION CASE STUDY : Predict whether a Customer Shall Sign a Loan or Not
• Intro and Business Challenge 04:52
• General Overview on CLASSIFICATION Metrics 22:06
• Data Import 04:27
• Basic Feature Engineering and Visualization 07:50
• Feature Engineering ( Part 2 ) 04:12
• Model Prediction and Evaluation 09:11
• Conclusion 00:46
• REGRESSION CASE STUDY : Player Salary Prediction
• Intro and Business Challenge 02:29
• General Overview on Regression Metrics 11:06
• Data Import 01:56
• Feature Engineering and visualization ( Part 1 ) 04:09
• Feature Engineering and visualization ( Part 2 ) 08:00
• Outlier Detection and Removal 03:56
• Feature Scaling 03:47
• Feature Encoding 03:41
• Model Fitting and Evaluation 06:34
• Suggestion to Improve this model 02:25
• Conclusion 01:12
• Conclusion
• Conclusion 00:27
• Video
Acing the Machine Learning Engineering Interview
• Introduction
• Course Introduction 01:30 Preview
• The AI Hierarchy 03:34 Preview
• The Two Types of Interviews 01:36
• The Core Careers in Artificial Intelligence 02:41
• Interview Questions (The AI Hierarchy and Definitions) 02:05
• Machine Learning Concepts
• Section Overview 01:30 Preview
• Five Common Job Themes 03:23 Preview
• Python 01:28
• Basic Data Terminlogy 02:01
• Types of Machine Learning 03:12
• Machine Learning Process 04:35
• Interview Questions (Core Vernacular and the Machine Learning Process) 08:37
• Data Wrangling Process 02:34
• The Array 01:28
• Interview Questions (Imputation and Arrays) 03:12
• Python and the Core Machine Learning Libraries
• Section Overview 01:08
• Interview Questions (Python) 05:41 Preview
• Interview Questions (More Python Questions) 03:27
• Interview Questions - Core Library - Pandas 03:24
• Interview Questions - Core Library - SciKit-Learn 02:58
• Interview Questions - Core Library - NumPy 02:15
• Working With Data
• Section Overview 01:36
• Two Types of Data 02:05
• Databases 02:41
• Table Relationships 03:47
• Manipulating Data 02:44
• Table Joins 03:06
• Statistics in Machine Learning
• Section Introduction 01:35
• Statistics and Machine Learning 03:55
• Interview Questions (Basic Statistics) 02:08
• Measures of Central Tendency 01:29
• Law of Large Numbers 01:00
• Measure of Variability 02:09
• Interview Questions (MOCT,MOV) 02:02
• Outliers and Imputation 06:28
• Interview Questions (Imputation) 00:55
• Modeling
• Section Introduction 01:23 Preview
• Machine Learning Models 02:37
• Common Modeling Problems 01:59
• Classification Metrics 03:59
• Interview Questions (Classification Metrics) 02:34
• Interview Questions (Regression Metrics) 02:54
• Bagging and Boosting 03:56
• What is XGBoost? 02:13
• Interview Questions (Bagging, Boosting and XGBoost) 02:11
• Artificial Neural Networks 03:22
• Interview Questions (ANNs and Deep Learning) 01:25
• eBook
Artificial Neural Network Tutorial
• Artificial Neural Network Tutorial Preview
• eBook
Machine Learning With Python Tutorial
• Machine Learning With Python Tutorial Preview
• Description

Overview: Data Science and Machine Learning Using R, Case Studies of Machine Learning, Data Science and Machine Learning Bootcamp in Python, Deep Learning Models using Keras & Tensorflow, Python's powerful numpy and pandas libraries, Understanding Data Science with real life problems, How to ace machine learning interview, Machine Learning Bootcamp Analysis.

• 9 Modules
• 670+ Lectures
• 65+Hrs HD Videos
• 13+ Projects
• Course Designed by Industry Experts
• Up-to-Date Curriculum
• eBooks
• 30 Days Refund Policy
• Certificate on Completion

Welcome to our 9-course premium pack which will help you learn and master different data processes such as visualization, computing, analysis, and more. You will also learn how to use data across different platforms and languages including Python, Django, Hadoop, R, Bootcamp, Case studies, Projects, Real life problems, Deep learning using Keras & Tensorflow.

Scope of DataScience:

World report 2020 predicts that by 2025, Data Scientists will be in high demand.

On 2021, Data scientists emerged as the #3 job with an annual growth rate of 37 percent.

Data-related technology continuously expands in breadth as data is new fuel.

Projects Overview:

• Retail Stores Sales Prediction
• Restaurant Sales Prediction
• Optimum Inventory Management
• Coal Production Estimation
• Sports Player Salary Prediction
• Credit Card Fraud Detection
• Heart Disease & Breast Cancer Prediction
• Diabetes Prediction for Prevention Care
• Telecom Network Disruptions Prediction
• Sports Player Salary Prediction
• Titanic Project
• And Much More…

Goals

• Master the fundamental mathematical concepts required for Datas Science and Machine Learning
• Learn to implement mathematical concepts using R
• Master Linear algebra, Calculus and Vector calculus from ground up
• Master R programming language
• 12 Real World CaseStudies For Machine Learning
• Get acquainted with Python for Data Science
• Understanding the Data Science Process
• Get a solid understanding of Artificial Neural Networks (ANN) and Deep Learning
• Data Analysis Using NumPy And Pandas
• Learn Artificial Intelligence with Python 2021
• Acing The Machine Learning Engineering Interview
• Develop Cognitive Application on AWS and Microsoft AZURE
• COMPLETE Machine Learning BOOTCAMP

Prerequist

• Basic knowledge of Statistics and Mathematics
• Basics of Machine Learning Process
• Python Programming
• Jupyter Notebook
• Background in computer science or development, it would be beneficial.
• Should have an AWS Account or Microsoft Azure account