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The Ultimate Python & Artificial Intelligence Certification Bundle

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Content
3.0 hours
Lessons
27

Artificial Intelligence (AI) in Python: A H2O Approach

Master Powerful Python Package for Machine Learning, Artificial Neural Networks (ANN) & Deep Learning

By Minerva Singh | in Online Courses

This course covers the main aspects of the H2O package for data science in Python. If you take this course, you can do away with taking other courses or buying books on Python-based data science as you will have the keys to a very powerful Python supported data science framework. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in machine learning, neural networks, and deep learning via a powerful framework, H2O in Python, you can give your company a competitive edge and boost your career to the next level!

4.6/5 average rating: ★ ★ ★ ★

  • Access 27 lectures & 3 hours of content 24/7
  • Use the Python/Anaconda environment for practical data science
  • Learn the important concepts associated with supervised & unsupervised learning
  • Implement supervised & unsupervised learning on real-life data
  • Implement Artificial Neural Networks (ANN) & Deep Neural Networks (DNN) on real-life data
Minerva Singh | Bestselling Instructor & Data Scientist
4.3/5 Instructor Rating: ★ ★ ★ ★

Minerva Singh is a Ph.D. 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: desktop & mobile
  • Certificate of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Prior exposure to common machine learning terms
  • Prior exposure to what neural networks are

Course Outline

  • Your First Program
  • Welcome To The World of Python AI
    • What is AI? - 9:51
    • Introduction to Python - 10:57
    • The IPython Ecosystem - 19:13
  • Introduction to Pandas
    • Read CSV - 5:42
    • Read Excel - 5:31
    • Read HTML - 5:58
  • Introduction to H2O
    • More H2O Installation - 2:11
    • Getting Used To the H2O Framework - 2:12
  • What is Machine Learning (ML)?
    • Theory Behind ML - 5:32
  • Supervised Learning
    • Set up GLMs - 11:35
    • glm2 - 9:48
    • glm grid - 10:03
    • rf2 - 7:49
    • gbm - 11:11
    • Search or GBM Parameters - 7:02
    • XGB Theory - 2:02
    • xgboost binary - 5:15
    • xgboost multiple - 5:12
    • Search For the Best H2O Model - 5:20
  • Unsupervised Learning
    • Principal Component Analysis (PCA) Theory - 2:37
    • PCA - 6:14
    • k-means theory - 1:57
    • k-means - 11:08
  • Neural Networks
    • What are Activation Functions? - 5:50
    • Implement Deep Learning for Binary Classification - 8:01
    • Theory Behind Autoencoders - 1:46
    • Set up Autoencoder - 4:06

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6.0 hours
Lessons
52

Master PyTorch for Artificial Neural Networks (ANN) & Deep Learning

Get Introduced to Deep Neural Networks & Become a Pro in Practical PyTorch-Based Data Science

By Minerva Singh | in Online Courses

This is a complete neural network and deep learning training with PyTorch in Python. It's a full 6-hour PyTorch Bootcamp that will help you learn basic machine learning, how to build neural networks and explore deep learning using one of the most important Python Deep Learning frameworks. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and the advent of frameworks such as PyTorch is revolutionizing deep learning. By gaining proficiency in PyTorch, you can give your company a competitive edge and boost your career to the next level.

4.5/5 average rating: ★ ★ ★ ★

  • Access 52 lectures & 6 hours of content 24/7
  • Learn implement deep learning models with PyTorch
  • Implement PyTorch based deep learning algorithms on imagery data
  • Configure the Anaconda Environment for getting started with PyTorch
  • Implement common machine learning algorithms for Image Classification
Minerva Singh | Bestselling Instructor & Data Scientist
4.3/5 Instructor Rating: ★ ★ ★ ★

Minerva Singh is a Ph.D. 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: desktop & mobile
  • Certificate of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Any device with basic specifications

