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60

Machine Learning with Python

Learn the Fundamentals of Machine Learning with Python

By Juan Galvan | in Online Courses

In this practical, hands-on course, our main objective is to give you the foundational educations of Machine Learning with Python. Understandably, a theory is important to build a solid foundation. However, that theory alone isn’t going to get the job done, so that’s why this course is packed with practical hands-on examples that you can follow step by step. This section gives you a full introduction to Machine Learning, including Supervised & Unsupervised ML with hands-on, step-by-step training.

  • Access 77 lectures & 12 hours of content 24/7
  • Introduction to Machine learning
  • Understand data processing
  • Learn about linear regression & logistic regression
  • Know what decision trees, ensemble learning, K-nearest neighbors & others are all about
  • Gain insights on support vector machines, PCA & K-means clustering
Juan E. Galvan | Top Instructor | Digital Entrepreneur
4.4/5 Instructor Rating: ★ ★ ★ ★

Juan Galvan has been an Entrepreneur since grade school. He has started several companies, created many products, and sold on various online marketplaces with great success. He founded Sezmi SEO, an agency based out of Seattle, Washington.

His collection of principles, thoughts, and sayings has grown over the years. These have come from the teachings of powerful and famous people like Warren Buffett, Charlie Munger, Peter Drucker, Jim Rohn and his personal mentors.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certification of completion included
  • Experience level required: intermediate
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Your First Program
  • 9. Machine Learning
    • Intro to Machine Learning - 26:03
  • 15. Decision Trees
    • 15.1 Decision Trees Section Overview - 4:11
    • 15.2 EDA on Adult Dataset - 16:53
    • 15.3 What is Entropy and Information Gain - 21:50
    • 15.4 The Decision Tree ID3 algorithm from scratch Part 1 - 11:32
    • 15.5 The Decision Tree ID3 algorithm from scratch Part 2 - 7:35
    • 15.6 The Decision Tree ID3 algorithm from scratch Part 3 - 4:07
    • 15.7 ID3 - Putting Everything Together - 21:23
    • 15.8 Evaluating our ID3 implementation - 16:53
    • 15.9 Compare with Sklearn implementation - 8:51
    • 15.10 Visualizing the Tree - 10:15
    • 15.11 Plot the features importance - 5:51
    • 15.12 Decision Trees Hyper-parameters - 11:39
    • 15.13 Pruning - 17:11
    • 15.14 [Optional] Gain Ration - 2:49
    • 15.15 Decision Trees Pros and Cons - 7:31
    • 15.16 [Project] Predict whether income exceeds $50Kyr - Overview - 2:33
  • 16. Ensemble Learning and Random Forests
    • Ensemble Learning Section Overview - 3:46
    • What is Ensemble Learning? - 13:06
    • What is Bootstrap Sampling? - 8:25
    • What is Bagging? - 5:20
    • Out-of-Bag Error (OOB Error) - 7:47
    • Implementing Random Forests from scratch Part 1 - 22:34
    • Implementing Random Forests from scratch Part 2 - 6:10
    • Compare with sklearn implementation - 3:41
    • Random Forests Hyper-Parameters - 4:23
    • Random Forests Pros and Cons - 5:25
    • What is Boosting? - 4:41
    • AdaBoost Part 1 - 4:10
    • AdaBoost Part 2 - 14:33
  • 17. Support Vector Machines
    • SVM - Outline - 5:15
    • SVM - SVM intuition - 11:38
    • SVM - Hard vs Soft Margin - 13:25
    • SVM - C Hyper-Parameter - 4:17
    • SVM - Kernel Trick - 12:18
    • SVM - Kernel Types - 18:13
    • SVM - with Linear Dataset - 13:35
    • SVM - Non-Linear Dataset - 12:50
    • SVM- Multi _ Regression - 5:51
    • SVM - Project Overview (Voice Gender Recognition) - 4:26
  • 19. PCA
    • PCA - Section Overview - 5:12
    • What is PCA - 9:36
    • PCA - Drawbacks - 3:31
    • PCA - Algorithm Steps - 13:12
    • PCA - Covariance Matrix vs SVD - 4:58
    • PCA - Main Applications - 2:50
    • PCA - Image Compression - 27:00
    • PCA - Data Preprocessing - 14:31
    • PCA - BiPlot and The Screen Plot - 17:27
    • PCA - Feature Scaling and Screeplot - 9:29
    • PCA - Supervised vs unsupervised - 4:55
    • PCA - Visualization - 7:31
  • 20. Data Science Career
    • Creating a Data Science Resume - 6:45
    • Data Science Cover Letter - 3:33
    • How to Contact Recruiters - 4:20
    • Getting Started with Freelancing - 4:13
    • Top Freelance Websites - 5:35
    • Personal Branding - 4:02
    • Networking Do's and Don'ts - 3:45
    • Importance of a Website - 2:56

