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.
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.
- 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 scikit-learn
- 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 Linear-Regression 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 K-Means
- ML | 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
Dimensionality Reduction :
- Introduction to Dimensionality Reduction
- Introduction to Kernel PCA
- Principal Component Analysis(PCA)
- Principal Component Analysis with Python
- Low-Rank 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
- Chi-Square Test for Feature Selection – Mathematical Explanation
- ML | T-distributed Stochastic Neighbor Embedding (t-SNE) 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 Q-Learning
- Implementing Deep Q-Learning 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 K-means clustering
- Deep learning | Image Caption Generation using the Avengers EndGames Characters
- 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?
- 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 Multi-Task 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.
- 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.
FAQs on Machine Learning Tutorial
Q.1 What is Machine learning and how is it different from Deep learning ?
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 ?
Q.3. Why we use machine learning ?
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 ?
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.