Course Outline

  • Introduction to the Course
    • Welcome to the Course - 2:32
    • Data and Code
    • Get Started With the Python Data Science Environment: Anaconda - 10:57
    • Anaconda for Mac Users - 4:05
    • The iPython Environment - 19:13
    • Why PyTorch? - 9:42
    • Install PyTorch - 3:36
    • Installing PyTorch-Written Instructions
    • Further Installation Instructions for Mac - 1:19
    • Working With CoLabs - 7:13
  • Non PyTorch Python Data Science Packages
    • Python Packages for Data Science - 3:16
    • Introduction to Numpy - 3:46
    • Create Numpy Arrays - 10:51
    • Numpy Operations - 16:48
    • Numpy for Basic Vector Arithmetric - 6:16
    • Numpy for Basic Matrix Arithmetic - 6:32
    • PyTorch Basics: What Is a Tensor? - 2:36
    • Explore PyTorch Tensors and Numpy Arrays - 4:26
    • Some Basic PyTorch Tensor Operations - 3:40
  • Other Python Data Science Packages For Dealing With Data
    • Read CSV - 5:42
    • Read Excel - 5:31
    • Basic Data Exploration With Pandas - 11:20
  • Basic Statistical Analysis With PyTorch
    • Ordinary Least Squares (OLS) Regression- Theory - 10:44
    • OLS Linear Regression-Without PyTorch - 11:18
    • OLS Linear Regression From First Principles-Theory - 12:48
    • OLS Linear Regression From First Principles-Without PyTorch - 9:22
    • OLS Linear Regression From First Principles-With PyTorch - 4:33
    • More OLS With PyTorch - 11:23
    • Generalised Linear Models (GLMs)-Theory - 5:25
    • Logistic Regression-Without PyTorch - 5:06
    • Logistic Regression-With PyTorch - 4:52
  • Introduction to Artificial Neural Networks (ANN)
    • Introduction to ANN - 9:17
    • PyTorch ANN Syntax - 5:24
    • What Are Activation Functions? Theory - 5:50
    • More on Backpropagation - 10:20
    • Bringing Them Together - 14:46
    • Setting Up ANN Analysis With PyTorch - 6:21
    • DNN Analysis with PyTorch - 11:26
    • More DNNs - 8:43
    • DNNs For Identifying Credit Card Fraud - 9:40
    • An Explanation of Accuracy Metrics - 4:19
  • Neural Networks on Images
    • What Are Images? - 4:54
    • Read in Images in Python - 7:46
    • Basic Image Conversions - 3:07
    • Why AI and Deep Learning? - 9:51
    • Artificial Neural Networks (ANN) For Image Classification - 10:50
    • Deep Neural Networks (DNN) For Image Classification - 5:27
  • Introduction to Artificial Intelligence (AI) and Deep Learning
    • What is CNN? - 11:25
    • Implement CNN on Imagery Data - 7:33
    • More on CNN - 4:36
    • Introduction to Transfer Learning: Theory - 7:41
    • Implement CNN Using a Pre-Trained Model - 7:25

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5.0 hours
Lessons
61

Image Processing & Analysis Bootcamp with OpenCV and Deep Learning in Python

Implement Both Machine Learning & Deep Learning Techniques in a Hands-On Manner

By Minerva Singh | in Online Courses

This course is your complete guide to practical image processing and computer vision tasks using Python. It covers the important aspects of Keras and Tensorflow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow and Keras based data science. In this 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. By gaining proficiency in Keras and Tensorflow, you can give your company a competitive edge and boost your career to the next level.

4.1/5 average rating: ★ ★ ★ ★

  • Access 61 lectures & 5 hours of content 24/7
  • Get started with the Python data science environment
  • Read in image data into the Jupiter/iPython environment
  • Carry out basic image pre-processing & computer vision tasks with Python
  • Implement Unsupervised Learning Algorithms on image data
Minerva Singh | Bestselling Instructor & Data Scientist
4.3/5 Instructor Rating: ★ ★ ★ ★

Minerva Singh is a Ph.D. 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: desktop & mobile
  • Certificate of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Any device with basic specifications

