Machine Learning Tutorial

  • Last Updated : 08 Aug, 2023

Machine Learning tutorial

Machine Learning tutorial covers basic and advanced concepts, specially designed to cater to both students and experienced working professionals.

This machine learning tutorial helps you gain a solid introduction to the fundamentals of machine learning and explore a wide range of techniques, including supervised, unsupervised, and reinforcement learning.

Machine learning (ML) is a subdomain of artificial intelligence (AI) that focuses on developing systems that learn—or improve performance—based on the data they ingest. Artificial intelligence is a broad word that refers to systems or machines that resemble human intelligence. Machine learning and AI are frequently discussed together, and the terms are occasionally used interchangeably, although they do not signify the same thing. A crucial distinction is that, while all machine learning is AI, not all AI is machine learning.

What is Machine Learning?

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.

Recent Articles on Machine Learning

 

Features of Machine learning

  • Machine learning is data driven technology. Large amount of data generated by organizations on daily bases. So, by notable relationships in data, organizations makes better decisions.
  • Machine can learn itself from past data and automatically improve.
  • From the given dataset it detects various patterns on data.
  • For the big organizations branding is important and it will become more easy to target relatable customer base.
  • It is similar to data mining because it is also deals with the huge amount of data.

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. Deploying ML Models as API using FastAPI
  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

Prerequisites to learn machine learning

  • Knowledge of Linear equations, graphs of functions, statistics, Linear Algebra, Probability, Calculus etc.
  • Any programming language knowledge like Python, C++, R are recommended.

    • FAQs on Machine Learning Tutorial

    Q.1 What is Machine learning and how is it different from Deep learning ?

    Answer:

    Machine learning develop programs that can access data and learn from it. Deep learning is the sub domain of the machine learning. Deep learning supports automatic extraction of features from the raw data.

    Q.2. What are the different type of machine learning algorithms ?

    Answer:

    • Supervised algorithms: These are the algorithms which learn from the labelled data, e.g. images labelled with dog face or not. Algorithm depends on supervised or labelled data. e.g. regression, object detection, segmentation.
    • Non-Supervised algorithms: These are the algorithms which learn from the non labelled data, e.g. bunch of images given to make a similar set of images. e.g. clustering, dimensionality reduction etc.
    • Semi-Supervised algorithms: Algorithms that uses both supervised or non-supervised data. Majority portion of data use for these algorithms are not supervised data. e.g. anamoly detection.

    Q.3. Why we use machine learning ?

    Answer:

    Machine learning is used to make decisions based on data. By modelling the algorithms on the bases of historical data, Algorithms find the patterns and relationships that are difficult for humans to detect. These patterns are now further use for the future references to predict solution of unseen problems.

    Q.4. What is the difference between Artificial Intelligence and Machine learning ?

    Answer:

    Artificial IntelligenceMachine Learning
    Develop an intelligent system that perform variety of complex jobs.Construct machines that can only accomplish the jobs for which they have trained.
    It works as a program that does smart work.The tasks systems machine takes data and learns from data.
    AI has broad variety of applications.ML allows systems to learn new things from data.
    AI leads wisdom.ML leads to knowledge.