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The Complete Machine Learning Course with Python

Video Description

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

About This Video

  • 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.

In Detail

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.

Table of Contents

  1. Chapter 1 : Introduction
    1. What Does the Course Cover? 00:02:19
  2. Chapter 2 : Getting Started with Anaconda
    1. [Windows OS] Downloading & Installing Anaconda 00:20:16
    2. [Windows OS] Managing Environment 00:06:06
    3. Navigating the Spyder & Jupyter Notebook Interface 00:17:12
    4. Downloading the IRIS Datasets 00:02:59
    5. Data Exploration and Analysis 00:14:20
    6. Presenting Your Data 00:15:05
  3. Chapter 3 : Regression
    1. Introduction 00:06:16
    2. Categories of Machine Learning 00:12:24
    3. Working with Scikit-Learn 00:19:26
    4. Boston Housing Data - EDA 00:20:11
    5. Correlation Analysis and Feature Selection 00:08:47
    6. Simple Linear Regression Modelling with Boston Housing Data 00:13:26
    7. Robust Regression 00:14:23
    8. Evaluate Model Performance 00:19:42
    9. Multiple Regression with statsmodel 00:19:32
    10. Multiple Regression and Feature Importance 00:14:11
    11. Ordinary Least Square Regression and Gradient Descent 00:18:32
    12. Regularised Method for Regression 00:19:09
    13. Polynomial Regression 00:14:31
    14. Dealing with Non-linear relationships 00:10:30
    15. Feature Importance Revisited 00:07:41
    16. Data Pre-Processing 1 00:13:07
    17. Data Pre-Processing 2 00:19:10
    18. Variance Bias Trade Off - Validation Curve 00:16:45
    19. Variance Bias Trade Off - Learning Curve 00:15:04
    20. Cross Validation 00:15:44
  4. Chapter 4 : Classification
    1. Introduction 00:04:15
    2. Logistic Regression 1 00:12:01
    3. Logistic Regression 2 00:16:34
    4. MNIST Project 1 - Introduction 00:13:10
    5. MNIST Project 2 - SGDClassifiers 00:10:26
    6. MNIST Project 3 - Performance Measures 00:12:08
    7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score 00:18:44
    8. MNIST Project 5 - Precision and Recall Tradeoff 00:16:43
    9. MNIST Project 6 - The ROC Curve 00:09:26
  5. Chapter 5 : Support Vector Machine (SVM)
    1. Introduction 00:02:17
    2. Support Vector Machine (SVM) Concepts 00:19:58
    3. Linear SVM Classification 00:11:10
    4. Polynomial Kernel 00:15:25
    5. Gaussian Radial Basis Function 00:12:31
    6. Support Vector Regression 00:06:28
    7. Advantages and Disadvantages of SVM 00:04:44
  6. Chapter 6 : Tree
    1. Introduction 00:02:52
    2. What is Decision Tree 00:16:30
    3. Training a Decision Tree 00:08:22
    4. Visualising a Decision Trees 00:20:31
    5. Decision Tree Learning Algorithm 00:13:42
    6. Decision Tree Regression 00:11:22
    7. Overfitting and Grid Search 00:17:39
    8. Where to From Here 00:05:33
    9. Project HR - Loading and preprocesing data 00:18:07
    10. Project HR - Modelling 00:06:59
  7. Chapter 7 : Ensemble Machine Learning
    1. Introduction 00:02:38
    2. Ensemble Learning Methods Introduction 00:13:42
    3. Bagging Part 1 00:22:21
    4. Bagging Part 2 00:12:13
    5. Random Forests 00:13:51
    6. Extra-Trees 00:07:06
    7. AdaBoost 00:13:01
    8. Gradient Boosting Machine 00:16:14
    9. XGBoost 00:19:36
    10. Project HR - Human Resources Analytics 00:23:16
    11. Ensemble of ensembles Part 1 00:20:03
    12. Ensemble of ensembles Part 2 00:13:01
  8. Chapter 8 : k-Nearest Neighbours (kNN)
    1. kNN Introduction 00:02:04
    2. kNN Concepts 00:07:26
    3. kNN and Iris Dataset Demo 00:08:35
    4. Distance Metric 00:05:31
    5. Project Cancer Detection Part 1 00:20:12
    6. Project Cancer Detection Part 2 00:14:55
  9. Chapter 9 : Dimensionality Reduction
    1. Introduction 00:01:42
    2. Dimensionality Reduction Concept 00:12:39
    3. PCA Introduction 00:17:05
    4. Dimensionality Reduction Demo 00:06:09
    5. Project Wine 1: Dimensionality Reduction with PCA 00:18:20
    6. Project Wine 2: Choosing the Number of Components 00:07:15
    7. Kernel PCA 00:16:17
    8. Kernel PCA Demo 00:07:10
    9. LDA & Comparison between LDA and PCA 00:07:22
  10. Chapter 10 : Unsupervised Learning: Clustering
    1. Introduction 00:01:58
    2. Clustering Concepts 00:08:02
    3. MLextend 00:06:15
    4. Ward’s Agglomerative Hierarchical Clustering 00:16:14
    5. Truncating Dendrogram 00:17:35
    6. k-Means Clustering 00:13:00
    7. Elbow Method 00:06:57
    8. Silhouette Analysis 00:07:42
    9. Mean Shift 00:10:54