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