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Courses
8
Lessons
449
Enrolled
498
Courses
8
Lessons
449
Enrolled
498
What's Included
Pre-Process & Visualize Data with Tidy Techniques in R
$200 Value
Practical Data Pre-Processing & Visualization Training with Python
$200 Value
Working with Classes: Classify & Cluster Data With Python
$200 Value
Statistics & Machine Learning Techniques for Regression Analysis with Python
$200 Value
Tensorflow & Keras Masterclass for Machine Learning and AI in Python
$200 Value
Complete Data Science Training with Python for Data Analysis
$200 Value
Tensorflow Masterclass for Machine Learning & AI
$200 Value
Machine Learning Terminology & Process For Beginners
$200 Value
Product Details
Access
Lifetime
Content
4 hours
Lessons
39
Pre-Process & Visualize Data with Tidy Techniques in R
Become Highly Proficient in Data Pre-Processing, Wrangling & Visualization Using the 2 Most In-Demand R Data Science Packages
By Minerva Singh | in Online Courses
With 39 lectures, this course will tackle the most fundamental building block of practical data science—data wrangling and visualization. It will take you from a basic level of performing some of the most common data wrangling tasks in R with two of the most important R data science packages, Tidyverse and Dplyr. It will introduce you to some of the most important data visualization concepts and techniques that will suit and apply to your data.
- Read-in data into the R environment from different sources
- Learn how to use some of the most important R data wrangling & visualization packages such as Dpylr and Ggplot2
- Carry out basic data pre-processing & wrangling in R studio
- Gain proficiency in data pre-processing, wrangling & data visualization in R
Instructor
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- Ability to install R & RStudio on your PC/laptop
Course Outline
- Welcome To The Course
- Introduction to the Course - 2:16
- Data & Scripts
- Install R and RStudio - 6:36
- Common Data Types We Encounter in Data Analysis - 3:37
- Read in Data From Different Sources
- Read in CSV and Excel Data - 9:56
- Read in Data from Online HTML Tables-Part 1 - 4:13
- Read in Data from Online HTML Tables-Part 2 - 6:24
- Read in Data from Databases - 8:23
- Read in Data from JSON - 5:28
- Data Processing With dplyr
- Introduction to Pipe Operators - 9:14
- Get acquainted with our data using "dplyr" - 8:29
- More selections with dplyr - 12:28
- Row filtering - 7:05
- More row filtering - 4:59
- Select desired rows and columns - 4:03
- Add new variables/columns - 10:02
- Making sense of data by grouping different categories - 5:28
- Grouping Data-Part 2 - 8:55
- Introduction to dplyr-1 - 6:11
- Introduction to dplyr-2 - 4:44
- Process your Data the Tidy Way: Start With tidyverse
- Getting Started With the tidyverse Package - 3:17
- Rename Columns - 6:59
- Long and Wide Format - 5:03
- Joining Tables - 5:58
- Nesting - 3:59
- Theory Behind Hypothesis Testing - 5:42
- Implement t-test With tidyverse - 3:44
- Dealing With Missing Values
- Removing NAs- the ordinary way - 17:12
- Remove NAs- using "dplyr" - 5:15
- Data imputation with dplyr - 4:44
- More data imputation - 3:53
- Data Visualisation and Explorations
- What is Data Visualisation? - 9:33
- Some Principles of Data Visualisation - 6:46
- Data Visualisation With dplyr and ggplot2 - 6:07
- Mining and Visualising Information About the Olympic Games - 12:49
- Of Winter and Summer Olympic Games - 4:16
- Of Men and Women - 8:26
- Theory of Ordinary Least Square (OLS) Regression - 10:44
- Implement OLS on Different Categories - 7:57
Access
Lifetime
Content
5 hours
Lessons
49
Practical Data Pre-Processing & Visualization Training with Python
Learn to Pre-Process, Wrangle & Visualize Data for Practical Data Science Applications in Python
By Minerva Singh | in Online Courses
This 5-hour course is created to take you by hand and teach you how to tackle the most fundamental building blocks of practical data science: data wrangling and visualization. It will equip you to use some of the most important Python data wrangling and visualization packages such as Seaborn. You will also be able to decide which wrangling and visualization techniques are best suited to answer your research questions and applicable to your data and interpret the results.
