Machine Learning

  • Last Updated : 02 Jun, 2022

 
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.

Machine Learning tutorial

Recent Articles on Machine Learning

Machine Learning is an essential skill for any aspiring data analyst and data scientist, and also for those who wish to transform a massive amount of raw data into trends and predictions. Learn this skill today with Machine Learning Foundation – Self Paced Course , designed and curated by industry experts having years of expertise in ML and industry-based projects.

 

Introduction :

  1. Getting Started with Machine Learning
  2. An Introduction to Machine Learning
  3. What is Machine Learning ?
  4. Introduction to Data in Machine Learning
  5. Demystifying Machine Learning
  6. ML – Applications
  7. Best Python libraries for Machine Learning
  8. Artificial Intelligence | An Introduction
  9. Machine Learning and Artificial Intelligence
  10. Difference between Machine learning and Artificial Intelligence
  11. Agents in Artificial Intelligence
  12. 10 Basic Machine Learning Interview Questions

Data and It’s Processing:

  1. Introduction to Data in Machine Learning
  2. Understanding Data Processing
  3. Python | Create Test DataSets using Sklearn
  4. Python | Generate test datasets for Machine learning
  5. Python | Data Preprocessing in Python
  6. Data Cleaning
  7. Feature Scaling – Part 1
  8. Feature Scaling – Part 2
  9. Python | Label Encoding of datasets
  10. Python | One Hot Encoding of datasets
  11. Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python
  12. Dummy variable trap in Regression Models

Supervised learning :

  1. Getting started with Classification
  2. Basic Concept of Classification
  3. Types of Regression Techniques
  4. Classification vs Regression
  5. ML | Types of Learning – Supervised Learning
  6. Multiclass classification using scikit-learn
  9. Python | Implementation of Polynomial Regression
  10. Softmax Regression using TensorFlow
  12. Naive Bayes Classifiers

Unsupervised learning :

  1. ML | Types of Learning – Unsupervised Learning
  2. Supervised and Unsupervised learning
  3. Clustering in Machine Learning
  4. Different Types of Clustering Algorithm
  5. K means Clustering – Introduction
  6. Elbow Method for optimal value of k in KMeans
  7. Random Initialization Trap in K-Means
  8. ML | K-means++ Algorithm
  9. Analysis of test data using K-Means Clustering in Python
  10. Mini Batch K-means clustering algorithm
  11. Mean-Shift Clustering
  12. DBSCAN – Density based clustering
  13. Implementing DBSCAN algorithm using Sklearn
  14. Fuzzy Clustering
  15. Spectral Clustering
  16. OPTICS Clustering
  17. OPTICS Clustering Implementing using Sklearn
  18. Hierarchical clustering (Agglomerative and Divisive clustering)
  19. Implementing Agglomerative Clustering using Sklearn
  20. Gaussian Mixture Model

Reinforcement Learning:

  1. Reinforcement learning
  2. Reinforcement Learning Algorithm : Python Implementation using Q-learning
  3. Introduction to Thompson Sampling
  4. Genetic Algorithm for Reinforcement Learning
  5. SARSA Reinforcement Learning
  6. Q-Learning in Python

Dimensionality Reduction :

  1. Introduction to Dimensionality Reduction
  2. Introduction to Kernel PCA
  3. Principal Component Analysis(PCA)
  4. Principal Component Analysis with Python
  5. Low-Rank Approximations
  6. Overview of Linear Discriminant Analysis (LDA)
  7. Mathematical Explanation of Linear Discriminant Analysis (LDA)
  8. Generalized Discriminant Analysis (GDA)
  9. Independent Component Analysis
  10. Feature Mapping
  11. Extra Tree Classifier for Feature Selection
  12. Chi-Square Test for Feature Selection – Mathematical Explanation
  13. ML | T-distributed Stochastic Neighbor Embedding (t-SNE) Algorithm
  14. Python | How and where to apply Feature Scaling?
  15. Parameters for Feature Selection
  16. Underfitting and Overfitting in Machine Learning

Natural Language Processing :

  1. Introduction to Natural Language Processing
  2. Text Preprocessing in Python | Set – 1
  3. Text Preprocessing in Python | Set 2
  4. Removing stop words with NLTK in Python
  5. Tokenize text using NLTK in python
  6. How tokenizing text, sentence, words works
  7. Introduction to Stemming
  8. Stemming words with NLTK
  9. Lemmatization with NLTK
  10. Lemmatization with TextBlob
  11. How to get synonyms/antonyms from NLTK WordNet in Python?