Course Outline

  • Welcome to Image Processing And Analysis in Python
    • Brief Introduction to the Course - 2:31
    • Data and Code
    • Get Started With the Python Data Science Environment - 10:57
    • For Mac Users - 4:05
    • Introduction to iPython/Jupyter - 19:13
    • Working With Colabs - 7:13
  • Getting Started With Basic Image Processing in Python
    • What Are Images? - 4:54
    • Read in Images in Python - 7:46
    • Some Basic Image Conversions - 3:07
    • Basic Image Resizing - 4:01
    • Basic Image Resizing - 4:01
    • What is Interpolation? A Geographic Perspective - 5:08
    • Basic Image Transformations - 6:08
    • Contrast Stretching - 6:20
    • Filtering Images - 6:21
  • Introduction to Computer Vision
    • What is Computer Vision? - 4:54
    • Read in Images Using OpenCV - 5:59
    • Image Filtering With OpenCV - 7:30
    • Edge Detection With OpenCV - 5:19
    • More Edge Detection: Sobel Method - 3:25
    • Corner Detection - 1:31
    • Face Detection With Haar Features: Theory - 5:42
    • Face Detection - 5:32
  • Introduction to Some Concepts
    • What is Machine Learning? - 5:32
  • Unsupervised Learning Methods
    • What is Unsupervised Learning? - 1:38
    • Theory Behind PCA - 2:37
    • Implement PCA on Images - 4:36
    • PCA For Image reconstruction - 4:14
    • Randomised PCA - 2:45
    • Theory Behind K-means - 1:57
    • K-Means For Image Reconstruction - 1:48
    • Classify High Dimensional Data With t-SNE - 4:55
    • Practical Case Study: Identify Flowers - 3:09
    • Cluster the Flowers: Read in Images - 7:45
    • Implement PCA - 4:04
    • Implement t-SNE - 2:25
  • Supervised Learning: Classifying Images
    • Brief Introduction to Supervised Learning - 10:10
    • Implement SVM to Classify Digits - 7:00
    • Accuracy Assessment - 9:42
    • rf - 4:19
  • Start With Deep Learning
    • Why Deep Learning? - 9:51
    • Tensorflow Installation - 15:12
    • Written Tensorflow Installation Instructions
    • Install Keras on Windows 10 - 5:16
    • Install Keras on Mac - 4:19
    • Written Keras Installation Instructions
  • Deep Learning For Image Classification
    • Introduction to CNN - 11:25
    • Implement a CNN for Multi-Class Supervised Classification - 7:27
    • More on CNN - 4:36
    • Pre-Requisite For Working With Imagery Data - 2:33
    • CNN on Image Data-Part 1 - 10:41
    • CNN on Image Data-Part 2 - 6:38
    • More on TFLearn - 7:54
    • CNN Workflow for Keras - 4:04
    • CNN With Keras - 4:10
    • CNN on Image Data with Keras-Part 1 - 2:27
    • CNN on Image Data with Keras-Part 2 - 5:05
  • Transfer Learning
    • What is Transfer Learning? - 7:41
    • Implement a Pre-Built Transfer Learning Model - 6:57
  • Unsupervised Deep Learning
    • Simple Autoencoders - 5:43
    • Add Sparsity Constraint - 4:32

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Lessons
35

Keras Bootcamp for Deep Learning & AI in Python

Master Keras: An Important Deep Learning Framework for Deep Learning & Artificial Intelligence

By Minerva Singh | in Online Courses

This is a full 3-hour Python Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks, and deep learning using one of the most important Deep Learning frameworks—Keras. This course is your complete guide to the practical machine and deep learning using the Keras framework in Python. This means, this course covers the important aspects of Keras (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Keras based data science.

4.5/5 average rating: ★ ★ ★ ★

  • Access 35 lectures & 3 hours of content 24/7
  • Get started with Jupyter notebooks for implementing data science techniques
  • Understand the basics of Keras syntax
  • Create artificial neural networks & deep learning structures with Keras
Minerva Singh | Bestselling Instructor & Data Scientist
4.3/5 Instructor Rating: ★ ★ ★ ★

Minerva Singh is a Ph.D. 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: desktop & mobile
  • Certificate of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Any device with basic specifications

Course Outline

  • Introduction to the Course
    • What is Keras? - 3:29
    • Data and Code
    • Python Data Science Environment - 10:57
    • For Mac Users - 4:05
    • 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
  • 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 Keras (Simple) - 5:43
    • Autoencoders in Keras (Sparsity Constraints) - 4:32
  • Neural Network With Keras
    • 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
    • DNN Classifier With Keras - 3:30
    • DNN Classifier With Keras-Example 2 - 4:23
  • Convolutional Neural Networks (CNN)
    • What are CNNs? - 11:25
    • Implement a CNN With Keras - 4:04
    • CNN on Image Data with Keras-Part 2 - 5:05
  • 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

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Lessons
51

Practical Data Pre-Processing & Visualization Training with R

Learn to Pre-Process, Wrangle & Visualize Data for Practical Data Science Applications in R

By Minerva Singh | in Online Courses

This course is designed to equip you to use some of the most important R data wrangling and visualization packages such as dplyr and ggplot2. You'll discover data visualization concepts in a practical manner that will help you apply them for practical data analysis and interpretation. You'll also be able to determine which wrangling and visualization techniques are best suited to specific problems.

