The Complete Machine Learning Course with Python

Video description

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


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.

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.

Table of contents

  1. Chapter 1 : Introduction
    1. What Does the Course Cover?
  2. Chapter 2 : Getting Started with Anaconda
    1. [Windows OS] Downloading Installing Anaconda
    2. [Windows OS] Managing Environment
    3. Navigating the Spyder Jupyter Notebook Interface
    4. Downloading the IRIS Datasets
    5. Data Exploration and Analysis
    6. Presenting Your Data
  3. Chapter 3 : Regression
    1. Introduction
    2. Categories of Machine Learning
    3. Working with Scikit-Learn
    4. Boston Housing Data - EDA
    5. Correlation Analysis and Feature Selection
    6. Simple Linear Regression Modelling with Boston Housing Data
    7. Robust Regression
    8. Evaluate Model Performance
    9. Multiple Regression with statsmodel
    10. Multiple Regression and Feature Importance
    11. Ordinary Least Square Regression and Gradient Descent
    12. Regularised Method for Regression
    13. Polynomial Regression
    14. Dealing with Non-linear relationships
    15. Feature Importance Revisited
    16. Data Pre-Processing 1
    17. Data Pre-Processing 2
    18. Variance Bias Trade Off - Validation Curve
    19. Variance Bias Trade Off - Learning Curve
    20. Cross Validation
  4. Chapter 4 : Classification
    1. Introduction
    2. Logistic Regression 1
    3. Logistic Regression 2
    4. MNIST Project 1 - Introduction
    5. MNIST Project 2 - SGDClassifiers
    6. MNIST Project 3 - Performance Measures
    7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score
    8. MNIST Project 5 - Precision and Recall Tradeoff
    9. MNIST Project 6 - The ROC Curve
  5. Chapter 5 : Support Vector Machine (SVM)
    1. Introduction
    2. Support Vector Machine (SVM) Concepts
    3. Linear SVM Classification
    4. Polynomial Kernel
    5. Gaussian Radial Basis Function
    6. Support Vector Regression
    7. Advantages and Disadvantages of SVM
  6. Chapter 6 : Tree
    1. Introduction
    2. What is Decision Tree
    3. Training a Decision Tree
    4. Visualising a Decision Trees
    5. Decision Tree Learning Algorithm
    6. Decision Tree Regression
    7. Overfitting and Grid Search
    8. Where to From Here
    9. Project HR - Loading and preprocesing data
    10. Project HR - Modelling
  7. Chapter 7 : Ensemble Machine Learning
    1. Introduction
    2. Ensemble Learning Methods Introduction
    3. Bagging Part 1
    4. Bagging Part 2
    5. Random Forests
    6. Extra-Trees
    7. AdaBoost
    8. Gradient Boosting Machine
    9. XGBoost
    10. Project HR - Human Resources Analytics
    11. Ensemble of ensembles Part 1
    12. Ensemble of ensembles Part 2
  8. Chapter 8 : k-Nearest Neighbours (kNN)
    1. kNN Introduction
    2. kNN Concepts
    3. kNN and Iris Dataset Demo
    4. Distance Metric
    5. Project Cancer Detection Part 1
    6. Project Cancer Detection Part 2
  9. Chapter 9 : Dimensionality Reduction
    1. Introduction
    2. Dimensionality Reduction Concept
    3. PCA Introduction
    4. Dimensionality Reduction Demo
    5. Project Wine 1: Dimensionality Reduction with PCA
    6. Project Wine 2: Choosing the Number of Components
    7. Kernel PCA
    8. Kernel PCA Demo
    9. LDA Comparison between LDA and PCA
  10. Chapter 10 : Unsupervised Learning: Clustering
    1. Introduction
    2. Clustering Concepts
    3. MLextend
    4. Ward’s Agglomerative Hierarchical Clustering
    5. Truncating Dendrogram
    6. k-Means Clustering
    7. Elbow Method
    8. Silhouette Analysis
    9. Mean Shift

Product information

  • Title: The Complete Machine Learning Course with Python
  • Author(s): Anthony NG, Rob Percival
  • Release date: October 2018
  • Publisher(s): Packt Publishing
  • ISBN: 9781789953725