Python Machine Learning Crash Course for Beginners

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Course curriculum

1. Introduction to the Course
2. Introduction To Instructor
3. Focus of the Course
4. Python Practical of the Course
1. Machine Learning Applications-Part 1
2. Machine Learning Applications-Part 2
3. Why Machine Learning is Trending Now
1. Supervised Learning
2. UnSupervised Learning and Reinforcement Learning
1. Features
2. Features Practice with Python
3. Regression
4. Regression Practice with Python
5. Classsification
6. Classification Practice with Python
7. Clustering
8. Clustering Practice with Python
1. Handling Image Data
2. Handling Video and Audio Data
3. Handling Text Data
4. One Hot Encoding
5. Data Standardization
1. Machine Learning Model 1
2. Machine Learning Model 2
3. Machine Learning Model 3
4. Training Process, Error, Cost and Loss
5. Optimization
1. Linear Regression from Scratch- Part 1
2. Linear Regression from Scratch- Part 2
3. Minimun-to-mean Distance Classifier from Scratch- Part 1
4. Minimun-to-mean Distance Classifier from Scratch- Part 2
5. K-means Clustering from Scratch- Part 1
6. K-means Clustering from Scratch- Part 2
1. Overfitting Introduction
2. Overfitting example on Python
3. Regularization
4. Generalization
5. Data Snooping and the Test Set
6. Cross-validation
1. The Accuracy
2. The Confusion Matrix
1. The Curse of Dimensionality
2. The Principal Component Analysis (PCA)
1. Introduction to Deep Neural Networks (DNN)
2. Introduction to Convolutional Neural Networks (CNN)
3. Introduction to Recurrent Neural Networks (CNN)
1. Principal Component Analysis (PCA) with Python
2. Pipeline in Scikit-Learn for Machine Learning Project
3. Cross-validation with Python
4. Face Recognition Project with Python
1. Mathematical Wrap-up on Machine Learning

About this course

$199.99
51 lessons
10 hours of video content

Python Machine Learning Crash Course for Beginners

Course curriculum

Introduction to the Course

Why Machine Learning

Process of Learning from Data

Machine Learning Methods

Data Preparation and Preprocessing

Machine Learning Models and Optimization

Building Machine Learning Model from Scratch

Overfitting, Underfitting and Generalization

Machine Learning Model Performance Metrics

Dimensionality Reduction

Deep Learning Overview

Hands-on Machine Learning Project Using Scikit-Learn

OPTIONAL Section- Mathematics Wrap-up

About this course