4.8/5 average rating: ★ ★ ★ ★

  • Access 51 lectures & 6 hours of content 24/7
  • Read in data into the R environment from different sources
  • Carry out basic data pre-processing & wrangling in R Studio
  • Learn to identify which visualizations should be used in any given situation
  • Build powerful visualizations & graphs from real data
Minerva Singh | Bestselling Instructor & Data Scientist
4.3/5 Instructor Rating: ★ ★ ★ ★

Minerva Singh is a Ph.D. 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: desktop & mobile
  • Certificate of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Any device with basic specifications

Course Outline

  • Welcome To The Course
    • Introduction To The Course and Instructor - 1:59
    • Data and Code Used in the Course
    • Install R and RStudio - 6:36
  • Read in Data From Different Sources
    • Read in CSV and Excel Data - 9:56
    • Read Unzipped Folder - 3:00
    • Read Online CSV - 4:04
    • Read in Googlesheets - 3:53
    • Read in Data from Online HTML Tables-Part 1 - 4:13
    • Read in Data from Online HTML Tables-Part 2 - 6:24
    • Read Data from a Database - 8:23
  • Common Data Pre-Processing Techniques
    • Basic Data Cleaning in R: Remove NA - 17:12
    • Additional Data Cleaning - 8:05
    • Indexing and Subsetting Data - 11:59
    • Summarising Based on Qualitative Attributes - 3:40
    • Of Long and Wide - 5:36
    • Pre-processing Tasks and the Pipe Operator - 9:14
    • Introduction to dplyr for Data Summarizing-Part 1 - 6:11
    • Introduction to dplyr for Data Summarizing-Part 2 - 4:44
    • Start with Tidyverse - 3:17
    • Column Renaming - 6:59
    • Tidy Data: Long and Wide - 5:03
    • Joining Tables - 5:58
    • Summarising Based on Qualitative Attributes - 3:40
    • Of Long and Wide - 5:36
  • Basic Data Visualization
    • What is Data Visualisation? - 9:33
    • Some Principles of Data Visualisation - 6:46
    • Exploratory Data Analysis (EDA) in R - 9:02
    • More Exploratory Data Analysis with xda - 4:16
  • Grammar of Graphics: ggplot2
    • Start with qplot - 4:45
    • More qplot Visualizations - 7:24
    • Start with ggplot - 4:59
    • Scatterplots with ggplot2 - 5:38
    • Faceting With ggplot2 - 4:42
    • More Faceting - 11:51
    • Insert a Smoothing Line - 7:08
    • Boxplots - 3:50
    • More Boxplots - 11:21
    • Histograms - 11:58
    • Error Bars - 7:08
    • Barplots For Discrete Numbers - 14:12
    • Line Charts - 5:57
    • Additional ggplot2 Themes - 4:32
  • Real Life Data
    • Use dplyr and ggplot2 - 6:07
    • nobel1 - 16:26
    • nobel2 - 7:35
    • Mining and Visualising Information About the Olympic Games-Part 1 - 12:49
    • Of Winter and Summer Olympic Games - 4:16
    • Of Men and Women - 8:26
  • Geographic Visualisations
    • Brief Introduction - 4:17
    • Work With R's Inbuilt Geospatial Data-Part 2 - 7:32
    • Use ggplot2 For Geographic Data Visualisations - 14:11

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Lessons
39

Pre-Process & Visualize Data with Tidy Techniques in R

Become Highly Proficient in Data Pre-Processing, Wrangling & Visualization Using the Two 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.

  • Access 39 lectures & 4 hours of content 24/7
  • 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
Minerva Singh | Bestselling Instructor & Data Scientist
4.3/5 Instructor Rating: ★ ★ ★ ★

Minerva Singh is a Ph.D. 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: desktop & mobile
  • Certificate of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Any device with basic specifications

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

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1.0 hours
Lessons
13

Python For Beginners: The Basics For Python Development

Start Your Programming Journey with an Hour of Basic Content on the Python Language & Its Fundamental Concepts

By Digital Flow | in Online Courses

Python is a high-level statically typed programming language that has become a trendsetter in the industry. It offers easy syntax and wide support for APIs and external packages. Python is extremely versatile it can be used for automation, GUI Applications, making websites, making web apps, and even for hacking! In this course, you are going to learn the basics of Python Programming so that you can start your journey by working with this programming language.