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64

Machine Learning and Data Science Developer Certification Program

Learn the Powerful Tools Used in Data Science & Machine Learning

By Starweaver | in Online Courses

The Machine Learning & Data Science Developer Certification Program provides a comprehensive set of knowledge and skills in data science, machine learning, and deep learning. This immersive training curriculum covers all the key technologies, techniques, principles, and practices you need to play a key role in your data science development team and distinguish yourself professionally. This program moves progressively and rapidly to cover the foundational components of machine learning, beginning with foundational principles and concepts used in data science and machine learning.

4.6/5 average rating: ★ ★ ★ ★

  • Access 64 lectures & 11 hours of content 24/7
  • Develop to real-world machine learning problems
  • Explain & discuss the essential concepts of machine learning and, in particular, deep learning
  • Implement supervised & unsupervised learning models for tasks such as forecasting, predicting and outlier detection
  • Apply & use advanced machine learning applications, including recommendation systems and natural language processing
  • Evaluate & apply deep learning concepts and software applications
  • Identify, source & prepare raw data for analysis and modelling
  • Work with open source tools such as Python, Scikit-learn, Keras and Tensorflow
Starweaver
4.4/5 Instructor Rating: ★ ★ ★ ★

Starweaver provides live, immersive, activity-based online education in technology and business for professionals and graduating college-aged students, and an extensive library of activities & courses. Our mission is to transform technologists into world-class experts and businesspeople into tech savvy leaders.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Module 1: Introduction to Machine Learning
    • READING: Intro to Machine Learning for Managers (Read Pages 1-12)
    • READING: Jeff Dean Rice Talk - State of Artificial Intelligence (Read entire document) (Dated but useful)
    • Segment - 01 - Introduction to Machine Learning - 53:10
    • Segment - 02 - Lab 1 - 8:23
    • Segment - 03 - Lab 2a - 1:52
    • Segment - 04 - Pandas - 35:26
    • Segment - 05 - Exploring Pandas - 9:10
    • Segment - 06 - Lab 2b - 2:07
    • Segment - 07 - Lab 2c - 1:48
    • Segment - 08 - Visualization - 18:42
    • Segment - 09 - Lab 2d - 1:30
    • Segment - 10 - Visualization Stats - 13:24
    • Segment - 11 - Lab 3a - 3:29
    • Segment - 12 - Sklearn - 29:55
    • Segment - 13 - Lab 3b - 1:38
    • Segment - 14 - Linear Regression - 12:50
    • Segment - 15 - Multivariate Linear Regression - 7:16
    • Segment - 16 - Logistic Regression - 22:21
  • Module 2: Exploring and Using Data Sets
    • Segment - 17 - Classification (Support Vector Machines) - 21:19
    • Segment - 18 - Classification (Naive Bayes) - 27:41
    • Segment - 19 - Lab 1a and 1b - 2:38
  • Module 3: Review of Machine Learning Algorithms
    • Segment - 20 - Decision Trees - 27:02
    • Segment - 21 - Random Forests - 13:21
    • Segment - 22 - Lab 2a and 2b - 2:58
    • Segment - 23 - Lab 2c - 2:42
    • Segment - 24 - Clustering - 26:36
    • Segment - 25 - Principle Component Analysis - 20:59
    • Segment - 26 - Lab 3a and 3b - 3:52
    • Segment - 27 - Lab-3c (Principal Component Analysis) - 4:21
  • Module 4: Machine Learning with Scikit
    • Segment - 28 - Deep Learning Introduction - 20:36
    • Segment - 29 - Lab 1a - TensorFlow Playground - 3:20
    • Segment - 30 - TensorFlow Introduction - 9:17
    • Segment - 31 - Lab 1b - TensorFlow Sessions - 1:24
    • Segment - 32 - TensorFlow Low Level API - 14:14
    • Segment - 33 - TensorFlow Linear Models - 34:18
    • Segment - 34 - Lab 2a and 2b - 2:22
    • Segment - 35 - TensorFlow High Level API - 14:52
    • Segment - 36 - Lab 2c and 2d - 2:15
    • Segment - 37 - Lab 3a - 1:51
    • Segment - 38 - Lab 3b and 3c - 2:57
    • Segment - 39 - Lab 3d and 3e - 4:32
    • Segment - 40 - Multilayer Perceptron (MLP) - 21:45
  • Module 5: Deep Learning with Keras and TensorFlow
    • Segment - 41 - Convolutional Neural Network - 33:30
    • Segment - 42 - Convolutional Neural Network Extended - 20:45
    • Segment - 43 - TensorBoard Visualizing Learning - 10:03
  • Module 6: Deeper Understanding of Tensorflow
    • Segment - 44 - Transfer Learning - 15:51
    • Segment - 45 - Recurrent Neural Network - 29:30
    • Segment - 46 - Long Short Term Memory (LSTM) - 34:50
  • Module 7: Building a Machine Learning Pipeline
    • Segment - 47 - Scaling Machine Learning Distributed TensorFlow - 31:33
    • Segment - 48 - Feature Engineering - 25:19
    • Segment - 49 - Pipeline Examples - 3:48
  • Quizzes
    • Overview
    • Quiz One
    • Quiz Two
    • Quiz Three
    • Quiz Four
    • Quiz Five
    • Quiz Six
  • Labs
    • Module One
    • Module Two
    • Module Three
    • Module Four
    • Module Five
    • Module Six