- Access 49 lectures & 5 hours of content 24/7
- Understand the most fundamental building blocks of practical data science
- Be equipped w/ some of the most important Python data wrangling & visualization packages
- Implement different techniques on real-life data
- Learn a new concept or technique after each video
Instructor
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- PC or Mac
- Internet access required
Course Outline
- INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
- Welcome to the Course - 2:01
- Data & Script For the Course
- Python Data Science Environment - 10:57
- For Mac Users - 4:05
- Introduction to IPython/Jupyter - 19:13
- Read in Data From Different Sources With Pandas
- What are Pandas? - 12:06
- Read CSV - 5:42
- Read Excel - 5:31
- Data Clean
- Remove NA Values - 10:28
- Missing Values in a Real Dataset - 6:04
- Data Imputation - 9:07
- Imputing Qualitative Value - 3:27
- Theory Behind k-NN Algorithm
- Use k-NN for Data Imputation - 6:23
- Basic Data Wrangling
- Basic Principles - 4:20
- Preliminary Data Explorations - 8:17
- Basic Data Handling With Conditional Statements - 5:24
- Drop Column/Row - 4:42
- Change Column Name - 3:35
- Change the Column Type - 3:50
- Explore Date Related Data - 4:02
- Simple Date Related Computations - 3:46
- More Data Wrangling
- Data Grouping - 9:47
- Data Subsetting and Indexing - 9:44
- More Data Subsetting - 8:54
- Extract Information From Strings - 4:40
- (Fuzzy) String Matching - 2:39
- Ranking & Sorting - 8:03
- Concatenate - 8:16
- Merging and Joining - 10:47
- Feature Selection and Transformation
- Correlation Analysis - 8:26
- Using Correlation to Decide Which Features to Retain - 5:00
- Univariate Feature Selection - 4:56
- Recursive Feature Elimination (RFE) - 4:26
- Theory Behind PCA - 2:37
- Implement PCA - 3:53
- Data Standardisation - 4:10
- Create a New Feature - 6:16
- Theory Behind Data Visualisation
- What is Data Visualisation? - 9:33
- Some Theoretical Principles Behind Data Visualization - 6:46
- Most Common Data Visualisations
- Histograms- Visualize the Distribution of Continuous Numerical Variables - 12:13
- Boxplot- Visualise Data Distribution - 5:54
- Scatter Plot: Relationship Between Variables - 11:57
- Barplot - 22:25
- Pie Chart - 5:29
- Line Chart - 12:31
- More Line Charts - 2:32
- Some More Plot Types - 11:14
- And Some More - 8:40
Access
Lifetime
Content
4 hours
Lessons
46
Working with Classes: Classify & Cluster Data With Python
Harness The Power of Machine Learning For Unsupervised & Supervised Learning In Python
By Minerva Singh | in Online Courses
In this course, you’ll start by absorbing the most valuable Python Data Science basics and techniques. You'll get up to speed with packages like Numpy, Pandas, and Matplotlib and work with real data in Python. You'll even delve into concepts like unsupervised learning, dimension reduction, and supervised learning.