4.2/5 average rating: ★ ★ ★ ★

  • Access 13 lectures & 1 hour of content 24/7
  • Understand the Python programming language
  • Know the basic functions of Python
  • Learn how Python programming works
  • Know some of the basic yet essential applications of Python
DF Courses comes from Digital Flow Courses which is an online course publishing house. They work with multiple instructors and professionals to try and bring you the best and most practical online courses that will help you to succeed in your business and professional life.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Certificate of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: beginner

Requirements

  • Any device with basic specifications

Course Outline

  • First Section
    • Introduction To Python - 5:52
    • Data Types - 13:20
    • Strings - 4:17
    • Math Operators - 2:00
    • If Else Conditions - 6:20
    • For Loop - 3:32
    • While Loop - 5:30
    • Lists - 9:57
    • Dictionaries - 5:27
    • Tuples - 1:00
    • Practice Problem - 3:31
    • Functions - 4:55
    • Files - 8:23

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29

Python for Beginners: Learn All the Basics of Python

Learn How to Program in Python — Functions, Basic Apps, Tips & Tricks, and Other Python Features

By Yassin Marco | in Online Courses

The way the course is made is really done to help you learn all the basics of this programming language. From installing your text editor to writing your first lines of code to create your small apps everything is structured to help you achieve your goal of learning how to program in python. This course also teaches many python functions and formulas and gives a complete understanding of those. The goal is really to help you have a complete understanding of this programming language. Indeed, the hardest part is not to learn the language but to think like a programmer and this is exactly what this course is going to teach you.

4.3/5 average rating: ★ ★ ★ ★

  • Access 29 lectures & 5 hours of content 24/7
  • Learn how to use Python 3 the right way
  • Understand complex functions in Python
  • Be able to use Python on a daily basis
  • Create your own basic programs with Python
Yassin Marco
4.1/5 Instructor Rating: ★ ★ ★ ★

Yassin has a BS in international management and multiple certifications in management and IT. He works on a daily basis with various Microsoft apps and is a specialist in excel as well as in various other fields such as online business creation and promotion, marketing, and many more. Also, he has a passion for finances and has helped many people in taking their first steps in the trading and investing world, from basic financial coaching to advanced Stock/Forex data analysis.

He has developed a passion for coaching and educating and has helped more than 380 000+ students on multiple online platforms. Teaching in English and French, he has been able to reach across to people spanning from over 198 countries.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Certificate of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: beginner

Requirements

  • Any device with basic specifications

Course Outline

  • Introduction
    • Introduction - 5:16
    • Why Python - 8:17
    • Downloading and setting up Python - 6:53
  • Let's start
    • Writing our First lines of code (Hello World) - 2:53
    • Playing with the Print Function - 6:48
    • Using Variables in Python - 18:38
    • String Manipulation - 17:38
    • Different number manipulation - 19:56
    • Understanding the Input Function on Python - 9:33
    • Practice Part 1 - 14:09
    • Practice Part 2 - 20:20
  • Part 2
    • Adding Comments on your Project - 3:14
    • The utility of Functions - 16:53
    • How to use the return statement - 4:41
    • Understanding the If Statement Part 1 - 13:45
    • Understanding the If Statement Part 2 - 17:13
    • Understanding the If Statement Part 3 - 12:37
    • Working with lists - 9:17
    • Using Functions with Lists - 13:05
    • Difference Between Lists and Tuples - 7:47
    • What is a Dictionnary in Python - 9:56
    • Introduction to While Loop structures - 6:47
    • Understanding For Loops - 8:43
    • Practice: Creating and blocking passwords - 15:07
    • Practice: Testing Combinations - 7:36
    • Creation of a Basic Python APP - 12:16
    • Working with Classes and Objects - 9:46
    • Dealing with Errors - 5:50
  • Conclusion
    • Conclusion - 2:31

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Access
Lifetime
Content
7.0 hours
Lessons
51

Python: Introduction to Data Science & Machine Learning A-Z

Learn Python for Data Science & Improve Your Current Programming Skills

By Yassin Marco | in Online Courses

This course is structured in a way that you will be able to learn each tool separately and practice by programming in python directly with the use of those tools. Indeed, you will first learn all the mathematics that is associated with Data Science. This means that you will have a complete introduction to the majority of important statistical formulas and functions that exist. You will also learn how to set up and use Jupyter as well as Pycharm to write your Python code. After, you are going to learn different Python libraries that exist and how to use them properly. Finally, you will have an introduction to machine learning and learn how a machine learning algorithm works. All this in just one course.