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45

Complete Machine Learning & Data Science with Python | A-Z

Use Scikit, NumPy, Pandas, Matplotlib, Seaborn & dive into Machine Learning Through This Python COurse with Real-Life Exercises

By Oak Academy | in Online Courses

This is a straightforward course for Python Programming Language and Machine Learning. In the course, you will have down-to-earth way explanations with projects. With this course, you will learn Machine Learning step-by-step. It comes with easy exercises, challenges, and lots of real-life examples. Open your door to the Data Science and Machine Learning world. You will learn the fundamentals of Machine Learning and its beautiful libraries such as Scikit Learn. Throughout the course, Your will learn how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms.

  • Access 45 lectures & 8 hours of content 24/7
  • Introduce yourself to Machine Learning
  • Familiarize with Evaluation Metrics
  • Linear Regression
  • What is Classification vs Regression?
  • Evaluating Performance-Classification Error Metrics
  • Evaluating Performance-Regression Error Metrics
  • Supervised Learning
  • Cross-Validation and Bias Variance Trade-Off
  • Use Matplotlib and seaborn for data visualizations
  • Machine Learning with SciKit Learn
  • Logistic Regression
Oak Academy | Long Live Tech Knowledge
4.4/5 Instructor Rating: ★ ★ ★ ★

Oak Academy is a group of tech experts who have been in the sector for years and years. Deeply rooted in the tech world, they know that the tech industry's biggest problem is the "tech skills gap" and their online course are their solution. They specialize in critical areas like cybersecurity, coding, IT, game development, app monetization, and mobile. Thanks to their practical alignment, they are able to constantly translate industry insights into the most in-demand and up-to-date courses.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certification of completion included
  • Experience level required: intermediate
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Introduction to Machine Learning
    • 1 - What is Machine Learning - 4:05
    • 2 - Machine Learning Terminology - 2:39
    • Project Files
  • Evalution Metrics
    • 3 - Classification vs Regression - 3:38
    • 4 - Classification Error Metrics - 20:15
    • 5 - Regression Error Metrics - 7:54
    • 6 - Machine Learning with Python - 13:13
  • Supervised Learning
    • 7 - Supervised Learning Overview - 11:27
  • Linear Regression
    • 8 - Linear Regression Theory - 6:09
    • 9 - LinearRegressionwithPythonPart_1 - 23:22
    • 10 - Linear Regression with Python Part 2 - 9:12
    • 11 - Linear Regression Project Overview - 3:19
    • 12 - Linear Regression Project Solution - 25:40
  • Bias Variance Trade-Off
    • 13 - BIAS Variance Trade-Off - 8:30
  • Logistic Regression
    • 14 - Logistic Regression Theory - 14:51
    • 15 - LogisticRegressionwithPythonPart_1 - 21:00
    • 16 - Logistic Regression with Python Part 2 - 23:03
    • 17 - Logistic Regression with Python Part 3 - 10:39
    • 18 - Logistic Regression Project Overview - 2:32
    • 19 - Logistic Regression Project Solutions - 14:57
  • K Nearest Neighbors Algorithm
    • 20 - K Nearest Neighbors Algorithm Theory - 6:56
    • 21 - K Nearest Neighbors Algorithm With Python - 26:38
    • 22 - K Nearest Neighbors Algorithm Project Overview - 1:51
    • 23 - K Nearest Neighbors Algorithm Project Solutions - 19:53
  • Decision Trees And Random Forest Algorithm
    • 24 - Decision Trees And Random Forest Algorithm Theory - 8:51
    • 25 - Decision Trees And Random Forest Algorithm With Python - 15:03
    • 26 - Decision Trees And Random Forest Algorithm Project Overview - 4:44
    • 27 - Decision Trees And Random Forest Algorithm Project Solutions Part 1 - 16:13
    • 28 - Decision Trees And Random Forest Algorithm Project Solutions Part 2 - 12:12
  • Support Vector Machine Algorithm
    • 29 - Support Vector Machines Algorithm Theory - 6:06
    • 30 - Support Vector Machines Algorithm With Python - 24:08
    • 31 - Support Vector Machines Algorithm Project Overview - 2:53
    • 32 - Support Vector Machines Algorithm Project Solutions - 14:06
  • Unsupervised Learning
    • 33 - Unsupervised Learning Overview - 3:26
  • K Means Clustering Algorithm
    • 34 - K Means Clustering Algorithm Theory - 6:17
    • 35 - K Means Clustering Algorithm With Python - 16:48
    • 36 - K Means Clustering Algorithm Project Overview - 4:22
    • 37 - K Means Clustering Algorithm Project Solutions - 18:42
  • Hierarchical Clustering Algorithm
    • 38 - Hierarchical Clustering Algorithm Theory - 4:41
    • 39 - Hierarchical Clustering Algorithm With Python - 11:04
  • Principal Component Analysis (PCA)
    • 40 - Principal Component Analysis (PCA) Theory - 4:13
    • 41 - Principal Component Analysis (PCA) With Python - 21:34
  • Recommender System Algorithm
    • 42 - Recommender System Algorithm Theory - 5:44
    • 43 - Recommender System Algorithm With Python Part 1 - 17:54
    • 44 - Recommender System Algorithm With Python Part 2 - 17:31