- Access 46 lectures & 4 hours of content 24/7
- Harness the power of Anaconda/iPython for practical data science
- Carry out basic data pre-processing & wrangling in Python
- Implement dimensional reduction techniques (PCA) & feature selection
- Explore neural network & deep learning-based classification
Instructor
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- PC or Mac
- Internet access required
Course Outline
- Introduction to the Course
- What is Data Science? - 3:37
- Data and Scripts for the Course
- Introduction to the Python Data Science Tool - 10:57
- For Mac Users - 4:05
- Introduction to the Python Data Science Environment - 19:15
- Some Miscellaneous IPython Usage Facts - 5:25
- Online iPython Interpreter - 3:26
- Introduction to Pandas
- What are Pandas? - 12:06
- Read CSV Data in Python - 5:42
- Read in Excel File - 5:31
- Read HTML Data - 12:06
- Read JSON Data - 9:14
- Data Pre-Processing/Wrangling
- Remove NA Values - 10:28
- Basic Data Handling: Starting with Conditional Data Selection - 5:24
- Subset and Index Data - 9:44
- Basic Data Grouping Based on Qualitative Attributes - 9:47
- Rank and Sort Data - 8:03
- Concatenate - 8:16
- Merge - 10:47
- Unsupervised Learning: Clustering and Dimensionality Reduction
- Some Basic Pointers - 1:38
- kmeans-theory - 2:31
- KMeans-implementation on the iris data
- Quantifying KMeans Clustering Performance - 3:53
- kmeans clustering on real data - 4:16
- How Do We Select the Number of Clusters? - 5:38
- Theory of hierarchical clustering - 4:10
- Implement hierarchical clustering - 9:19
- Theory of Principal Component Analysis (PCA) - 2:37
- Implement PCA - 3:52
- Supervised Learning
- What is this section about? - 10:10
- Logistic regression with classification - 8:26
- Random Forest (RF) For Classification - 12:02
- Linear Support Vector Machine (SVM) Classification - 3:10
- Non-Linear Support Vector Machine (SVM) Classification - 2:06
- kNN Classification - 7:46
- kNN Regression - 3:48
- Gradient Boosting Machine (GBM) Classification - 5:54
- GBM Classification
- Voting Classifier - 4:00
- Artificial Neural Networks (ANN) and Deep Learning
- Introduction
- Perceptrons for Binary Classification - 4:27
- Getting Started with ANN-binary classification - 3:26
- Multi-label classification with MLP - 4:53
- Start with H20 - 4:14
- Specify the Activation Function - 2:06
- Deep Learning Predictions - 5:02
Access
Lifetime
Content
6 hours
Lessons
50
Statistics & Machine Learning Techniques for Regression Analysis with Python
Learn Regression Analysis for Practical Statistical Modeling & Machine Learning In Python
By Minerva Singh | in Online Courses
This course offers a complete guide to practical data science using Python. You'll cover all aspects of practical data science in Python. By storing, filtering, managing, and manipulating data in Python, you can give your company a competitive edge and boost your career to the next level.
- Access 50 lectures & 6 hours of content 24/7
- Get a full introduction to Python Data Science & Anaconda
- Cover basic analysis tools like Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors & Broadcasting
- Explore data structures & reading in Pandas, including CSV, Excel, JSON, and HTML data
- Pre-process & wrangle your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
- Create data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts & more
Instructor
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- PC or Mac
- Internet access required
Course Outline
- Introduction to the Data Science in Python Bootcamp
- Welcome to the Course - 1:40
- Data and Scripts for the Course
- Introduction to the Python Data Science Tool - 10:57
- For Mac Users - 4:05
- Introduction to the Python Data Science Environment - 19:15
- Some Miscellaneous IPython Usage Facts - 5:25