4.2/5 average rating: ★ ★ ★ ★

  • Access 51 lectures & 7 hours of content 24/7
  • Understand the basics of python programming
  • Learn all the basic mathematical concepts
  • Master the basics of Data Science & how to perform it using Python
  • Use different python tools specialised for data science
  • Improve your python programming by integrating new concepts
Yassin Marco
4.1/5 Instructor Rating: ★ ★ ★ ★

Yassin has a BS in international management and multiple certifications in management and IT. He works on a daily basis with various Microsoft apps and is a specialist in excel as well as in various other fields such as online business creation and promotion, marketing, and many more. Also, he has a passion for finances and has helped many people in taking their first steps in the trading and investing world, from basic financial coaching to advanced Stock/Forex data analysis.

He has developed a passion for coaching and educating and has helped more than 380 000+ students on multiple online platforms. Teaching in English and French, he has been able to reach across to people spanning from over 198 countries.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Certificate of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Basic python programming skills (can be helpful but not mandatory)

Course Outline

  • Introduction
    • Introduction - 8:21
    • What is Data Science - 21:32
    • Installation of Anaconda and Jupyter Part 1 - 3:45
    • Introduction to Jupyter Part 1 - 6:55
    • Introduction to jupyter part 2 - 9:25
  • Basic Statistics knowledge
    • The Basics of Data - 11:29
    • The basics of statistics part 1 - 9:49
    • The Basics of statistics part 2 - 16:54
    • The Basics of Statistics Part 3 - 5:55
    • The Basics of Statistics Part 4 - 10:51
    • The Basics of Statistics Par 5 - 10:42
    • The basics of statistics part 6 - 15:03
  • Python library: NumPy
    • Introduction to NumPy - 7:49
    • Setting up NumPy - 3:15
    • Basics of NumPy part 1 - 6:15
    • Basics of NumPy Part 2 - 10:13
    • Basics of NumPy Part 3 - 10:31
    • Basics of NumPy Part 4 - 8:41
    • Basics of NumPy Part 5 - 4:30
  • Python library: Pandas
    • The Basics of Pandas - 7:19
    • Setting up Pandas - 2:52
    • Pandas operations Part 1 - 6:05
    • Pandas operations Part 2 - 3:37
    • Pandas operations Part 3 - 9:25
    • Pandas operations Part 4 - 8:26
    • Pandas operations Part 5 - 5:39
  • Python Library: SciPy
    • The Basics of SciPy - 5:53
    • SciPy operations Part 1 - 8:48
    • SciPy operations Part 2 - 8:34
    • SciPy operations Part 3 - 8:22
    • SciPy operations Part 4 - 5:56
    • SciPy operations Part 5 - 5:39
  • Python library: MatPlotlib
    • Introduction to Matplotlib - 6:13
    • Setting up Matplotlib - 1:28
    • The Basics of Matplotlib Part 1 - 12:42
    • The basics of Matplotlib Part 2 - 7:18
    • The basics of Matplotlib Part 3 - 9:13
    • The basics of Matplotlib Part 4 - 6:57
    • The basics of Matplotlib Part 5 - 5:40
  • Python library: Seaborn
    • Introduction to seaborn - 6:07
    • Setting up Seaborn - 2:24
    • Seaborn operations part 1 - 9:16
    • Seaborn operations part 2 - 5:49
    • Seaborn operations Part 3 - 8:23
    • Seaborn operations Part 4 - 7:13
  • Machine learning
    • Introduction to machine learning - 11:24
    • Different machine learning algorithms - 20:41
    • Machine learning algorithm part 1 - 8:43
    • Machine learning algorithm part 2 - 24:04
    • Machine learning algorithm Part 3 - 12:13
  • Conclusion
    • Conclusion - 2:48

View Full Curriculum



Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.