The Complete Machine Learning Course with Python
4.6 /5- Offered byUDEMY
The Complete Machine Learning Course with Python at UDEMY Overview
Duration | 17 hours |
Total fee | ₹599 |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
The Complete Machine Learning Course with Python at UDEMY Highlights
- Compatible on Mobile and TV
- Earn a Cerificate on successful completion
- Get Full Lifetime Access
- Learn from Codestars by Rob Percival
The Complete Machine Learning Course with Python at UDEMY Course details
- Anyone willing and interested to learn machine learning algorithm with Python
- Any one who has a deep interest in the practical application of machine learning to real world problems
- Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms
- Any intermediate to advanced EXCEL users who is unable to work with large datasets
- Anyone interested to present their findings in a professional and convincing manner
- Anyone who wishes to start or transit into a career as a data scientist
- Anyone who wants to apply machine learning to their domain
- Machine Learning Engineers earn on average $166,000 - become an ideal candidate with this course!
- Solve any problem in your business, job or personal life with powerful Machine Learning models
- Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
- Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning etc
- The Complete Machine Learning Course in Python has been FULLY UPDATED for November 2019 ! With brand new sections as well as updated and improved content , you get everything you need to master Machine Learning in one course! The machine learning field is constantly evolving, and we want to make sure students have the most up-to-date information and practicesavailable to them: Brand new sections include: Foundations of Deep Learning covering topics such as the difference between classical programming and machine learning, differentiate between machine and deep learning, the building blocks of neural networks, descriptions of tensor and tensor operations, categories of machine learning and advanced concepts such as over- and underfitting, regularization, dropout, validation and testing and much more. Computer Vision in the form of Convolutional Neural Networks covering building the layers, understanding filters / kernels, to advanced topics such as transfer learning, and feature extrations. And the following sections have all been improved and added to : All the codes have been updated to work with Python 3.6 and 3.7 The codes have been refactored to work with Google Colab Deep Learning and NLP Binary and multi-class classifications with deep learning Get the most up to date machine learning information possible, and get it in a single course! * * * The average salary of a Machine Learning Engineer in the US is $166,000! By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real life problems in your business, job or personal life with Machine Learning algorithms. Come learn Machine Learning with Python this exciting course with Anthony NG, a Senior Lecturer in Singapore who has followed Rob Percival's project based" teaching style to bring you this hands-on course. With over 18 hours of content and more than fifty 5 star ratings , it's already the longest and best rated Machine Learning course on Udemy! Build Powerful Machine Learning Models to Solve Any Problem You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen. By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more! Inside the course, you'll learn how to: Set up a Python development environment correctly Gain complete machine learning tool sets to tackle most real world problems Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them. Combine multiple models with by bagging, boosting or stacking Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data Develop in Jupyter (IPython) notebook, Spyder and various IDE Communicate visually and effectively with Matplotlib and Seaborn Engineer new features to improve algorithm predictions Make use of t rain/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data Use SVM for handwriting recognition, and classification problems in general Use decision trees to predict staff attrition Apply the association rule to retail shopping datasets And much much more! No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q&A area. Make This Investment in Yourself If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you! Take this course and become a machine learning engineer!
The Complete Machine Learning Course with Python at UDEMY Curriculum
Introduction
What Does the Course Cover?
What Does the Course Cover?
