Is there any gender biases during the course admission?

Irrespective of being of any gender, the students are allowed admission.

Who is the professional course instructor?

Jose Portilla is a professional course instructor.

Will students get their money returned if they are unable to complete the course?

This facility is only given post 30 days of the programme enrolment.

Can the Python for Machine Learning & Data Science Masterclass online course be previewed?

Yes, there is a preview video on the homepage of the course.

Python for Machine Learning & Data Science Masterclass by Udemy: Fee, Duration, How to Apply

Python for Machine Learning & Data Science Masterclass

Udemy

Start mastering the critical skills for ML, and data science with the Python for Machine Learning & Data Science Masterclass certification course.

Online

₹ 699 4099

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

particular

details

                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study

                                    Mode of Delivery
                                    Video and Text Based

Introduction to the course

Welcome to the Course!
Course Overview Lecture
Preview
Anaconda Python and Jupyter Install and Setup
Note on Environment Setup - Please read me!
Environment Setup

OPTIONAL: Python Crash Course

OPTIONAL: Python Crash Course
Python Crash Course - Part One
Python Crash Course - Part Two
Python Crash Course - Part Three
Python Crash Course - Exercise Questions
Python Crash Course - Exercise Solutions

Machine Learning Pathway Overview

Machine Learning Pathway Overview

NumPy

Introduction to NumPy
NumPy Arrays
Coding Exercise Check-in: Creating NumPy Arrays
NumPy Indexing and Selection
Coding Exercise Check-in: Selecting Data from Numpy Array
NumPy Operations
Check-In: Operations on NumPy Array
NumPy Exercises
Numpy Exercises - Solutions

Pandas

Introduction to Pandas
Series - Part One
Check-in: Labeled Index in Pandas Series
Series - Part Two
DataFrames - Part One - Creating a DataFrame
DataFrames - Part Two - Basic Properties
DataFrames - Part Three - Working with Columns
DataFrames - Part Four - Working with Rows
Pandas - Conditional Filtering
Pandas - Useful Methods - Apply on Single Column
Pandas - Useful Methods - Apply on Multiple Columns
Pandas - Useful Methods - Statistical Information and Sorting
Missing Data - Overview
Missing Data - Pandas Operations
GroupBy Operations - Part One
GroupBy Operations - Part Two - MultiIndex
Combining DataFrames - Concatenation
Combining DataFrames - Inner Merge
Combining DataFrames - Left and Right Merge
Combining DataFrames - Outer Merge
Pandas - Text Methods for String Data
Pandas - Time Methods for Date and Time Data
Pandas Input and Output - CSV Files
Pandas Input and Output - HTML Tables
Pandas Input and Output - Excel Files
Pandas Input and Output - SQL Databases
Pandas Pivot Table
Pandas Project Exercise Overview
Pandas Project Exercise Solutions

Matplotlib

Introduction to Matplotlib
Matplotlib Basics
Matplotlib - Understanding the Figure Object
Matplotlib - Implementing Figures and Axes
Matplotlib - Figure Parameters
Matplotlib - Suboplots Functionality
Matplotlib Styling - Legends
Advanced Matplotlib Commands (Optional)
Matplotlib Exercise Questions Overview
Matplotlib Exercise Questions - Solutions

Seaborn Data Visualizations

Introduction to Seaborn
Scatterplots with Seaborn
Distribution Plots - Part One - Understanding Plot Types
Distribution Plots - Part Two - Coding with Seaborn
Categorical Plots - Statistics within Categories - Understanding Plot Types
Categorical Plots - Statistics within Categories - Coding with Seaborn
Categorical Plots - Distributions within Categories - Understanding Plot Types
Categorical Plots - Distributions within Categories - Coding with Seaborn
Seaborn - Comparison Plots - Understanding the Plot Types
Seaborn - Comparison Plots - Coding with Seaborn
Seaborn Grid Plots
Seaborn - Matrix Plots
Seaborn Plot Exercises Overview
Seaborn Plot Exercises Solutions

