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The Complete Machine Learning Course with Python
By Packt
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Highlights
On-Demand course
18 hours 22 minutes
All levels
Description
Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you. You will train 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 toolsets 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 train/test, K-fold and Stratified K-fold cross-validation to select the 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 more! 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.
What You Will Learn
• Learn to Build Powerful Machine Learning Models to Solve Any Problem
• Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
Audience
A newbie who wants to learn machine learning algorithm with Python. Anyone 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.
Approach
You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.
Key Features
• Solve any problem in your business or job with powerful Machine Learning models * • Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.
Github Repo
https://github.com/packtpublishing/the-complete-machine-learning-course-with-python
About the Author
Anthony Ng
Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.
Course Outline
1. Introduction
1. What Does the Course Cover? Introduction: What Does the Course Cover? |
2. Getting Started with Anaconda
1. [Windows OS] Downloading & Installing Anaconda Getting Started with Anaconda: [Windows OS] Downloading & Installing Anaconda |
2. [Windows OS] Managing Environment Getting Started with Anaconda: [Windows OS] Managing Environment |
3. Navigating the Spyder & Jupyter Notebook Interface Getting Started with Anaconda: Navigating the Spyder & Jupyter Notebook Interface |
4. Downloading the IRIS Datasets Getting Started with Anaconda: Downloading the IRIS Datasets |
5. Data Exploration and Analysis Getting Started with Anaconda: Data Exploration and Analysis |
6. Presenting Your Data Getting Started with Anaconda: Presenting Your Data |
3. Regression
1. Introduction Regression: Introduction |
2. Categories of Machine Learning Regression: Categories of Machine Learning |
3. Working with Scikit-Learn Regression: Working with Scikit-Learn |
4. Boston Housing Data - EDA Regression: Boston Housing Data - EDA |
5. Correlation Analysis and Feature Selection Regression: Correlation Analysis and Feature Selection |
6. Simple Linear Regression Modelling with Boston Housing Data Regression: Simple Linear Regression Modelling with Boston Housing Data |
7. Robust Regression Regression: Robust Regression |
8. Evaluate Model Performance Regression: Evaluate Model Performance |
9. Multiple Regression with statsmodel Regression: Multiple Regression with statsmodel |
10. Multiple Regression and Feature Importance Regression: Multiple Regression and Feature Importance |
11. Ordinary Least Square Regression and Gradient Descent Regression: Ordinary Least Square Regression and Gradient Descent |
12. Regularised Method for Regression Regression: Regularised Method for Regression |
13. Polynomial Regression Regression: Polynomial Regression |
14. Dealing with Non-linear relationships Regression: Dealing with Non-linear relationships |
15. Feature Importance Revisited Regression: Feature Importance Revisited |
16. Data Pre-Processing 1 Regression: Data Pre-Processing 1 |
17. Data Pre-Processing 2 Regression: Data Pre-Processing 2 |
18. Variance Bias Trade Off - Validation Curve Regression: Variance Bias Trade Off - Validation Curve |
19. Variance Bias Trade Off - Learning Curve Regression: Variance Bias Trade Off - Learning Curve |
20. Cross Validation Regression: Cross Validation |
4. Classification
1. Introduction Classification: Introduction |
2. Logistic Regression 1 Classification: Logistic Regression 1 |
3. Logistic Regression 2 Classification: Logistic Regression 2 |
4. MNIST Project 1 - Introduction Classification: MNIST Project 1 - Introduction |
5. MNIST Project 2 - SGDClassifiers Classification: MNIST Project 2 - SGDClassifier |
6. MNIST Project 3 - Performance Measures Classification: MNIST Project 3 - Performance Measures |
7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score |
8. MNIST Project 5 - Precision and Recall Tradeoff Classification: MNIST Project 5 - Precision and Recall Tradeoff |
9. MNIST Project 6 - The ROC Curve Classification: MNIST Project 6 - The ROC Curve |
