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?
- 5 Mind-Blowing Ways Facebook Uses 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.
FAQS on Machine Learning with Python
What is ML
Machine learning (ML) is a branch of artificial intelligence (AI) focused on developing algorithms that enable computers to learn from and make predictions based on data.
1. What are the prerequisites for learning machine learning with Python?
Answer: Basic knowledge of Python programming and understanding of mathematical concepts like linear algebra and statistics are beneficial but not mandatory and you can aware of Python, NumPy, Scikit-learn, Scipy, Matplotlib.
2. Can Python be used for other AI tasks besides machine learning?
Answer: Yes, Python is widely used in various AI tasks, such as natural language processing, computer vision, and robotics.
3. How can I stay updated with the latest developments in machine learning?
Answer: Following reputable AI and machine learning websites, attending conferences, and engaging with the community on forums are effective ways to stay up-to-date.
4. How do I start an ML project?
Answer: It can be broken down into 7 major steps :
1. Collecting Data
2. Preparing the Data
3. Choosing a Model
4. Training the Model
5. Evaluating the Model
6. Parameter Tuning
