IIRF Online > Development > Data Science > Machine Learning > The Complete Machine Learning Course with Python
The Complete Machine Learning Course with Python by Udemy
Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!
Course Highlights
- Machine Learning Engineers earn on average $166,000 - become an ideal candidate with this course!
- Solve any problem in your business, job or personal life with powerful Machine Learning models
- Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
- Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning etc
Skills you will learn!
Curriculum
3 Topics
What Does the Course Cover?
How to Succeed in This Course
Project Files and Resources
6 Topics
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
19 Topics
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
12 Topics
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
5 Topics
Support Vector Machine (SVM) Concepts
Linear SVM Classification
Polynomial Kernel
Radial Basis Function
Support Vector Regression
7 Topics
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
10 Topics
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
4 Topics
kNN Introduction
Project Cancer Detection
Addition Materials
Project Cancer Detection Part 1
7 Topics
Dimensionality Reduction Concept
PCA Introduction
Project Wine
Kernel PCA
Kernel PCA Demo
LDA vs PCA
Project Abalone
2 Topics
Clustering
k_Means Clustering
5 Topics
Estimating Simple Function with Neural Networks
Neural Network Architecture
Motivational Example - Project MNIST
Binary Classification Problem
Natural Language Processing - Binary Classification
14 Topics
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
17 Topics
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
The Complete Machine Learning Course with Python
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