- Online iPython Interpreter - 3:26
- Conclusion to Section 1 - 2:36
- Introduction to Pandas
- What are Pandas? - 12:06
- Read CSV Data in Python - 5:42
- Read in Excel File - 5:31
- Read HTML Data - 12:06
- Read JSON Data - 9:14
- Conclusions to Section 4 - 2:06
- Data Pre-Processing/Wrangling
- Remove NA Values - 10:28
- Basic Data Handling: Starting with Conditional Data Selection - 5:24
- Basic Data Grouping Based on Qualitative Attributes - 9:47
- Rank and Sort Data - 8:03
- Concatenate - 8:16
- Merge - 10:47
- Basic Statistical Data Analysis
- What is Statistical Data Analysis? - 10:08
- Some Pointers on Collecting Data for Statistical Studies - 8:38
- Explore the Quantitative Data: Descriptive Statistics - 9:05
- Group By Qualitative Categories - 10:25
- Visualize Descriptive Statistics-Boxplots - 5:28
- Common Terms Relating to Descriptive Statistics - 5:15
- Data Distribution- Normal Distribution - 4:07
- Check for Normal Distribution - 6:23
- Standard Normal Distribution and Z-scores - 4:10
- Confidence Interval-Theory - 6:06
- Confidence Interval-Calculation - 5:20
- Regression Modelling for Defining Relationship bw Variables
- Explore the Relationship Between Two Quantitative Variables - 4:26
- Correlation Analysis - 8:26
- Linear Regression-Theory - 10:44
- Linear Regression-Implementation in Python - 11:18
- Conditions of Linear Regression-Check in Python - 12:03
- Polynomial Regression - 3:53
- GLM: Generalized Linear Model - 5:25
- Logistic Regression - 11:10
- Machine Learning for Data Science
- How is Machine Learning Different from Statistical Data Analysis? - 5:36
- What is Machine Learning (ML) About? Some Theoretical Pointers - 5:32
- Machine Learning Based Regression Modelling
- What is this section about - 10:10
- Data Preparation for Supervised Learning - 9:47
- Pointers on Evaluating the Accuracy of Classification and Regression Modelling - 9:42
- Random Forest (RF) Regression - 9:20
- Support Vector Regression - 4:30
- kNN Regression - 3:48
- Gradient Boosting-regression - 4:46
- Theory Behind ANN and DNN - 9:17
- Regression with MLP - 3:48
Access
Lifetime
Content
5 hours
Lessons
61
Tensorflow & Keras Masterclass for Machine Learning and AI in Python
Master the Most Important Deep Learning Frameworks for Python Data Science
By Minerva Singh | in Online Courses
This course is your complete guide to the practical machine and deep learning using the Tensorflow and Keras frameworks in Python. In the age of Big Data, companies across the globe use Python to sift through the avalanche of information at their disposal and the advent of Tensorflow and Keras is revolutionizing deep learning. This course will help you break into this booming field.
- Access 61 lectures & 5 hours of content 24/7
- Get a full introduction to Python Data Science
- Get started w/ Jupyter notebooks for implementing data science techniques in Python
- Learn about Tensorflow & Keras installation
- Understand the workings of Pandas & Numpy
- Cover the basics of the Tensorflow syntax & graphing environment and Keras syntax
- Discover how to create artificial neural networks & deep learning structures w/ Tensorflow & Keras
Instructor
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- PC or Mac
- Internet access required
Course Outline
- Introduction to the Course
- Tensorflow and Keras For Data Science - 2:12
- Data and Code
- Python Data Science Environment - 10:57
- For Mac Users - 4:05
- Install Tensorflow - 15:12
- Written Instructions for Tensorflow Install
- Install Keras on Windows 10 - 5:16
- Install Keras with Mac - 4:19
- Written Keras Installation Instructions
- Introduction to Python Data Science Packages
- Python Packages For Data Science - 3:16
- Introduction to Numpy - 3:46
- Create Numpy - 10:51
- Numpy for Statistical Operations - 7:23
- Introduction to Pandas - 12:06
