Introduction To Machine Learning

Introduction

In this series I am going to cover a bunch of Machine Learning algorithms and their explanation with real life examples. Using Python and SciKit-Learn.

Prerequisites

Basic core Python knowledge is required, also requires basic Pandas knowledge such as column selection, csv file reading functionalities.

Before moving to the next step make sure you have the following things installed on your pc:

1. Python3 
2. Pandas – Pandas is a python library used to manipulate datasets. Install pandas using pip install pandas command
3. SciKit-Learn – Scikit-learn or sklearn is a free python library for machine learning.Install sklearn using pip install scikit-learn
4. Matplotlib – Matplotlib is a data visualization and graphical plotting library for Python. Install matplotlib using the following command pip install matplotlib

What is machine learning?

Machine learning is a subset of AI. ML enables learning capabilities for computers like how humans learn something. and it is used to extract knowledge from data and this knowledge is used to make decisions for computers without following any explicit instructions. 

Machine learning can be used to solve a wide range of problems, such as image recognition, natural language processing, predictive analytics, and anomaly detection. It involves various techniques, such as supervised, unsupervised, and reinforcement learning.

Real life ML use cases

1. Voice Assistant

2. Bank Fraud Detection

3. Sentiment Analysis

4. Email Spam and Malware Filtering

5. Image Recognition

6. Speech Recognition

What are the different types of machine learnings?

1. Supervised learning – algorithms are used to train with labeled data. model predicts the output. (e.x) Regression and Classification
2. Unsupervised learning – algorithms are trained using unlabeled data. used to find hidden patterns in data (e.x) Clustering
3. Reinforcement learning – involves training an algorithm to make decisions based on feedback from its environment (e.x) Adaptive user interfaces

Table of content

Regression Models

1. Fundamentals of Regression 
2. Simple Linear Regression 
3. Multiple Linear Regression 
4. Polynomial Regression 
5. Support Vector Regression 
6. Decision Tree Regression 
7. Random Forest Regression 
8. Regression Model Regularization

Classification Models

1. Fundamentals of Classification
2. Support Vector Classifier 
3. Native Bayes Classifier 
4. K-Nearest Neighbors 
5. Logistic Regression 
6. Random Forest Classifier

Cluster Models

1. Fundamentals of Clustering
2. K-Means Clustering 
3. Hierarchical Clustering 
4. Gaussian Mixture Models (GMM)

Useful Topics

1. Dimensionality Reduction Algorithms 
2. Data Cleaning 
3. Data Scaling 
4. Cross Validation 
5. Streamlit Data App 
6. Image Processing

Conclusion

In this blog I have given a simple introduction to machine learning, its prerequisites, different types in ML and table of contents. The next blog will start with the Fundamentals of Regression.