Machine Learning with Python Tutorial
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
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
GeeksforGeeks Courses
Machine Learning Basic and Advanced – Self Paced Course
Understanding the core idea of building systems has now become easier. With our Machine Learning Basic and Advanced – Self Paced Course, you will not only learn about the concepts of machine learning but will gain hands-on experience implementing effective techniques. This Machine Learning course will provide you with the skills needed to become a successful Machine Learning Engineer today. Enrol now!
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.