- Read in CSV - 7:13
- Read in Excel - 5:31
- Basic Data Cleaning - 4:30
- Introduction to Tensorflow
- A Brief Touchdown - 2:36
- A Brief Touchdown: Computational Graphs - 2:56
- Common Mathematical Operator
- A Tensorflow Session - 4:37
- Interactive Tensorflow Session - 1:38
- Constants and Variables in Tensorflow - 3:42
- Placeholders in Tensorflow - 3:58
- Introduction to Keras
- What is Keras? - 3:29
- Some Preliminary Tensorflow and Keras Applications
- Theory of Linear Regression (OLS) - 10:44
- OLS From First Principles - 9:22
- Visualize the Results of OLS - 3:28
- Multiple Regression With Tensorflow-Part 1 - 5:08
- Estimate With Tensorflow Estimators - 3:05
- Multiple Regression With Tensorflow Estimators - 5:24
- More on Linear Regressor Estimator - 8:24
- GLM: Generalized Linear Model - 5:25
- Linear Classifier For Binary Classification - 9:33
- Accuracy Assessment For Binary Classification - 4:19
- Linear Classification with Binary Classification With Mixed Predictors - 8:15
- Softmax Classification With Tensorflow - 7:35
- Some Basic Concepts
- What is Machine Learning? - 5:32
- Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks) - 9:17
- Unsupervised Learning With Tensorflow and Keras
- What is Unsupervised Learning? - 5:32
- Autoencoders for Unsupervised Classification - 1:46
- Autoencoders in Tensorflow (Binary Class Problem) - 7:32
- Autoencoders in Tensorflow (Multiple Classes) - 5:43
- Autoencoders in Keras (Simple) - 5:43
- Autoencoders in Keras (Sparsity Constraints) - 4:32
- Neural Network for Tensorflow & Keras
- Multi Layer Perceptron (MLP) with Tensorflow - 6:24
- Multi Layer Perceptron (MLP) With Keras - 3:31
- Keras MLP For Binary Classification - 4:01
- Keras MLP for Multiclass Classification - 6:01
- Keras MLP for Regression - 3:27
- Deep Learning For Tensorflow & Keras
- Deep Neural Network (DNN) Classifier With Tensorflow - 6:47
- Deep Neural Network (DNN) Classifier With Mixed Predictors - 8:11
- Deep Neural Network (DNN) Regression With Tensorflow - 5:24
- Wide & Deep Learning (Tensorflow) - 11:34
- DNN Classifier With Keras - 3:30
- DNN Classifier With Keras-Example 2 - 4:23
- Autoencoders with Convolution Neural Networks (CNN)
- Autoencoders With CNN-Tensorflow - 7:15
- Autoencoders With CNN- Keras - 4:46
- Recurrent Neural Network (RNN)
- Introduction to RNN - 5:40
- LSTM for Time Series - 6:24
- LSTM for Stock Prices - 7:21
Access
Lifetime
Content
12 hours
Lessons
116
Complete Data Science Training with Python for Data Analysis
Learn Statistics, Visualization, Machine Learning & More
By Minerva Singh | in Online Courses
In this easy-to-understand, hands-on course, you'll learn the most valuable Python Data Science basics and techniques. You'll discover how to implement these methods using real data obtained from different sources and get familiar with packages like Numpy, Pandas, Matplotlib, and more. You'll even understand deep concepts like statistical modeling in Python's Statsmodels package and the difference between statistics and machine learning.
- Access 116 lectures & 12 hours of content 24/7
- Get a full introduction to Python Data Science & Anaconda
- Cover basic analysis tools like Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors & Broadcasting
- Explore data structures & reading in Pandas, including CSV, Excel, JSON, and HTML data
- Pre-process & wrangle your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
- Create data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts & more
- Discover how to create artificial neural networks & deep learning structures
Instructor
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- PC or Mac
- Internet access required
Course Outline
- Introduction to the Data Science in Python Bootcamp
- What is Data Science? - 3:37
- Introduction to the Course & Instructor - 11:34
