Machine Learning Practical Course

Learn Machine Learning using PYTHON and SKLEARN

What you'll learn

Python for Machine Learning

Machine Learning

Basic Data Science

Hands-on ML

Requirements

Basic Python Coding Skill

Description

Python, Machine Learning, Scikit Learn, Algorithms, Classification, Machine Learning Case Study, Dataset, Machine Learning Techniques, Machine Learning Terms, Google CollabWelcome to our innovative and practical Python-based machine learning course! This course is specifically designed to equip you with the skills needed for developing intrusion detection systems using machine learning technology. With a primary focus on the Python programming language and leveraging the scikit-learn (sklearn) library, this course provides a robust foundation for understanding machine learning concepts and their real-world applications.You will gain expertise in implementing machine learning techniques using the scikit-learn library, delving into profound insights from the Intrusion Detection System dataset, which serves as the primary case study. Throughout the course, you'll develop a deep understanding of machine learning algorithms, data preprocessing, and model evaluation, learning how to apply these concepts effectively in the context of intrusion detection.Combining structured theory and hands-on labs, this course not only enhances your knowledge of machine learning but also instills confidence to tackle professional challenges. The certificate earned upon completion adds significant value to your profile. Join now to seize better career opportunities in the field of machine learning and become an expert in intrusion detection using Python and scikit-learn.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 What exactly machine learning is?

Lecture 3 Machine Learning Types

Lecture 4 ML Tools

Section 2: Basic Knowledge & Practice

Lecture 5 Dataset Repository

Lecture 6 Load the dataset

Lecture 7 Basic Commands / Data Preparation

Lecture 8 Basic Data Visualization

Lecture 9 Train Test Split

Lecture 10 Algorithms implementation

Section 3: Introduction to Algorithms / Modeling

Lecture 11 Decision Tree Classification

Lecture 12 Support Vector Classification

Lecture 13 Random Forest Classification

Lecture 14 Xgb Classification

Lecture 15 Gradient Boosting Classification

Lecture 16 Neural Network Classification

Lecture 17 Logistic Regression & KNN

Lecture 18 Add other performance metrics

Section 4: [Study Case] Intrusion Detection System (practicing with different datasets)

Lecture 19 Introduction

Lecture 20 Intrusion Detection System

Lecture 21 Quick review about dataset

Lecture 22 Load the Dataset

Lecture 23 Define features' name

Lecture 24 Dataframe info and describe

Lecture 25 Data preprocessing - set class values

Lecture 26 Pie Plot to know the data distribution

Lecture 27 Data Preprocessing - Scaling the data with RobustScaler

Lecture 28 Define x and y variable

Lecture 29 Principal Component Analysis (PCA) - Feature Selection

Lecture 30 Splitting data for training and testing phase

Lecture 31 Create a method as classifier executor

Lecture 32 Classifiers / Algorithms implementation

Lecture 33 Create a method to list down features importance

Lecture 34 Plot tree

Lecture 35 Other Algorithms - Random Forest

Lecture 36 XGBoost (Regressor, Classifier, Plot Legend Actual vs Predicted value)

Lecture 37 The use of reduced data

Lecture 38 All Algorithms score comparison in bar chart

Lecture 39 Load saved input and model then making new prediction

Lecture 40 Cross Validation

Lecture 41 Gridsearch CV

Section 5: Nice to know (short brief only, no practice)

Lecture 42 Clustering

Lecture 43 K-Means

Lecture 44 Bias and Variance

Lecture 45 Bootstrap Aggregating (Bagging)

Lecture 46 Boosting

Lecture 47 Ensemble Modeling

Lecture 48 Model Optimization

Anyone who wants to learn machine learning practically