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Deep Learning with Python

Complete Hands-on Deep Learning Tutorial and Learn to Create Deep Learning Algorithms on Python

By Oak Academy | in Online Courses

In this course, you will learn some fundamental stuff about Python and the Numpy library. Then Machine Learning history, concepts, workflow, models, and algorithms. Also, learn what is neural network concept is. Then learn Artificial Neural networks and enter the Keras world, then we exit the Tensorflow world. Then understand the Convolutional Neural Network concept. Then learn about Recurrent Neural Networks and LTSM. After a while, you will learn the Transfer Learning concept. Finally, Projects. Here you'll make some interesting machine learning models with the information you've learned along the course.

  • Access 59 lectures & 10 hours of content 24/7
  • Fundamental stuff of Python and its library Numpy
  • What are the AI, Machine Learning, and Deep Learning
  • History of Machine Learning
  • Turing Machine and Turing Test
  • Convolutional Neural Network
  • Recurrent Neural Network and LTSM
  • Transfer Learning
Oak Academy | Long Live Tech Knowledge
4.4/5 Instructor Rating: ★ ★ ★ ★

Oak Academy is a group of tech experts who have been in the sector for years and years. Deeply rooted in the tech world, they know that the tech industry's biggest problem is the "tech skills gap" and their online course are their solution. They specialize in critical areas like cybersecurity, coding, IT, game development, app monetization, and mobile. Thanks to their practical alignment, they are able to constantly translate industry insights into the most in-demand and up-to-date courses.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certification of completion included
  • Experience level required: intermediate
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Introduction
    • 1 - Introduction - 4:44
  • Python Setup
    • 2 - Installing Anaconda Distribution and Python - 4:58
    • 3 - Overview of Jupyter Notebook and Google Colab - 5:32
  • Fundamentals of Python
    • 4 - Data Types in Python - 12:42
    • 5 - Operators in Python - 10:31
    • 6 - Conditionals - 9:49
    • 7 - Loops - 13:07
    • 8 - Lists Tuples Dictionaries and Sets - 17:54
    • 9 - Data Type Operators and Methods - 11:21
    • 10 - Modules in Python - 5:15
    • 11 - Functions in Python - 8:05
    • 12 - Exercise Analyse - 1:46
    • 13 - Exercise Solution - 10:46
  • Object Oriented Programming
    • 14 - Logic of OOP - 4:58
    • 15 - Constructor - 6:34
    • 16 - Methods - 4:41
    • 17 - Inheritance - 6:42
    • 18 - Overriding and Overloading - 10:33
  • Numpy Library
    • 19 - What is Numpy - 6:49
    • 20 - Why Numpy - 4:23
    • 21 - Array and Features - 12:08
    • 22 - Array Operators - 4:53
    • 23 - Numpy Functions - 18:25
    • 24 - Indexing and Slicing - 10:15
    • 25 - Numpy Exercises - 16:03
    • 26 - Using Numpy in Linear Algebra - 30:14
    • 27 - NumExpr Guide - 9:15
  • Fundamentals of Machine Learning
    • 28 - AI, Machine Learning and Deep Learning - 4:54
    • 29 - History of Machine Learning - 6:52
    • 30 - Turing Machine and Turing Test - 12:10
    • 31 - What is Deep Learning - 5:53
    • 32 - Learning Representetion from Data - 11:15
    • 33 - Workflow of Machine Learning - 9:45
    • 34 - Machine Learning Methods - 13:34
    • 35 - Supervised Machine Learning Methods - 1 - 8:47
    • 36 - Supervised Machine Learning Methods - 2 - 13:26
    • 37 - Supervised Machine Learning Methods - 3 - 13:53
    • 38 - Supervised Machine Learning Methods - 4 - 17:04
    • 39 - Unsupervised Machine Learning Methods - 23:58
    • 40 - Gathering Data - 4:54
    • 41 - Data Pre-Processing - 5:33
    • 42 - Choosing The Right Algorithm and Model - 7:49
    • 43 - Training and testing the model - 5:19
    • 44 - Evaluation - 6:52
  • Artificial Neural Network
    • 45 - What is ANN - 7:19
    • 46 - Anatomy of a neural network - 9:22
    • 47 - Creating a Simple ANN - 17:33
    • 48 - Tensor Operations - 1 - 14:04
    • 49 - Tensor Operations - 2 - 8:20
    • 50 - Keras API - 6:46
    • 51 - Optimizers - 10:40
    • 52 - What is Tensorflow - 17:40
  • Convolutional Neural Network
    • 53 - What is CNN - 15:16
  • Recurrent Neural Network and LSTM
    • 54 - Understanding RNN and LSTM Networks - 13:14
  • Transfer Learning
    • 55 - What is Transfer Learning - 16:08
  • Projects
    • 56 - Project - 1 - 22:32
    • 57 - Project - 2 - 28:40
    • 58 - Project - 3 - 15:33
    • 59 - Project - 4 - 16:12

