This Machine Learning with Python Tutorial, whether you’re a beginner or an experienced professional, this tutorial will provide you with a solid foundation in the fundamentals of machine learning using Python.
In this tutorial, we’ll cover a wide range of topics, including the basics of Python programming and Machine learning, Data processing, Supervised learning, Unsupervised Learning, and more, where you’ll also be creating some projects using it.
What is Machine Learning?
Machine Learning is the ability of the computer to learn without being explicitly programmed. In layman’s terms, it can be described as automating the learning process of computers based on their experiences without any human assistance. Machine learning is actively used in our daily life and perhaps in more places than one would expect.
Introduction
- Getting Started with Machine Learning
- An Introduction to Machine Learning
- What is Machine Learning ?
- Introduction to Data in Machine Learning
- ML – Applications
- Difference between Machine learning and Artificial Intelligence
- 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
- Python | Linear Regression using sklearn
- Linear Regression Using Tensorflow
- Linear Regression using PyTorch
- Pyspark | Linear regression using Apache MLlib
- Boston Housing Kaggle Challenge with Linear Regression
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?
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