- Data and Scripts for the Course
- Introduction to the Python Data Science Tool - 10:57
- For Mac Users - 4:05
- Introduction to the Python Data Science Environment - 19:15
- Some Miscellaneous IPython Usage Facts - 5:25
- Online iPython Interpreter - 3:26
- Conclusion to Section 1 - 2:36
- Introduction to Python Pre-Requisites for Data Science
- Different Types of Data Used in Statistical & ML Analysis - 3:37
- Different Types of Data Used Programatically - 3:46
- Python Data Science Packages To Be Used - 3:16
- Conclusion to Section 2 - 1:59
- Introduction to Numpy
- Numpy: Introduction - 3:46
- Create Numpy Arrays - 10:51
- Numpy Operations - 16:48
- Matrix Arithmetic and Linear Systems - 7:34
- Numpy for Basic Vector Arithmetic - 6:16
- Numpy for Basic Matrix Arithmetic - 5:16
- Broadcasting for Numpy - 3:52
- Solve for Equations - 5:04
- Numpy For Statistics - 7:23
- Conclusions to Section 3 - 2:24
- Introduction to Pandas
- What are Pandas? - 12:06
- Read CSV Data in Python - 5:42
- Read in Excel File - 5:31
- Read HTML Data - 12:06
- Read JSON Data - 3:09
- Conclusions to Section 4 - 2:06
- Data Pre-Processing/Wrangling
- Rationale behind this section - 4:19
- Remove NA Values - 10:28
- Basic Data Handling: Starting with Conditional Data Selection - 5:24
- Drop Column/Row - 4:42
- Subset and Index Data - 9:44
- Basic Data Grouping Based on Qualitative Attributes - 9:47
- Crosstabulation - 4:54
- Reshaping - 9:26
- Pivotting - 8:30
- Rank and Sort Data - 8:03
- Concatenate - 8:16
- Merge - 10:47
- Conclusion to Section 5
- Introduction to Data Visualization
- What is Data Visualisation? - 9:33
- Theory Behind Data Visualisation - 6:46
- Histograms- Visualise the Distribution of Quantitative Variables - 12:13
- Boxplot- Visualise the Data Summary - 5:54
- Scatterplot- Visualise The Relationship Between Quantitative Variables - 11:57
- Line Chart - 12:31
- Barplot - 22:25
- Pie Chart - 5:29
- Conclusion to Section 6 - 2:14
- Basic Statistical Data Analysis
- What is Statistical Data Analysis? - 10:08
- Some Pointers on Collecting Data for Statistical Studies - 8:38
- Explore the Quantitative Data: Descriptive Statistics - 9:05
- Group By Qualitative Categories - 10:25
- Visualize Descriptive Statistics-Boxplots - 5:28
- Common Terms Relating to Descriptive Statistics - 5:15
- Data Distribution- Normal Distribution - 4:07
- Check for Normal Distribution - 6:23
- Standard Normal Distribution and Z-scores - 4:10
- Confidence Interval-Theory - 6:06
- Confidence Interval-Calculation - 5:20
- Conclusion to Section 7 - 1:28
- Statistical Inference & Relationship Between Variables
- What is Hypothesis Testing? - 5:42
- Test the Difference Between Two Groups - 7:30
- Test the Difference Between More Than Two Groups - 10:55
- Explore the Relationship Between Two Quantitative Variables - 4:26
- Correlation Analysis - 8:26
- Linear Regression-Theory - 10:44
- Linear Regression-Implementation in Python - 11:18
- Conditions of Linear Regression-Check in Python - 12:03
- Polynomial Regression - 3:53
- GLM: Generalized Linear Model - 5:25
- Logistic Regression - 11:10
- Conclusion to Section 8 - 1:52
- Machine Learning for Data Science
- How is Machine Learning Different from Statistical Data Analysis? - 11:12
- What is Machine Learning (ML) About? Some Theoretical Pointers - 5:32
- Unsupervised Learning
- Some Basic Pointers - 1:38
- kmeans-theory - 2:31
- KMeans-implementation on the iris data - 8:01
- Quantifying KMeans Clustering Performance - 3:53
- kmeans clustering on real data - 4:16
- How Do We Select the Number of Clusters? - 5:38
- Theory of hierarchical clustering - 4:10
- Implement hierarchical clustering - 9:19
- Theory of Principal Component Analysis (PCA) - 2:37
- Implement PCA - 3:52
- Conclusion to Section 10 - 2:08
- Data Preparation for Supervised Classification - 9:47