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Tensorflow and Keras Masterclass For Machine Learning and AI in Python

Master the Most Important Deep Learning Frameworks for Python Data Science

By Minerva | in Online Courses

This course is your complete guide to 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
Note: Software not included
Minerva Singh | Best Selling Instructor & Data Scientist
4.5/5 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.

63,454 Total Students
11,305 Reviews

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

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Harness the Power of the H2O Framework For Machine Learning in R

Master Powerful R Package for Machine Learning, Artificial Neural Networks, & Deep Learning

By Minerva | in Online Courses

In this age of big data, companies across the globe use R 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 R, you can give your company a competitive edge and boost your career to the next level. This course covers the main aspects of the H2O package for data science in R. If you take this course, you can do away with taking other courses or buying books on R-based data science as you will have the keys to a very powerful R supported data science framework.

4.4/5 average rating: ★ ★ ★ ★

  • Access 6 lectures & 0.5 hour of content 24/7
  • Be familiar with powerful R-based deep learning packages such as H2O
  • Learn the important concepts of machine learning without the jargon
  • Implement both supervised & unsupervised algorithms using H2O
  • Do Artificial Neural Networks (ANN) & Deep Neural Networks (DNN)
  • Work with real data within the framework
Minerva Singh | Best Selling Instructor & Data Scientist
4.5/5 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.

63,454 Total Students
11,305 Reviews

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
    • What is This Course About? - 2:30
    • Data and Code
    • Install R and RStudio - 6:36
    • Common data types - 3:37
    • Install H2O - 5:37
  • Read in Data From Different Sources
    • Read CSV Files - 9:56

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12

Create an Image Detection App from Scratch using Machine Learning

Learn to Create a Functioning Image Detection App on Phyton

By Kalob Taulien | in Online Courses

In this short course, you're going to create an application with Python that will detect objects inside of images. You'll use a busy downtown intersection, a cat, and a bike as examples of multiple object detection, living object detection, and how things don't always turn out how you expect. You do not need to know math or how to code to take this course! You do not need to know Python or machine learning for this course. It'll walk you through each of the steps to get set up and how to modify the code so you can perform object detection on any image.

  • Access 12 lectures & 1 hour of content 24/7
  • Create an Image Detection App from scratch
  • Learn to install Phyton
  • Familiarize yourself with its environment
  • Install packages
  • Use a custom model
  • Detect images
Kalob Taulien | Web Developer & Coding Instructor
4.6/5 Instructor Rating: ★ ★ ★ ★

Kalob is a professional web developer who 's been developing websites and working with startups since 1999. He also has a broad set of skills in software, web development, and information technology. Teaching over 210,000 students on Udemy alone, he's helped tens of thousands of people learn web development. From zero to hero and novice to ninja, he's considered a top teacher by thousands.

