- Machine Learning Basics
- Machine Learning - Home
- Machine Learning - Introduction
- Machine Learning - Getting Started
- Machine Learning - Basic Concepts
- Machine Learning - Ecosystem
- Machine Learning - Python Libraries
- Machine Learning - Applications
- Machine Learning - Life Cycle
- Machine Learning - Required Skills
- Machine Learning - Implementation
- Machine Learning - Challenges & Common Issues
- Machine Learning - Limitations
- Machine Learning - Reallife Examples
- Machine Learning - Data Structure
- Machine Learning - Mathematics
- Machine Learning - Artificial Intelligence
- Machine Learning - Neural Networks
- Machine Learning - Deep Learning
- Machine Learning - Getting Datasets
- Machine Learning - Categorical Data
- Machine Learning - Data Loading
- Machine Learning - Data Understanding
- Machine Learning - Data Preparation
- Machine Learning - Models
- Machine Learning - Supervised
- Machine Learning - Unsupervised
- Machine Learning - Semi-supervised
- Machine Learning - Reinforcement
- Machine Learning - Supervised vs. Unsupervised
- Machine Learning Data Visualization
- Machine Learning - Data Visualization
- Machine Learning - Histograms
- Machine Learning - Density Plots
- Machine Learning - Box and Whisker Plots
- Machine Learning - Correlation Matrix Plots
- Machine Learning - Scatter Matrix Plots
- Statistics for Machine Learning
- Machine Learning - Statistics
- Machine Learning - Mean, Median, Mode
- Machine Learning - Standard Deviation
- Machine Learning - Percentiles
- Machine Learning - Data Distribution
- Machine Learning - Skewness and Kurtosis
- Machine Learning - Bias and Variance
- Machine Learning - Hypothesis
- Regression Analysis In ML
- Machine Learning - Regression Analysis
- Machine Learning - Linear Regression
- Machine Learning - Simple Linear Regression
- Machine Learning - Multiple Linear Regression
- Machine Learning - Polynomial Regression
- Classification Algorithms In ML
- Machine Learning - Classification Algorithms
- Machine Learning - Logistic Regression
- Machine Learning - K-Nearest Neighbors (KNN)
- Machine Learning - Naïve Bayes Algorithm
- Machine Learning - Decision Tree Algorithm
- Machine Learning - Support Vector Machine
- Machine Learning - Random Forest
- Machine Learning - Confusion Matrix
- Machine Learning - Stochastic Gradient Descent
- Clustering Algorithms In ML
- Machine Learning - Clustering Algorithms
- Machine Learning - Centroid-Based Clustering
- Machine Learning - K-Means Clustering
- Machine Learning - K-Medoids Clustering
- Machine Learning - Mean-Shift Clustering
- Machine Learning - Hierarchical Clustering
- Machine Learning - Density-Based Clustering
- Machine Learning - DBSCAN Clustering
- Machine Learning - OPTICS Clustering
- Machine Learning - HDBSCAN Clustering
- Machine Learning - BIRCH Clustering
- Machine Learning - Affinity Propagation
- Machine Learning - Distribution-Based Clustering
- Machine Learning - Agglomerative Clustering
- Dimensionality Reduction In ML
- Machine Learning - Dimensionality Reduction
- Machine Learning - Feature Selection
- Machine Learning - Feature Extraction
- Machine Learning - Backward Elimination
- Machine Learning - Forward Feature Construction
- Machine Learning - High Correlation Filter
- Machine Learning - Low Variance Filter
- Machine Learning - Missing Values Ratio
- Machine Learning - Principal Component Analysis
- Machine Learning Miscellaneous
- Machine Learning - Performance Metrics
- Machine Learning - Automatic Workflows
- Machine Learning - Boost Model Performance
- Machine Learning - Gradient Boosting
- Machine Learning - Bootstrap Aggregation (Bagging)
- Machine Learning - Cross Validation
- Machine Learning - AUC-ROC Curve
- Machine Learning - Grid Search
- Machine Learning - Data Scaling
- Machine Learning - Train and Test
- Machine Learning - Association Rules
- Machine Learning - Apriori Algorithm
- Machine Learning - Gaussian Discriminant Analysis
- Machine Learning - Cost Function
- Machine Learning - Bayes Theorem
- Machine Learning - Precision and Recall
- Machine Learning - Adversarial
- Machine Learning - Stacking
- Machine Learning - Epoch
- Machine Learning - Perceptron
- Machine Learning - Regularization
- Machine Learning - Overfitting
- Machine Learning - P-value
- Machine Learning - Entropy
- Machine Learning - MLOps
- Machine Learning - Data Leakage
- Machine Learning - Resources
- Machine Learning - Quick Guide
- Machine Learning - Useful Resources
- Machine Learning - Discussion
Machine Learning - Getting Started
Machine learning has become an increasingly important topic in recent years as the amount of data generated by businesses and individuals continues to grow at an exponential rate. From self-driving cars to personalized recommendations on streaming platforms, machine learning algorithms are now used in a wide range of applications.
Let's explore what exactly machine learning is.
What is Machine learning?
Machine learning is a subset of Artificial Intelligence; as the name suggests, it is defined as the capability of a machine to learn to exhibit "intelligent behavior" like humans. Machine learning uses algorithms that are trained on datasets to understand patterns in the data and to create self-learning models that are capable of predicting outcomes.