- Classification accuracy evaluation - 9:42
- Random Forest (RF) For Regression - 9:20
- Supervised Learning
- What is this section about? - 10:10
- Logistic regression with classification - 8:26
- Random Forest (RF) For Classification - 12:02
- Linear Support Vector Machine (SVM) Classification - 3:10
- Non-Linear Support Vector Machine (SVM) Classification - 2:06
- Support Vector Regression - 4:30
- kNN Classification - 7:46
- kNN Regression - 3:48
- Gradient Boosting Machine (GBM) Classification - 5:54
- GBM Classification
- Gradient Boosting Regression (GBR) - 4:46
- Voting Classifier - 4:00
- Conclusion to Section 11 - 2:46
- Artificial Neural Networks (ANN) and Deep Learning
- Introduction
- Perceptrons for Binary Classification - 4:27
- Getting Started with ANN-binary classification - 3:26
- Multi-label classification with MLP - 4:53
- Regression with MLP - 3:48
- MLP with PCA on a Large Dataset - 7:33
- Start With Deep Neural Network (DNN)
- Start with H20 - 4:14
- Default H2O Deep Learning Algorithm - 3:20
- Specify the Activation Function - 2:06
- Deep Learning Predictions - 5:02
- Conclusion to section 12 - 2:03
Access
Lifetime
Content
5 hours
Lessons
62
Tensorflow Masterclass for Machine Learning & AI
Complete Tensorflow Mastery For Machine Learning & Deep Learning in Python
By Minerva Singh | in Online Courses
This course is your complete guide to practical data science using the Tensorflow framework in Python. Here, you'll cover all the aspects of practical data science with Tensorflow, Google's powerful deep learning framework used by organizations everywhere.
- Access 62 lectures & 5 hours of content 24/7
- Get a full introduction to Python Data Science
- Get started w/ Jupyter notebooks for implementing data science techniques in Python
- Learn about Tensorflow installation & other Python data science packages
- Understand the workings of Pandas & Numpy
- Cover the basics of the Tensorflow syntax & graphing environment
- Learn statistical modeling w/ Tensorflow
- Discover how to create artificial neural networks & deep learning structures w/ Tensorflow
Instructor
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- PC or Mac
- Internet access required
Course Outline
- Introduction to the Course
- Welcome to the World of TensorFlow - 4:03
- Data and Code
- Anaconda:Python Data Science Environment
- Anaconda Installation For Mac Users
- Install Tensorflow - 15:12
- Written Instructions for Tensorflow Install
- Introduction to Python Data Science Packages
- Commonly Used Python Data Science Packages - 3:16
- What is Numpy? - 3:46
- Create Numpy - 10:51
- Numpy for Statistical Operations - 7:23
- Introduction to Pandas - 12:06
- Read in CSV - 7:13
- Read in Excel - 5:31
- Basic Data Preprocessing - 4:30
- Introduction to Tensorflow
- Start With Tensorflow - 2:36
- Start With Tensorflow Computational Graphs - 2:56
- Common Mathematical Operations
- A Brief Tensorflow Session - 4:37
- Interactive Tensorflow Session - 1:38
- Constants and Variables in Tensorflow - 3:42
- Placeholders in Tensorflow
- TensorBoard: Visualize Graphs in TensorFlow - 2:44
- Access TensorBoard Graphs - 2:55
- Some Preliminary Tensorflow and Keras Applications
- Ordinary Least Squares Linear Regression (OLS): Theory - 10:44
- OLS From First Principles - 9:22
- Visualize the Results of OLS - 3:28
- OLS With Multiple Predictors With Tensorflow-Part 1 - 5:08
- Estimate With Tensorflow Estimators - 3:05
- Multiple Regression With Tensorflow Estimators - 5:24
- More on Linear Regressor Estimator - 8:24
- GLM: Generalized Linear Model - 5:25
- Linear Classifier For Binary Classification - 9:33
- Accuracy Assessment For Binary Classification - 4:19
- Linear Classification with Binary Classification With Mixed Predictors - 8:15
- Some Basic Concepts
- Machine Learning: Theory
