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The Complete Machine Learning Course with Python
BY
Udemy
Learn Python, SVM, regression, unsupervised machine learning and other machine learning techniques to develop a ...Read more
Online
₹ 699 3699
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
The Complete Machine Learning Course with Python online certification focus on providing an opportunity to candidates to create a catalogue of 12 Machine Learning projects that will help them secure their dream jobs or enable them to use Machine Learning algorithms to effectively deal with problems in business, career, or personal life.
The Complete Machine Learning Course with Python certification course is created by Codestars by Rob Percival - Teaching the Next Generation of Coders, Anthony NG - Algorithmic Trading Workshop Researcher and Conductor & Rob Percival - Web Developer And Teacher and presented by Udemy, to help programmers learn machine learning techniques from roots.
The Complete Machine Learning Course with Python online training will teach machine learning algorithms with python to classify flowers, estimate home prices, identify handwritings or numerals, identify staff who are most likely to depart early, detect cancer cells, and many such things until the end.
The highlights
- Certificate of completion
- Self-paced course
- English videos with multi-language subtitles
- 17.5 hours of pre-recorded video content
- Online course
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and TV
Program offerings
- Certificate of completion
- Self-paced course
- English videos
- Multi-language subtitles
- 17.5 hours of pre-recorded video content
- 3 articles
- 2 downloadable resources
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
₹ 699 ₹3,699
certificate availability
Yes
certificate providing authority
Udemy
Who it is for
What you will learn
Machine learning
Knowledge of python
Knowledge of deep learning
After completing The Complete Machine Learning Course with Python online course, learners will understand about kinds of regressions, classification, and other metrics like R-squared, MSE, accuracy, confusion, matrix, perversion, recall, and using them at right time, machine learning tools to solve issues. They will learn how bagging, boosting, or stacking can combine numerous models, to pick the correct model and forecast model performance with unseen data and utilise train/test, K-fold, and Stratified K-fold cross-validation, use unsupervised machine learning algorithms like hierarchical clustering and k-means clustering to analyse data. Candidates will be skilled to use SVM for handwriting recognition and classifying problems, using decision trees to forecast employee attrition, and communicating visually and effectively with Matplotlib and Seaborn.
The syllabus
Introduction
Getting Started with Anaconda
- Installing Applications and Creating Environment
- Hello World
- Iris Project 1: Working with Error Messages
- Iris Project 2: Reading CSV Data into Memory
- Iris Project 3: Loading data from Seaborn
- Iris Project 4: Visualization
Regression
- Scikit-Learn
- EDA
- Correlation Analysis and Feature Selection
- Correlation Analysis and Feature Selection
- Linear Regression with Scikit-Learn
- Five Steps Machine Learning Process
- Robust Regression
- Evaluate Regression Model Performance
- Multiple Regression 1
- Multiple Regression 2
- Regularized Regression
- Polynomial Regression
- Dealing with Non-linear Relationships
- Feature Importance
- Data Preprocessing
- Variance-Bias Trade-Off
- Learning Curve
- Cross-Validation
- CV Illustration
Classification
- Logistic Regression
- Introduction to Classification
- Understanding MNIST
- SGD
- Performance Measure and Stratified k-Fold
- Confusion Matrix
- Precision
- Recall
- f1
- Precision-Recall Tradeoff
- Altering the Precision-Recall Tradeoff
- ROC
Support Vector Machine (SVM)
- Support Vector Machine (SVM) Concepts
- Linear SVM Classification
- Polynomial Kernel
- Radial Basis Function
- Support Vector Regression
Tree
- Introduction to Decision Tree
- Training and Visualizing a Decision Tree
- Visualizing Boundary
- Tree Regression, Regularization, and Over Fitting
- End to End Modeling
- Project HR
- Project HR with Google Colab
Ensemble Machine Learning
- Ensemble Learning Methods Introduction
- Bagging
- Random Forests and Extra-Trees
- AdaBoost
- Gradient Boosting Machine
- XGBoost Installation
- XGBoost
- Project HR - Human Resources Analytics
- Ensemble of Ensembles Part 1
- Ensemble of ensembles Part 2
k-Nearest Neighbours (kNN)
- kNN Introduction
- Project Cancer Detection
- Addition Materials
- Project Cancer Detection Part 1
Unsupervised Learning: Dimensionality Reduction
- Dimensionality Reduction Concept
- PCA Introduction
- Project Wine
- Kernel PCA
- Kernel PCA Demo
- LDA vs PCA
- Project Abalone
Unsupervised Learning: Clustering
- Clustering
- k_Means Clustering
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
Instructors
Mr Rob Percival
Web Developer
Udemy
Other Bachelors
Mr Anthony NG
Instructor
Udemy
Articles
Popular Articles
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