How to Succeed in This Course
Project Files
Project Files and Resources
Getting Started with Anaconda
[Windows OS] Downloading & Installing Anaconda
Installing Applications and Creating Environment
[Windows OS] Managing Environment
[Mac OS] Intructions on Installing Anaconda and Managing Environment
Practice Activity: Create a New Environment
Navigating the Spyder & Jupyter Notebook Interface
Hello World
Downloading the IRIS Datasets
Iris Project 1: Working with Error Messages
Data Exploration and Analysis
Iris Project 2: Reading CSV Data into Memory
Presenting Your Data
Iris Project 3: Loading data from Seaborn
Iris Project 4: Visualization
Regression
Introduction
Categories of Machine Learning
Working with Scikit-Learn
Scikit-Learn
Boston Housing Data - EDA
EDA
Correlation Analysis and Feature Selection
Correlation Analysis and Feature Selection
Simple Linear Regression Modelling with Boston Housing Data
Linear Regression with Scikit-Learn
Five Steps Machine Learning Process
Robust Regression
Robust Regression
Evaluate Model Performance
Evaluate Regression Model Performance
Multiple Regression with statsmodel
Multiple Regression 1
Multiple Regression 2
Multiple Regression and Feature Importance
Ordinary Least Square Regression and Gradient Descent
Regularised Method for Regression
Regularized Regression
Polynomial Regression
Polynomial Regression
Dealing with Non-linear relationships
Dealing with Non-linear Relationships
Feature Importance Revisited
Feature Importance
Data Pre-Processing 1
Data Preprocessing
Data Pre-Processing 2
Variance Bias Trade Off - Validation Curve
Variance-Bias Trade Off
Variance Bias Trade Off - Learning Curve
Learning Curve
Cross Validation
Cross Validation
CV Illustration
Classification
Introduction
Logistic Regression 1
Logistic Regression
Logistic Regression 2
Introduction to Classification
Understanding MNIST
MNIST Project 1 - Introduction
MNIST Project 2 - SGDClassifier
SGD
Performance Measure and Stratified k-Fold
MNIST Project 3 - Performance Measures
Confusion Matrix
Precision
Recall
f1
Precision Recall Tradeoff
Altering the Precision Recall Tradeoff
ROC
MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score
MNIST Project 5 - Precision and Recall Tradeoff
MNIST Project 6 - The ROC Curve
MNIST Exercise
Support Vector Machine (SVM)
Introduction
Support Vector Machine (SVM) Concepts
Support Vector Machine (SVM) Concepts
Linear SVM Classification
Linear SVM Classification
Polynomial Kernel
Polynomial Kernel
Gaussian Radial Basis Function
Radial Basis Function
Support Vector Regression
Support Vector Regression
Advantages and Disadvantages of SVM
Tree
Introduction
What is Decision Tree
Introduction to Decision Tree
Training a Decision Tree
Training and Visualizing a Decision Tree
Visualising a Decision Trees
Visualizing Boundary
Decision Tree Learning Algorithm
Decision Tree Regression
Tree Regression, Regularization and Over Fitting
End to End Modeling
Project HR
Project HR with Google Colab
Overfitting and Grid Search
Where to From Here
Project HR - Loading and preprocesing data
Project HR - Modelling
Ensemble Machine Learning
Introduction
Ensemble Learning Methods Introduction
Ensemble Learning Methods Introduction
Bagging Part 1
Bagging
Bagging Part 2
Random Forests
Random Forests and Extra-Trees
Extra-Trees
AdaBoost
AdaBoost
Gradient Boosting Machine
Gradient Boosting Machine
XGBoost Installation
XGBoost
XGBoost
Project HR - Human Resources Analytics
Project HR - Human Resources Analytics
Ensemble of ensembles Part 1
Ensemble of Ensembles Part 1
Ensemble of ensembles Part 2
Ensemble of ensembles Part 2
k-Nearest Neighbours (kNN)
kNN Introduction
kNN Introduction
kNN Concepts
kNN and Iris Dataset Demo
Distance Metric
Project Cancer Detection
Addition Materials
Project Cancer Detection Part 1
Project Cancer Detection Part 2
Unsupervised Learning: Dimensionality Reduction
Introduction
Dimensionality Reduction Concept
Dimensionality Reduction Concept
PCA Introduction
PCA Introduction
Dimensionality Reduction Demo
Project Wine 1: Dimensionality Reduction with PCA
Project Wine
Project Abalone
Project Wine 2: Choosing the Number of Components
Kernel PCA
Kernel PCA
Kernel PCA Demo
Kernel PCA Demo
LDA & Comparison between LDA and PCA
LDA vs PCA
Project Abalone
Unsupervised Learning: Clustering
Introduction
Clustering Concepts
Clustering
MLextend
Ward?????????????s Agglomerative Hierarchical Clustering
Truncating Dendrogram
k-Means Clustering
k_Means Clustering
Elbow Method
Silhouette Analysis
Mean Shift
Deep Learning
Estimating Simple Function with Neural Networks
Neural Network Architecture
Motivational Example - Project MNIST
Binary Classification Problem
Natural Language Processing - Binary Classification
Appendix A1: Foundations of Deep Learning
Introduction to Neural Networks
Differences between Classical Programming and Machine Learning
Learning Representations
What is Deep Learning
Learning Neural Networks
Why Now?
Building Block Introduction
Tensors
Tensor Operations
Gradient Based Optimization
Getting Started with Neural Network and Deep Learning Libraries
Categories of Machine Learning
Over and Under Fitting
Machine Learning Workflow
Computer Vision and Convolutional Neural Network (CNN)
Outline
Neural Network Revision
Motivational Example
Visualizing CNN
Understanding CNN
Layer - Input
Layer - Filter
Activation Function
Pooling, Flatten, Dense
Training Your CNN 1
Training Your CNN 2
Loading Previously Trained Model
Model Performance Comparison
Data Augmentation
Transfer Learning
Feature Extraction
State of the Art Tools
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