Machine Learning Masterclass | Course Details

01

Introduction to Machine Learning

3 lessons

▾

Introduction to Machine Learning – Fundamentals

Introduction to Machine Learning – Key Concepts

Introduction to Machine Learning – Hands-on Practice

02

Types of Machine Learning

3 lessons

▾

Types of Machine Learning – Fundamentals

Types of Machine Learning – Key Concepts

Types of Machine Learning – Hands-on Practice

03

Python Libraries for ML (NumPy, Pandas)

4 lessons

▾

Python Libraries for ML (NumPy, Pandas) – Fundamentals

Python Libraries for ML (NumPy, Pandas) – Key Concepts

Python Libraries for ML (NumPy, Pandas) – Hands-on Practice

Python Libraries for ML (NumPy, Pandas) – Quiz & Review

04

Data Preprocessing

3 lessons

▾

Data Preprocessing – Fundamentals

Data Preprocessing – Key Concepts

Data Preprocessing – Hands-on Practice

05

Feature Scaling & Selection

3 lessons

▾

Feature Scaling & Selection – Fundamentals

Feature Scaling & Selection – Key Concepts

Feature Scaling & Selection – Hands-on Practice

06

Linear Regression

4 lessons

▾

Linear Regression – Fundamentals

Linear Regression – Key Concepts

Linear Regression – Hands-on Practice

Linear Regression – Quiz & Review

07

Logistic Regression

3 lessons

▾

Logistic Regression – Fundamentals

Logistic Regression – Key Concepts

Logistic Regression – Hands-on Practice

08

Decision Trees

3 lessons

▾

Decision Trees – Fundamentals

Decision Trees – Key Concepts

Decision Trees – Hands-on Practice

09

Random Forest

4 lessons

▾

Random Forest – Fundamentals

Random Forest – Key Concepts

Random Forest – Hands-on Practice

Random Forest – Quiz & Review

10

Support Vector Machines

3 lessons

▾

Support Vector Machines – Fundamentals

Support Vector Machines – Key Concepts

Support Vector Machines – Hands-on Practice

11

K-Nearest Neighbors

3 lessons

▾

K-Nearest Neighbors – Fundamentals

K-Nearest Neighbors – Key Concepts

K-Nearest Neighbors – Hands-on Practice

12

Naive Bayes

4 lessons

▾

Naive Bayes – Fundamentals

Naive Bayes – Key Concepts

Naive Bayes – Hands-on Practice

Naive Bayes – Quiz & Review

13

Clustering (K-Means)

3 lessons

▾

Clustering (K-Means) – Fundamentals

Clustering (K-Means) – Key Concepts

Clustering (K-Means) – Hands-on Practice

14

Dimensionality Reduction (PCA)

3 lessons

▾

Dimensionality Reduction (PCA) – Fundamentals

Dimensionality Reduction (PCA) – Key Concepts

Dimensionality Reduction (PCA) – Hands-on Practice

15

Model Evaluation & Validation

4 lessons

▾

Model Evaluation & Validation – Fundamentals

Model Evaluation & Validation – Key Concepts

Model Evaluation & Validation – Hands-on Practice

Model Evaluation & Validation – Quiz & Review

16

Hyperparameter Tuning

3 lessons

▾

Hyperparameter Tuning – Fundamentals

Hyperparameter Tuning – Key Concepts

Hyperparameter Tuning – Hands-on Practice

17

Ensemble Learning

3 lessons

▾

Ensemble Learning – Fundamentals

Ensemble Learning – Key Concepts

Ensemble Learning – Hands-on Practice

18

Introduction to Scikit-learn

4 lessons

▾

Introduction to Scikit-learn – Fundamentals

Introduction to Scikit-learn – Key Concepts

Introduction to Scikit-learn – Hands-on Practice

Introduction to Scikit-learn – Quiz & Review

19

Handling Overfitting & Underfitting

3 lessons

▾

Handling Overfitting & Underfitting – Fundamentals

Handling Overfitting & Underfitting – Key Concepts

Handling Overfitting & Underfitting – Hands-on Practice

20

Capstone ML Project

3 lessons

▾

Capstone ML Project – Fundamentals

Capstone ML Project – Key Concepts

Capstone ML Project – Hands-on Practice

Machine Learning

What you'll learn

Course Curriculum

Requirements

What you'll get