5. Support Vector Machine (SVM)
1. Introduction Support Vector Machine (SVM): Introduction |
2. Support Vector Machine (SVM) Concepts Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts |
3. Linear SVM Classification Support Vector Machine (SVM): Linear SVM Classification |
4. Polynomial Kernel Support Vector Machine (SVM): Polynomial Kernel |
5. Gaussian Radial Basis Function Support Vector Machine (SVM): Gaussian Radial Basis Function |
6. Support Vector Regression Support Vector Machine (SVM): Support Vector Regression |
7. Advantages and Disadvantages of SVM Support Vector Machine (SVM): Advantages and Disadvantages of SVM |
6. Tree
1. Introduction Tree: Introduction |
2. What is Decision Tree Tree: What is Decision Tree |
3. Training a Decision Tree Tree: Training a Decision Tree |
4. Visualising a Decision Trees Tree: Visualising a Decision Trees |
5. Decision Tree Learning Algorithm Tree: Decision Tree Learning Algorithm |
6. Decision Tree Regression Tree: Decision Tree Regression |
7. Overfitting and Grid Search Tree: Overfitting and Grid Search |
8. Where to From Here Tree: Where to From Here |
9. Project HR - Loading and preprocesing data Tree: Project HR - Loading and preprocesing data |
10. Project HR - Modelling Tree: Project HR - Modelling |
7. Ensemble Machine Learning
1. Introduction Ensemble Machine Learning: Introduction |
2. Ensemble Learning Methods Introduction Ensemble Machine Learning: Ensemble Learning Methods Introduction |
3. Bagging Part 1 Ensemble Machine Learning: Bagging Part 1 |
4. Bagging Part 2 Ensemble Machine Learning: Bagging Part 2 |
5. Random Forests Ensemble Machine Learning: Random Forests |
6. Extra-Trees Ensemble Machine Learning: Extra-Trees |
7. AdaBoost Ensemble Machine Learning: Decision Tree Regression |
8. Gradient Boosting Machine Ensemble Machine Learning: Gradient Boosting Machine |
9. XGBoost Ensemble Machine Learning: XGBoost |
10. Project HR - Human Resources Analytics Ensemble Machine Learning: Project HR - Human Resources Analytics |
11. Ensemble of ensembles Part 1 Ensemble Machine Learning: Ensemble of ensembles Part 1 |
12. Ensemble of ensembles Part 2 Ensemble Machine Learning: Ensemble of ensembles Part 2 |
8. k-Nearest Neighbours (kNN)
1. kNN Introduction K-Nearest Neighbours (kNN): kNN Introduction |
2. kNN Concepts K-Nearest Neighbours (kNN): kNN Concepts |
3. kNN and Iris Dataset Demo K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo |
4. Distance Metric K-Nearest Neighbours (kNN): Distance Metric |
5. Project Cancer Detection Part 1 K-Nearest Neighbours (kNN): Project Cancer Detection Part 1 |
6. Project Cancer Detection Part 2 K-Nearest Neighbours (kNN): Project Cancer Detection Part 2 |
9. Dimensionality Reduction
1. Introduction Dimensionality Reduction: Introduction |
2. Dimensionality Reduction Concept Dimensionality Reduction: Dimensionality Reduction Concept |
3. PCA Introduction Dimensionality Reduction: PCA Introduction |
4. Dimensionality Reduction Demo Dimensionality Reduction: Dimensionality Reduction Demo |
5. Project Wine 1: Dimensionality Reduction with PCA Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA |
6. Project Wine 2: Choosing the Number of Components Dimensionality Reduction: Project Wine 2: Choosing the Number of Components |
7. Kernel PCA Dimensionality Reduction: Kernel PCA |
8. Kernel PCA Demo Dimensionality Reduction: Kernel PCA Demo |
9. LDA & Comparison between LDA and PCA Dimensionality Reduction: LDA & Comparison between LDA and PCA |
10. Unsupervised Learning: Clustering
1. Introduction Unsupervised Learning: Clustering: Introduction |
2. Clustering Concepts Unsupervised Learning: Clustering: Clustering Concepts |
3. MLextend Unsupervised Learning: Clustering: MLextend |
4. Ward's Agglomerative Hierarchical Clustering Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering |
5. Truncating Dendrogram Unsupervised Learning: Clustering: Truncating Dendrogram |
6. k-Means Clustering Unsupervised Learning: Clustering: k-Means Clustering |
7. Elbow Method Unsupervised Learning: Clustering: Elbow Method |
8. Silhouette Analysis Unsupervised Learning: Clustering: Silhouette Analysis |
9. Mean Shift Unsupervised Learning: Clustering: Mean Shift |
Course Content
- The Complete Machine Learning Course with Python
About The Provider
Packt
Birmingham
Founded in 2004 in Birmingham, UK, Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and i...
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