Python language is widely used in Machine Learning because it provides libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and Keras. These libraries offer tools and functions essential for data manipulation, analysis, and building machine learning models. It is well-known for its readability and offers platform independence. These all things make it the perfect language of choice for Machine Learning.

Machine Learning is a subdomain of artificial intelligence. It allows computers to learn and improve from experience without being explicitly programmed, and It is designed in such a way that allows systems to identify patterns, make predictions, and make decisions based on data.

So, let's start Python Machine Learning guide to learn more about ML.

Introduction

• Introduction to Machine Learning
• What is Machine Learning?
• ML – Applications
• Difference between ML and AI
• Best Python Libraries for Machine Learning

Data Processing

• Understanding Data Processing
• Generate test datasets
• Create Test DataSets using Sklearn
• Data Preprocessing
• Data Cleansing
• Label Encoding of datasets
• One Hot Encoding of datasets
• Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python

Supervised learning

• Types of Learning – Supervised Learning
• Getting started with Classification
• Types of Regression Techniques
• Classification vs Regression

Linear Regression

• Introduction to Linear Regression
• Implementing Linear Regression
• Univariate Linear Regression
• Multiple Linear Regression
• Linear Regression using sklearn
• Linear Regression Using Tensorflow
• Linear Regression using PyTorch
• Boston Housing Kaggle Challenge with Linear Regression [Project]

Polynomial Regression

• Polynomial Regression ( From Scratch using Python )
• Polynomial Regression
• Polynomial Regression for Non-Linear Data
• Polynomial Regression using Turicreate

Logistic Regression

• Understanding Logistic Regression
• Implementing Logistic Regression
• Logistic Regression using Tensorflow
• Softmax Regression using TensorFlow
• Softmax Regression Using Keras

Naive Bayes

• Naive Bayes Classifiers
•  Naive Bayes Scratch Implementation using Python
• Complement Naive Bayes (CNB) Algorithm
• Applying Multinomial Naive Bayes to NLP Problems

Support Vector

• Support Vector Machine Algorithm
• Support Vector Machines(SVMs) in Python
• SVM Hyperparameter Tuning using GridSearchCV
• Creating linear kernel SVM in Python
• Major Kernel Functions in Support Vector Machine (SVM)
• Using SVM to perform classification on a non-linear dataset

Decision Tree

• Decision Tree
• Implementing Decision tree
• Decision Tree Regression using sklearn

Random Forest

• Random Forest Regression in Python
• Random Forest Classifier using Scikit-learn
• Hyperparameters of Random Forest Classifier
• Voting Classifier using Sklearn
• Bagging classifier

K-nearest neighbor (KNN)

• K Nearest Neighbors with Python | ML
• Implementation of K-Nearest Neighbors from Scratch using Python
• K-nearest neighbor algorithm in Python
• Implementation of KNN classifier using Sklearn
• Imputation using the KNNimputer()
• Implementation of KNN using OpenCV

Unsupervised Learning

• Types of Learning – Unsupervised Learning
• Clustering in Machine Learning
• Different Types of Clustering Algorithm
• K means Clustering – Introduction
• Elbow Method for optimal value of k in KMeans
• K-means++ Algorithm
• Analysis of test data using K-Means Clustering in Python
• Mini Batch K-means clustering algorithm
• Mean-Shift Clustering
• DBSCAN – Density based clustering
• Implementing DBSCAN algorithm using Sklearn
• Fuzzy Clustering
• Spectral Clustering
• OPTICS Clustering
• OPTICS Clustering Implementing using Sklearn
• Hierarchical clustering (Agglomerative and Divisive clustering)
• Implementing Agglomerative Clustering using Sklearn
• Gaussian Mixture Model

Projects using Machine Learning

• Rainfall prediction using Linear regression
• Identifying handwritten digits using Logistic Regression in PyTorch
• Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
• Implement Face recognition using k-NN with scikit-learn
• Credit Card Fraud Detection
• Image compression using K-means clustering

Applications of Machine Learning

• How Does Google Use Machine Learning?
• How Does NASA Use Machine Learning?
• Targeted Advertising using Machine Learning
• How Machine Learning Is Used by Famous Companies?

Applications Based on Machine Learning

Machine Learning is the most rapidly evolving technology; we are in the era of AI and ML. It is used to solve many real-world problems which cannot be solved with the standard approach. Following are some applications of ML.

• Sentiment analysis
• Fraud detection
• Error detection and prevention
• Weather forecasting and prediction
• Speech synthesis
• Recommendation of products to customers in online shopping.
• Stock market analysis and forecasting
• Speech recognition
• Fraud prevention
• Customer segmentation
• Object recognition
• Emotion analysis

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Conclusion

Well, this is the end of this write-up here you will get all the details as well as all the resources about machine learning with Python tutorial. We are sure that this Python machine learning guide will provide a solid foundation in the field of machine learning.

What is Machine Learning

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What is Machine Learning

Types of Machine Learning: Supervised, Unsupervised, Reinforcement

Introduction to Linear Regression - Machine Learning

Decision Tree in Machine Learning

Naive Bayes Classifiers

K-Nearest Neighbor(KNN) Algorithm in Machine Learning

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