Data Analysis and Visualization Capstone Exercises

Capstone Projects Overview
Capstone Project Solutions - Part One
Capstone Project Solutions - Part Two
Capstone Project Solutions - Part Three

Machine Learning Concepts Overview

Introduction to Machine Learning Overview Section
Why Machine Learning?
Types of Machine Learning Algorithms
Supervised Machine Learning Process
Companion Book - Introduction to Statistical Learning

Linear Regression

Introduction to Linear Regression Section
Linear Regression - Algorithm History
Linear Regression - Understanding Ordinary Least Squares
Linear Regression - Cost Functions
Linear Regression - Gradient Descent
Python coding Simple Linear Regression
Overview of Scikit-Learn and Python
Linear Regression - Scikit-Learn Train Test Split
Linear Regression - Scikit-Learn Performance Evaluation - Regression
Linear Regression - Residual Plots
Linear Regression - Model Deployment and Coefficient Interpretation
Polynomial Regression - Theory and Motivation
Polynomial Regression - Creating Polynomial Features
Polynomial Regression - Training and Evaluation
Bias Variance Trade-Off
Polynomial Regression - Choosing Degree of Polynomial
Polynomial Regression - Model Deployment
Regularization Overview
Feature Scaling
Introduction to Cross-Validation
Regularization Data Setup
L2 Regularization - Ridge Regression - Python Implementation
L1 Regularization - Lasso Regression - Background and Implementation
L1 and L2 Regularization - Elastic Net
Linear Regression Project - Data Overview

Feature Engineering & Data Preparation

A note from Jose on Feature Engineering and Data Preparation
Introduction to Feature Engineering and Data Preparation
Dealing with Outliers
Dealing with Missing Data: Part One - Evaluation of Missing Data
Dealing with Missing Data: Part Two - Filling or Dropping data based on Rows
Dealing with Missing Data: Part 3 - Fixing data based on Columns
Dealing with Categorical Data - Encoding Options

Cross Validations, Grid Search, And the Linear Regression Project

Section Overview and Introduction
Cross-Validation - Test | Train Split
Cross-Validation - Test | Validation | Train Split
Cross-Validation - cross_val_score
Cross-Validation - cross_validate
Grid Search
Linear Regression Project Overview
Linear Regression Project - Solutions

Logistic Regression

Early Bird Note on Downloading .zip for Logistic Regression Notes
Introduction to Logistic Regression Section
Logistic Regression - Theory and Intuition - Part One: The Logistic Function
Logistic Regression - Theory and Intuition - Part Two: Linear to Logistic
Logistic Regression - Theory and Intuition - Linear to Logistic Math
Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood
Logistic Regression with Scikit-Learn - Part One - EDA
Logistic Regression with Scikit-Learn - Part Two - Model Training
Classification Metrics - Confusion Matrix and Accuracy
Classification Metrics - Precision, Recall, F1-Score
Classification Metrics - ROC Curves
Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation
Multi-Class Classification with Logistic Regression - Part One - Data and EDA
Multi-Class Classification with Logistic Regression - Part Two - Model
Logistic Regression Exercise Project Overview
Logistic Regression Project Exercise - Solutions

KNN - K Nearest Neighbours

Introduction to KNN Section
KNN Classification - Theory and Intuition
KNN Coding with Python - Part One
KNN Coding with Python - Part Two - Choosing K
KNN Classification Project Exercise Overview
KNN Classification Project Exercise Solutions

Support Vector Machines

Introduction to Support Vector Machines
History of Support Vector Machines
SVM - Theory and Intuition - Hyperplanes and Margins
SVM - Theory and Intuition - Kernel Intuition
SVM - Theory and Intuition - Kernel Trick and Mathematics
SVM with Scikit-Learn and Python - Classification Part One
SVM with Scikit-Learn and Python - Classification Part Two
SVM with Scikit-Learn and Python - Regression Tasks
Support Vector Machine Project Overview
Support Vector Machine Project Solutions