Neural Networks :

  1. Introduction to Artificial Neutral Networks | Set 1
  2. Introduction to Artificial Neural Network | Set 2
  3. Introduction to ANN (Artificial Neural Networks) | Set 3 (Hybrid Systems)
  4. Introduction to ANN | Set 4 (Network Architectures)
  5. Activation functions
  6. Implementing Artificial Neural Network training process in Python
  7. A single neuron neural network in Python
  11. Introduction to Deep Q-Learning
  12. Implementing Deep Q-Learning using Tensorflow

ML – Deployment :

  1. Deploy your Machine Learning web app (Streamlit) on Heroku
  2. Deploy a Machine Learning Model using Streamlit Library
  3. Deploy Machine Learning Model using Flask
  4. Python – Create UIs for prototyping Machine Learning model with Gradio
  5. How to Prepare Data Before Deploying a Machine Learning Model?
  6. https://www.geeksforgeeks.org/deploying-ml-models-as-api-using-fastapi/?ref=rp
  7. Deploying Scrapy spider on ScrapingHub

ML – Applications :

  1. Rainfall prediction using Linear regression
  2. Identifying handwritten digits using Logistic Regression in PyTorch
  3. Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
  4. Python | Implementation of Movie Recommender System
  5. Support Vector Machine to recognize facial features in C++
  6. Decision Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle)
  7. Credit Card Fraud Detection
  8. NLP analysis of Restaurant reviews
  9. Applying Multinomial Naive Bayes to NLP Problems
  10. Image compression using K-means clustering
  11. Deep learning | Image Caption Generation using the Avengers EndGames Characters
  12. How Does Google Use Machine Learning?
  13. How Does NASA Use Machine Learning?
  14. 5 Mind-Blowing Ways Facebook Uses Machine Learning
  15. Targeted Advertising using Machine Learning
  16. How Machine Learning Is Used by Famous Companies?

Misc :

  1. Pattern Recognition | Introduction
  2. Calculate Efficiency Of Binary Classifier
  3. Logistic Regression v/s Decision Tree Classification
  4. R vs Python in Datascience
  5. Explanation of Fundamental Functions involved in A3C algorithm
  6. Differential Privacy and Deep Learning
  7. Artificial intelligence vs Machine Learning vs Deep Learning
  8. Introduction to Multi-Task Learning(MTL) for Deep Learning
  9. Top 10 Algorithms every Machine Learning Engineer should know
  10. Azure Virtual Machine for Machine Learning
  11. 30 minutes to machine learning
  12. What is AutoML in Machine Learning?
  13. Confusion Matrix in Machine Learning

GeeksforGeeks Courses

Machine Learning Foundation

Machines are learning, so why do you wish to get left behind? Strengthen your ML and AI foundations today and become future ready. This self-paced course will help you learn advanced concepts like- Regression, Classification, Data Dimensionality and much more. Also included- Projects that will help you get hands-on experience. So wait no more, and strengthen your Machine Learning Foundations.

Data Science Live Course

Every organisation now relies on data before making any important decisions regarding their future. So, it is safe to say that Data is really the king now. So why do you want to get left behind? This LIVE course will introduce the learner to advanced concepts like: Linear Regression, Naive Bayes & KNN, Numpy, Pandas, Matlab & much more. You will also get to work on real-life projects through the course. So wait no more, Become a Data Science Expert now.


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