Advanced Machine Learning with Python by Start-Tech Academy

Advanced Machine Learning with Python

Covers classification models such as Logistic regression, LDA, KNN, advanced ML models such as Decision Trees and SVMs

What's included?

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Contents

Basics of Statistics
Types of Data
5 mins
Types of Statistics
3 mins
Describing data Graphically
12 mins
Measures of Centers
8 mins
Measures of Dispersion
5 mins
Setting up Python and Python Crash course
Installing Python and Anaconda
4 mins
Opening Jupyter Notebook
10 mins
Introduction to Jupyter
14 mins
Arithmetic operators in Python: Python Basics
5 mins
Strings in Python: Python Basics
20 mins
Lists, Tuples and Directories: Python Basics
19 mins
Working with Numpy Library of Python
12 mins
Working with Pandas Library of Python
10 mins
Working with Seaborn Library of Python
9 mins
Introduction to Machine Learning
Introduction to Machine Learning
9 mins
Building a Machine Learning model
17 mins
Data Preprocessing
Gathering Business Knowledge
4 mins
Data Exploration
4 mins
The Dataset and the Data Dictionary
9 mins
Data Import in Python
5 mins
Univariate analysis and EDD
4 mins
EDD in Python
19 mins
Outlier Treatment
5 mins
Outlier treatment in Python
10 mins
Missing Value Imputation
4 mins
Missing Value Imputation in Python
5 mins
Seasonality in Data
4 mins
Variable Transformation
2 mins
Variable transformation and Deletion in Python
5 mins
Dummy variable creation: Handling qualitative data
5 mins
Dummy variable creation in Python
6 mins
Classification Models: Logistic, LDA & KNN
Three Classifiers and the problem statement
4 mins
Why can't we use Linear Regression?
5 mins
Logistic Regression
8 mins
Training a Simple Logistic Model in Python
13 mins
Result of Simple Logistic Regression
6 mins
Logistic with multiple predictors
3 mins
Training multiple predictor Logistic model in Python
7 mins
Confusion Matrix
4 mins
Making Confusion Matrix in Python
10 mins
Evaluating performance of model
8 mins
Evaluating model performance in Python
3 mins
Linear Discriminant Analysis
10 mins
LDA in Python
3 mins
Test-Train Split
10 mins
Test-Train Split in Python
7 mins
K-Nearest Neighbors classifier
9 mins
K-Nearest Neighbors in Python: Part 1
6 mins
K-Nearest Neighbors in Python: Part 2
7 mins
Understanding the results of classification models
7 mins
Summary of the three models
5 mins
Decision Trees
Basics of decision trees
11 mins
Understanding a Regression Tree
11 mins
The stopping criteria for controlling tree growth
4 mins
The Data set for the Course
3 mins
Importing Data in Python
6 mins
Missing value treatment in Python
4 mins
Dummy Variable creation in Python
5 mins
Dependent- Independent Data split in Python
5 mins
Test-Train split in Python
7 mins
Creating Decision tree in Python
4 mins
Evaluating model performance in Python
5 mins
Plotting decision tree in Python
5 mins
Pruning a tree
5 mins
Pruning a tree in Python
11 mins
Classification tree
7 mins
The Data set for Classification problem
2 mins
Classification tree in Python : Preprocessing
9 mins
Classification tree in Python : Training
14 mins
Advantages and Disadvantages of Decision Trees
2 mins
Ensemble technique 1 - Bagging
7 mins
Ensemble technique 1 - Bagging in Python
12 mins
Ensemble technique 2 - Random Forests
4 mins
Ensemble technique 2 - Random Forests in Python
7 mins
Using Grid Search in Python
13 mins
Ensemble technique 3 - Boosting
8 mins
Ensemble technique 3a - Boosting in Python
6 mins
Ensemble technique 3b - AdaBoost in Python
4 mins
Ensemble technique 3c - XGBoost in Python
12 mins
Support Vector Machines
Topic flow
2 mins
The Concept of a Hyperplane
5 mins
Maximum Margin Classifier
4 mins
Limitations of Maximum Margin Classifier
3 mins
Support Vector classifiers
10 mins
Limitations of Support Vector Classifiers
2 mins
Kernel Based Support Vector Machines
7 mins
Regression and Classification Models
1 min
Standardizing the data
7 mins
SVM based Regression Model in Python
11 mins
Standardizing the data for classification models
2 mins
Classification Model with Linear Kernel
12 mins
Hyperparameter Tuning with Grid Search
10 mins
Polynomial Kernel with Hyperparameter Tuning
5 mins
Radial Kernel with Hyperparameter Tuning
7 mins