Tree Based Methods: Decision Tree Learning

Introduction to Tree-Based Methods
Decision Tree - History
Decision Tree - Terminology
Decision Tree - Understanding Gini Impurity
Constructing Decision Trees with Gini Impurity - Part One
Constructing Decision Trees with Gini Impurity - Part Two
Coding Decision Trees - Part One - The Data
Coding Decision Trees - Part Two -Creating the Model

Random Forest

Introduction to Random Forests Section
Random Forests - History and Motivation
Random Forests - Key Hyperparameters
Random Forests - Number of Estimators and Features in Subsets
Random Forests - Bootstrapping and Out-of-Bag Error
Coding Classification with Random Forest Classifier - Part One
Coding Classification with Random Forest Classifier - Part Two
Coding Regression with Random Forest Regressor - Part One - Data
Coding Regression with Random Forest Regressor - Part Two - Basic Models
Coding Regression with Random Forest Regressor - Part Three - Polynomials
Coding Regression with Random Forest Regressor - Part Four - Advanced Models

Boosting Methods

Introduction to Boosting Section
Boosting Methods - Motivation and History
AdaBoost Theory and Intuition
AdaBoost Coding Part One - The Data
AdaBoost Coding Part Two - The Model
Gradient Boosting Theory
Gradient Boosting Coding Walkthrough

Supervised Learning Capstone Project - Cohort Analysis And Tree Based Methods

Introduction to Supervised Learning Capstone Project
Solution Walkthrough - Supervised Learning Project - Data and EDA
Solution Walkthrough - Supervised Learning Project - Cohort Analysis
Solution Walkthrough - Supervised Learning Project - Tree Models

Naive Bayes Classification and Natural Language Processing

Introduction to NLP and Naive Bayes Section
Naive Bayes Algorithm - Part One - Bayes Theorem
Naive Bayes Algorithm - Part Two - Model Algorithm
Feature Extraction from Text - Part One - Theory and Intuition
Feature Extraction from Text - Coding Count Vectorization Manually
Feature Extraction from Text - Coding with Scikit-Learn
Natural Language Processing - Classification of Text - Part One
Natural Language Processing - Classification of Text - Part Two
Text Classification Project Exercise Overview
Text Classification Project Exercise Solutions

Unsupervised Learning

Unsupervised Learning Overview

K-Means Clustering

Introduction to K-Means Clustering Section
Clustering General Overview
K-Means Clustering Theory
K-Means Clustering - Coding Part One
K-Means Clustering Coding Part Two
K-Means Clustering Coding Part Three
K-Means Color Quantization - Part One
K-Means Color Quantization - Part Two
K-Means Clustering Exercise Overview
K-Means Clustering Exercise Solution - Part One
K-Means Clustering Exercise Solution - Part Two
K-Means Clustering Exercise Solution - Part Three

Hierarchical Clustering

Introduction to Hierarchical Clustering
Hierarchical Clustering - Theory and Intuition
Hierarchical Clustering - Coding Part One - Data and Visualization
Hierarchical Clustering - Coding Part Two - Scikit-Learn

DBSCAN - Density-Based spatial clustering of applications with noise

Introduction to DBSCAN Section
DBSCAN - Theory and Intuition
DBSCAN versus K-Means Clustering
DBSCAN - Hyperparameter Theory
DBSCAN - Hyperparameter Tuning Methods
DBSCAN - Outlier Project Exercise Overview
DBSCAN - Outlier Project Exercise Solutions

PCA - Principal Component Analysis And Manifold Learning

Introduction to Principal Component Analysis
PCA Theory and Intuition - Part One
PCA Theory and Intuition - Part Two
PCA - Manual Implementation in Python
PCA - SciKit-Learn
PCA - Project Exercise Overview
PCA - Project Exercise Solution

Model Deployment

Model Deployment Section Overview
Model Deployment Considerations
Model Persistence
Model Deployment as an API - General Overview
Note on Upcoming Video
Model API - Creating the Script
Testing the API

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