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 :
 Getting Started with Machine Learning
 An Introduction to Machine Learning
 What is Machine Learning ?
 Introduction to Data in Machine Learning
 Demystifying Machine Learning
 ML – Applications
 Best Python libraries for Machine Learning
 Artificial Intelligence  An Introduction
 Machine Learning and Artificial Intelligence
 Difference between Machine learning and Artificial Intelligence
 Agents in Artificial Intelligence
 10 Basic Machine Learning Interview Questions
Data and It’s Processing:
 Introduction to Data in Machine Learning
 Understanding Data Processing
 Python  Create Test DataSets using Sklearn
 Python  Generate test datasets for Machine learning
 Python  Data Preprocessing in Python
 Data Cleaning
 Feature Scaling – Part 1
 Feature Scaling – Part 2
 Python  Label Encoding of datasets
 Python  One Hot Encoding of datasets
 Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python
 Dummy variable trap in Regression Models
Supervised learning :
 Getting started with Classification
 Basic Concept of Classification
 Types of Regression Techniques
 Classification vs Regression
 ML  Types of Learning – Supervised Learning
 Multiclass classification using scikitlearn

 Linear Regression :
 Introduction to Linear Regression
 Gradient Descent in Linear Regression
 Mathematical explanation for Linear Regression working
 Normal Equation in Linear Regression
 Linear Regression (Python Implementation)
 Simple LinearRegression using R
 Univariate Linear Regression in Python
 Multiple Linear Regression using Python
 Multiple Linear Regression using R
 Locally weighted Linear Regression
 Generalized Linear Models
 Python  Linear Regression using sklearn
 Linear Regression Using Tensorflow
 A Practical approach to Simple Linear Regression using R
 Linear Regression using PyTorch
 Pyspark  Linear regression using Apache MLlib
 ML  Boston Housing Kaggle Challenge with Linear Regression
 Python  Implementation of Polynomial Regression
 Softmax Regression using TensorFlow
 Naive Bayes Classifiers
Unsupervised learning :
 ML  Types of Learning – Unsupervised Learning
 Supervised and Unsupervised learning
 Clustering in Machine Learning
 Different Types of Clustering Algorithm
 K means Clustering – Introduction
 Elbow Method for optimal value of k in KMeans
 Random Initialization Trap in KMeans
 ML  Kmeans++ Algorithm
 Analysis of test data using KMeans Clustering in Python
 Mini Batch Kmeans clustering algorithm
 MeanShift 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
Reinforcement Learning:
Dimensionality Reduction :
 Introduction to Dimensionality Reduction
 Introduction to Kernel PCA
 Principal Component Analysis(PCA)
 Principal Component Analysis with Python
 LowRank Approximations
 Overview of Linear Discriminant Analysis (LDA)
 Mathematical Explanation of Linear Discriminant Analysis (LDA)
 Generalized Discriminant Analysis (GDA)
 Independent Component Analysis
 Feature Mapping
 Extra Tree Classifier for Feature Selection
 ChiSquare Test for Feature Selection – Mathematical Explanation
 ML  Tdistributed Stochastic Neighbor Embedding (tSNE) Algorithm
 Python  How and where to apply Feature Scaling?
 Parameters for Feature Selection
 Underfitting and Overfitting in Machine Learning
Natural Language Processing :
 Introduction to Natural Language Processing
 Text Preprocessing in Python  Set – 1
 Text Preprocessing in Python  Set 2
 Removing stop words with NLTK in Python
 Tokenize text using NLTK in python
 How tokenizing text, sentence, words works
 Introduction to Stemming
 Stemming words with NLTK
 Lemmatization with NLTK
 Lemmatization with TextBlob
 How to get synonyms/antonyms from NLTK WordNet in Python?
Neural Networks :
 Introduction to Artificial Neutral Networks  Set 1
 Introduction to Artificial Neural Network  Set 2
 Introduction to ANN (Artificial Neural Networks)  Set 3 (Hybrid Systems)
 Introduction to ANN  Set 4 (Network Architectures)
 Activation functions
 Implementing Artificial Neural Network training process in Python
 A single neuron neural network in Python
 Introduction to Deep QLearning
 Implementing Deep QLearning using Tensorflow
ML – Deployment :
 Deploy your Machine Learning web app (Streamlit) on Heroku
 Deploy a Machine Learning Model using Streamlit Library
 Deploy Machine Learning Model using Flask
 Python – Create UIs for prototyping Machine Learning model with Gradio
 How to Prepare Data Before Deploying a Machine Learning Model?
 Deploying ML Models as API using FastAPI
 Deploying Scrapy spider on ScrapingHub
ML – Applications :
 Rainfall prediction using Linear regression
 Identifying handwritten digits using Logistic Regression in PyTorch
 Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
 Python  Implementation of Movie Recommender System
 Support Vector Machine to recognize facial features in C++
 Decision Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle)
 Credit Card Fraud Detection
 NLP analysis of Restaurant reviews
 Applying Multinomial Naive Bayes to NLP Problems
 Image compression using Kmeans clustering
 Deep learning  Image Caption Generation using the Avengers EndGames Characters
 How Does Google Use Machine Learning?
 How Does NASA Use Machine Learning?
 5 MindBlowing Ways Facebook Uses Machine Learning
 Targeted Advertising using Machine Learning
 How Machine Learning Is Used by Famous Companies?
Misc :
 Pattern Recognition  Introduction
 Calculate Efficiency Of Binary Classifier
 Logistic Regression v/s Decision Tree Classification
 R vs Python in Datascience
 Explanation of Fundamental Functions involved in A3C algorithm
 Differential Privacy and Deep Learning
 Artificial intelligence vs Machine Learning vs Deep Learning
 Introduction to MultiTask Learning(MTL) for Deep Learning
 Top 10 Algorithms every Machine Learning Engineer should know
 Azure Virtual Machine for Machine Learning
 30 minutes to machine learning
 What is AutoML in Machine Learning?
 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.
 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.
 NonSupervised 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.
 SemiSupervised algorithms: Algorithms that uses both supervised or nonsupervised data. Majority portion of data use for these algorithms are not supervised data. e.g. anamoly detection.
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:
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 Intelligence Machine 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.