Throughout the years, Kalob has built hundreds, if not thousands, of websites, and has created multiple companies from his ideas and software. He also provides one-on-one coaching and startup consulting to new organizations.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certification of completion included
  • Experience level required: intermediate
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications
  • You'll need to have Python 3.7 installed
  • Command-Line program

Course Outline

  • Create an Image Detection App from Scratch using Machine Learning
    • Course introduction - 1:45
    • Demonstration - 0:51
    • Installing python - 1:06
    • Python environments - 1:53
    • Installing packages - 1:53
    • Using a custom model - 1:57
    • The 20 lines of code - 4:28
    • First detection - 4:35
    • Second detection - 1:58
    • Confidence matters - 4:34
    • How to learn more - 1:34
    • Summary - 1:34

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28

Machine Learning for Absolute Beginners

Learn the Basics of Machine Learning & AI Even with No Prior Knowledge

By John Bura | in Online Courses

The concept of Artificial Intelligence and Machine Learning can be a little bit intimidating for beginners, and specifically for people without a substantial background in complex math and programming. This training is a soft starting point to walk you through the fundamental theoretical concepts. In this course, you're going to open the mysterious AI/ML black box, take a look inside, get more familiar with the terms used in the industry. It is going to be a super interesting story. It is important to mention that there are no specific prerequisites for starting this training, and it is designed for absolute beginners.

4.3/5 average rating: ★ ★ ★ ★

  • Access 28 lectures & 2 hours of content 24/7
  • Understand the difference between Applied & Generalized AI
  • Learn the process of training a model
  • Learn more about Machine Learning & Deep Learning
  • Understand clustering & dimension reduction

Instructor

John Bura has been programming games since 1997 and teaching since 2002. John is the owner of the game development studio Mammoth Interactive, producer of XBOX 360, iPhone, iPad, Android, HTML 5, ad-games, and more. John has been contracted by many different companies to provide game design, audio, programming, level design, and project management. For more details on this course and instructor, click here.

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 to Mammoth interactive
    • 00 About Mammoth Interactive - 1:05
    • 01 How to Learn Online Effectively - 13:39
  • 01 Course Introduction
    • 01.01 Course Overview - 4:17
    • 01.02 Build Models on the Web - 2:26
    • Source Files
  • 02 Build Machine Learning Models for Absolute Beginners
    • 02.01 Types of Machine Learning Models - 11:39
    • 02.02 Load Dataset - 11:42
    • 02.03 Visualize Attributes - 12:57
    • Source Files
  • 03 Train a Model with Pandas and Scikit-learn
    • 03.01 Import Dataset - 4:19
    • 03.02 Make a Prediction - 6:32
    • 03.03 Train a Model - 7:58
    • Source Files
  • 04 Build a Neural Network
    • 04.01 What are Neural Networks - 6:15
    • 04.02 Set Up Project - 8:39
    • 04.03 Set Up Data - 7:56
    • 04.04 Train a Model - 6:42
    • 04.05 Make a Prediction - 5:05
    • Source Files

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11

Python Data Analysis & Visualization

Learn Data Analysis & Visualization with Python

By Juan Galvan | in Online Courses

In this practical, hands-on course, our main objective is to give you the foundational education on implementing Python Data Analysis & Visualization. And we understand that theory is important to build a solid foundation. We understand that theory alone isn’t going to get the job done, so that’s why this course is packed with practical hands-on examples that you can follow step by step.

  • Access 11 lectures & 2 hours of content 24/7
  • NumPy data analysis
  • Pandas data analysis
  • Phyton data visualization
Juan E. Galvan | Top Instructor | Digital Entrepreneur
4.4/5 Instructor Rating: ★ ★ ★ ★

Juan Galvan has been an Entrepreneur since grade school. He has started several companies, created many products, and sold on various online marketplaces with great success. He founded Sezmi SEO, an agency based out of Seattle, Washington.

His collection of principles, thoughts, and sayings has grown over the years. These have come from the teachings of powerful and famous people like Warren Buffett, Charlie Munger, Peter Drucker, Jim Rohn and his personal mentors.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certification of completion included
  • Experience level required: intermediate
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • 1. NumPy Data Analysis
    • NumPy Arrays - 8:21
    • NumPy Array Basics - 11:36
    • NumPy Array Indexing - 9:10
    • NumPy Array Data Types - 12:58
    • NumPy Array Computations - 5:53
    • Broadcasting - 4:32
  • 2. Pandas Data Analysis
    • Intro to Pandas - 15:52
    • Intro to Panda Continued - 18:05
  • Python Data Visualization
    • Data Visualization Overview - 24:49
    • Different Data Visualization Libraries in Python - 12:48
    • Python Data Visualization Implementation - 8:27

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13

Machine Learning with R

Expand Your AI Understanding with a Practical Approach to R Programming

By Juan Galvan | in Online Courses

From Netflix's recommendation system to Tesla's self-driving cars, machine learning is all around us, and more companies are getting on board with what this technology can offer. Serving as your machine learning primer, this course offers a comprehensive look at machine learning, the algorithms that power it, and how you can implement them with the R programming language. You'll dive into what makes today's AI innovations tick, explore key tools like TensorFlow, and get hands-on training as you explore neural networks, decisions trees, and more.