- What Are ANN (Artificial Neural Network) and DNN (Deep Neural Networks)? - 9:17
- Unsupervised and Supervised Learning With Tensorflow
- What is Unsupervised Learning? - 5:32
- K-means Clustering: Theory - 5:44
- Implement K-Means on Real Data - 5:37
- Softmax Classification - 7:35
- Random Forests (RF) Theory - 7:14
- Random Forest (RF) for Binary Classification - 7:09
- Random Forest (RF) for Multiclass Classification - 5:07
- kNN Theory
- Implement kNN - 3:22
- Neural Network for Tensorflow & Keras
- Multi Layer Perceptron (MLP) with Tensorflow - 6:24
- Multi Layer Perceptron (MLP) With Keras - 3:31
- Keras MLP For Binary Classification - 4:01
- Keras MLP for Multiclass Classification - 6:01
- Keras MLP for Regression - 3:27
- Deep Learning For Tensorflow & Keras
- Deep Neural Network (DNN) Classifier With Tensorflow - 6:47
- Deep Neural Network (DNN) Classifier With Mixed Predictors - 8:11
- Deep Neural Network (DNN) Regression With Tensorflow - 5:24
- New Lecture
- Wide & Deep Learning (Tensorflow) - 11:34
- DNN Classifier With Keras - 3:30
- DNN Classifier With Keras-Example 2 - 4:23
- Autoencoders with Convolution Neural Networks (CNN)
- Autoencoders With CNN-Tensorflow - 7:15
- Autoencoders With CNN- Keras - 4:46
- Recurrent Neural Network (RNN)
- Introduction to RNN - 5:40
- LSTM for Time Series
- LSTM for Stock Prices - 7:21
Access
Lifetime
Content
3 hours
Lessons
26
Machine Learning Terminology & Process For Beginners
Use Hands-On Labs to Learn Machine Learning Terminology & Processes
By Syed Raza | in Online Courses
This course will help you learn machine learning terminology and processes with up-to-date knowledge. In this course, you'll learn and practice framing machine learning problems, data sets, data visualizations, evaluation, and more. You will also get complete resources and applicable codes in this course.
- Access 26 lectures & 3 hours of content 24/7
- Understand basic machine learning terminology & process
- Learn how to frame a machine learning problem & when to use machine learning
- Prepare & develop data sets
Instructor
Syed Raza has numerous technical IT and developer certifications (MCSE+I, MCT, CCNA—including a Ph.D. Management—which enable him to teach a variety of powerful courses, from IT to Project Management. He has been providing technical and training solutions using Microsoft Server 2016, 2019 Beta, Azure, Python, Java, JavaScript, React JS, GCP, Kubernetes, Docker. He has a working knowledge of TensorFlow, Pytorch, Keras, Convolutional networks, and data science concepts.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: beginner
Requirements
- PC or Mac
- Internet access required
Course Outline
- First Section
- Course Promo & Instructor Bio` - 3:43
- Introduction - Course Agenda - 5:38
- What Will I Learn? - 4:50
- When Do You Use Machine Learning - 4:34
- Your feedback: Updates - Let us know! - 2:27
- AWS Machine Learning Stack & AI - 12:34
- Machine Learning Problem Framing
- Business Vs ML Problem - 9:44
- Types of Machine Learning - 9:58
- Machine Learning Algorithms - 15:36
- Working With DataSets
- Different Types of Data - 4:44
- Data Collection & Integration - 6:54
- Data Cleaning - 5:50
- Data Splitting & Shuffling - 7:35
- Data Visualization & Feature Engineering
- What Are Features - 4:03
- Visualization Types - 5:18
- What Is Feature Engineering? - 6:12
- Numeric Value Binning - 7:01
- Model Training & Evaluation
- Parameter Tuning - 5:18
- Overfitting Vs Underfitting - 7:25
- Business Goal Evaluation
- Goal Evaluation - 4:22
- Augmenting Your Data - 5:38
- Predictions - 5:05
- Hands-On Machine Learning Using AWS Rekognition & Python
- Complete ML Run through Project - 18:29
- Banking Data Set Upload - 8:47
- Create & Training Data Model - 10:33
- Creating Predictions - 8:01
Terms
- Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.