  • Access 36 lectures & 5 hours of content 24/7
  • Explore implementing machine learning algorithms w/ the R language
  • Walk through creating neural networks & implementing them in R
  • Familiarize yourself w/ TensorFlow & H2O
Juan E. Galvan | Top Instructor | Digital Entrepreneur
4.4/5 Instructor Rating: ★ ★ ★ ★

Juan Galvan has been an Entrepreneur since grade school. He has started several companies, created many products, and sold on various online marketplaces with great success. He founded Sezmi SEO, an agency based out of Seattle, Washington.

His collection of principles, thoughts, and sayings has grown over the years. These have come from the teachings of powerful and famous people like Warren Buffett, Charlie Munger, Peter Drucker, Jim Rohn and his personal mentors.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Course Outline

  • Introduction to Machine Learning
    • Machine Learning Overview - 5:26
    • Intro to Machine Learning - Part 1 - 21:48
    • Intro to Machine Learning - Part 2 - 46:45
    • Intro to Data Preprocessing - 27:03
    • Data Preprocessing - 37:47
    • Linear Regression a Simple Model Part 1 - 25:09
    • Linear Regression A Simple Model Part 2 - 53:04
    • Exploratory Data Analysis Intro - 25:03
    • Hands-on Exploratory Data Analysis - 62:57
    • Linear Regression a Real Model Part 1 - 32:04
    • Linear Regression a Real Model Part 2 - 52:48
    • Intro to Logistic Regression in R - 37:48
    • Logistic Regression in R - 39:37

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60

Machine Learning with Python

Learn the Fundamentals of Machine Learning with Python

By Juan Galvan | in Online Courses

In this practical, hands-on course, our main objective is to give you the foundational educations of Machine Learning with Python. Understandably, a theory is important to build a solid foundation. However, that theory alone isn’t going to get the job done, so that’s why this course is packed with practical hands-on examples that you can follow step by step. This section gives you a full introduction to Machine Learning, including Supervised & Unsupervised ML with hands-on, step-by-step training.

  • Access 77 lectures & 12 hours of content 24/7
  • Introduction to Machine learning
  • Understand data processing
  • Learn about linear regression & logistic regression
  • Know what decision trees, ensemble learning, K-nearest neighbors & others are all about
  • Gain insights on support vector machines, PCA & K-means clustering
Juan E. Galvan | Top Instructor | Digital Entrepreneur
4.4/5 Instructor Rating: ★ ★ ★ ★

Juan Galvan has been an Entrepreneur since grade school. He has started several companies, created many products, and sold on various online marketplaces with great success. He founded Sezmi SEO, an agency based out of Seattle, Washington.

His collection of principles, thoughts, and sayings has grown over the years. These have come from the teachings of powerful and famous people like Warren Buffett, Charlie Munger, Peter Drucker, Jim Rohn and his personal mentors.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certification of completion included
  • Experience level required: intermediate
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Machine Learning with Python
    • Intro to Machine Learning - 26:03
    • Exploratory Data Analysis - 13:05
    • Feature Scaling - 7:40
    • Data Cleaning - 7:43
    • Feature Engineering - 6:11
    • Linear Regression Intro - 8:17
    • Gradient Descent - 5:58
    • Linear Regression + Correlation Methods - 26:33
    • Linear Regression Implementation - 5:06
    • Linear Regression - 3:22
    • KNN Overview - 3:01
    • Parametric vs non-parametric models - 3:28
    • EDA on Iris Dataset - 22:08
    • KNN - Intuition - 2:16
    • Implement the KNN algorithm from scratch - 11:45
    • Compare the result with the sklearn library - 3:47
    • KNN Hyperparameter tuning using the cross-validation - 10:47
    • The decision boundary visualization - 8:17
    • KNN - Manhattan vs Euclidean Distance - 11:20
    • KNN Scaling in KNN - 6:01
    • Curse of dimensionality - 8:09
    • KNN use cases - 3:32
    • KNN pros and cons - 5:32
    • Decision Trees Section Overview - 4:11
    • EDA on Adult Dataset - 16:53
    • What is Entropy and Information Gain - 21:50
    • The Decision Tree ID3 algorithm from scratch Part 1 - 11:32
    • The Decision Tree ID3 algorithm from scratch Part 2 - 7:35
    • The Decision Tree ID3 algorithm from scratch Part 3 - 4:07
    • ID3 - Putting Everything Together - 21:23
    • Evaluating our ID3 implementation - 16:53
    • Compare with Sklearn implementation - 8:51
    • Visualizing the Tree - 10:15
    • Plot the features importance - 5:51
    • Decision Trees Hyper-parameters - 11:39
    • Pruning - 17:11
    • [Optional] Gain Ration - 2:49
    • Decision Trees Pros and Cons - 7:31
    • [Project] Predict whether income exceeds $50Kyr - Overview - 2:33
    • Ensemble Learning Section Overview - 3:46
    • What is Ensemble Learning? - 13:06
    • What is Bootstrap Sampling? - 8:25
    • What is Bagging? - 5:20
    • Out-of-Bag Error (OOB Error) - 7:47
    • Implementing Random Forests from scratch Part 1 - 22:34
    • Implementing Random Forests from scratch Part 2 - 6:10
    • Compare with sklearn implementation - 3:41
    • Random Forests Hyper-Parameters - 4:23
    • Random Forests Pros and Cons - 5:25
    • What is Boosting? - 4:41
    • AdaBoost Part 1 - 4:10
    • AdaBoost Part 2 - 14:33
    • SVM - Outline - 5:15
    • SVM - SVM intuition - 11:38
    • SVM - Hard vs Soft Margin - 13:25
    • SVM - C Hyper-Parameter - 4:17
    • SVM - Kernel Trick - 12:18
    • SVM - Kernel Types - 18:13
    • SVM - with Linear Dataset - 13:35
    • SVM - Non-Linear Dataset - 12:50
    • SVM- Multi _ Regression - 5:51
    • SVM - Project Overview (Voice Gender Recognition) - 4:26
    • Unsupervised Machine Learning Intro - 20:22
    • Unsupervised Machine Learning Continued - 20:48
    • Data Standardization - 19:05
    • PCA - Section Overview - 5:12
    • What is PCA - 9:36
    • PCA - Drawbacks - 3:31
    • PCA - Algorithm Steps - 13:12
    • PCA - Covariance Matrix vs SVD - 4:58
    • PCA - Main Applications - 2:50
    • PCA - Image Compression - 27:00
    • PCA - Data Preprocessing - 14:31
    • PCA - BiPlot and The Screen Plot - 17:27
    • PCA - Feature Scaling and Screeplot - 9:29
    • PCA - Supervised vs unsupervised - 4:55
    • PCA - Visualization - 7:31

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19

Python Basic and Advanced Functions

Learn the Basics & Advanced Functions of Python

By Juan Galvan | in Online Courses

Python is the number one programming language choice for machine learning, data science, and artificial intelligence. However, to get those high-paying programming jobs, you need the skills and knowledge of becoming an expert Python Programmer, and that’s exactly what you’ll learn in this course. In this practical, hands-on course, the main objective is to educate you on the ins and outs of Python Programming. Blending practical work with solid theoretical training, we take you from the basics of Python Programming to mastery, giving you the training you need not just to create software programs, scrape websites, and build automation but also the foundational understanding of data science and visualization so you can become a well-rounded Python Programmer.

  • Access 20 lectures & 2 hours of content 24/7
  • Learn the ins & outs of Python programming
  • Create software programs, scrape websites, & build automations
  • Understand data science & visualization
  • Become a well-rounded Python Programmer
Juan E. Galvan | Top Instructor | Digital Entrepreneur
4.5/5 Instructor Rating: ★ ★ ★ ★

Juan Galvan has been an Entrepreneur since grade school. He has started several companies, created many products, and sold on various online marketplaces with great success. He founded Sezmi SEO, an agency based out of Seattle, Washington.

His collection of principles, thoughts, and sayings has grown over the years. These have come from the teachings of powerful and famous people like Warren Buffett, Charlie Munger, Peter Drucker, Jim Rohn and his personal mentors.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Python Basic and Advanced Functions
    • What is Programming - 6:03
    • Why Python for Data Science? - 3:14
    • What is Jupyter - 3:54
    • What is Google Colab - 3:27
    • Python Variables, Booleans and None - 11:47
    • Getting Started with Colab - 9:07
    • Python Operators - 25:26
    • Python Numbers and Booleans - 7:47
    • Python Strings - 13:12
    • Python Conditional Statements - 13:53
    • Python For Loops and While Loops - 8:07
    • Python Lists - 5:10
    • More About Python Lists - 15:08
    • Python Tuples - 11:25
    • Python Dictionaries - 20:19
    • Python Sets - 9:41
    • Compound Data Types & When to use each one? - 22:39
    • Python Functions - 14:23
    • Object Oriented Programming in Python - 18:47

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