Ever wondered how 'Amazon' accurately recommends something that you might like or that you are looking to purchase? Or how emails are filtered into primary, social and promotions? Well, thanks to Machine learning.
Types of Machine Learning
We can categorize the machine learning algorithms into three different types - supervised, unsupervised, and reinforcement learning. Let's discuss these three types in detail −
Supervised Learning
Supervised learning that uses labeled dataset to train algorithms to understand data patterns and predict outcomes. For example, filtering a mail into inbox or spam folder.
The supervised learning further can be classified into two types − classification and regression.
There are different supervised learning algorithms that are widely used −
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- K-nearest Neighbor
- Support Vector Machine
- Naive Bayes
- Linear Discriminant Analysis
- Neural Networks
Unsupervised Learning
Unsupervised learning is a type of Machine learning that uses unlabeled dataset to discover patterns without any explicit guidance or instruction. For example, customer segmentation i.e, dividing a company's customers into groups that reflect similarity.
Further, we can classify the unsupervised learning algorithms into three types − clustering, association, and dimensionality reduction.
Followings are some commonly used unsupervised learning algorithms −
- K-Means Clustering
- Principal Component Analysis(PCA)
- Hierarchical Clustering
- DBSCAN Clustering
- Agglomerative Clustering
- Apriori Algorithm
- Autoencoder
- Restricted Boltzmann machine (RBM)
Reinforcement Learning
Reinforcement learning algorithms are trained on datasets to make decisions and achieve optimized results by minimizing the trial and error method. For example, Robotics.
Following are some common reinforcement learning algorithms −
- Q-learning
- Markov Decision Process (MDP)
- SARSA
- DQN
- DDPG
Use Cases of Machine Learning
Let's discuss some important real-life use cases of different types of machine learning algorithms
Supervised Learning
Following are some real-life use cases of supervised learning −
- Image Classification
- Spam Filtering
- House Price Prediction
- Signature Recognition
- Weather Forecasting
- Stock price prediction
Unsupervised Learning
Some use cases of unsupervised machine learning are as follows −
- Anomaly detection
- Recommendation systems
- Customer segmentation
- Fraud detection
- Natural language processing
- Genetic search
Reinforcement Learning
Followings are some application examples, where reinforcement learning is used −
- Autonomous vehicles
- Robotics
- Game playing
Prerequisites to Get Started
To get started with machine learning, you should have some basic understanding of computer science fundamentals. Along with basic computer science, you should be familiar with the following −
- Programming languages
- Libraries and Packages
- Mathematics and statistics
Let's discuss the above three prerequisites one by one.
Programming Languages: Python or R
There are many programming languages, such as C++, Java, Python, R, Julia, etc., that are used for machine learning development. You can start with any programming language of your choice. Python programming is widely used for machine learning and data science.
In this machine learning tutorial, we will be using Python and/ or R programming to implement the example programs.
Following are some basic topics to cover before starting this tutorial −
- Variables, basic data types
- Data Structures: list, set, dictionaries
- Loops and conditional statements
- Functions
- String formatting
- Classes and Objects
Libraries and Packages
To get started with this machine learning tutorial, we recommend getting familiar with some libraries, packages, and modules such as NumPy, Pandas, Matplotlib, etc.
As we are using Python programming in this tutorial, you should have some basic understanding of the following libraries/ packages/ modules −
NumPy − for numeric computations.
Pandas − for data manipulation and preprocessing.
Scikit-learn − has implemented almost all the machine learning algorithms such as linear regression, logistic regression, k-means clustering, k-nearest neighbor, etc.
Matplotlib − for data visualization.
Mathematics and Statistics
Mathematics and statistics play important role in developing machine learning and data science related applications. Advanced mathematics is not required to get started but it helps to understand the machine learning concepts in great detail.
The following topics are generally recommended to get familiar with before getting started with machine learning tutorial −
Algebra
Variables, coefficients, functions.
Linear equations, logarithm and logarithmic equations, sigmoid function.
Linear Algebra
Vector and matrix, matrix multiplication, dot product
tensor and tensor ranks
Statistics and Probability
Mean, median, mode, outliers, and standard deviation
Ability to read a histogram
Probability, conditional probability, Bayes rules
Calculus
Concept of a derivative, gradient, or slope
Partial derivatives
Chain rule
Trigonometry
Trigonometric functions (specially tanh) used in activation functions
Getting started with Machine Learning
You might wonder if Machine learning is hard to learn? The answer would be absolutely not; you will require a strong understanding of mathematics, computer science and coding, and should keep up with the AI trends. Well, excelling in Machine learning is something that every technophile dreams of but does not know where to start, so here are a few steps that help you get started.
Step 1 − Learn Prerequisites
There are a few prerequisites that lay the foundation to understand how algorithms and machine learning models work. Start by learning the basics of:
- Any programming language like Python or R.
- Libraries and Packages
- Mathematics and Statistics(Like Calculus, Linear Algebra and more)
Step 2 − Learn Machine Learning Fundamentals
Before diving into machine learning, it's important to have a solid understanding of the fundamentals. This includes learning about different types of Machine Learning methods such as regression, classification, clustering, dimensionality reduction, etc.
In this Machine Learning tutorial, we have covered all the machine learning concepts from basics to advanced, along with their implementations. You just need to start learning the tutorial chapter-wise and keep practicing the programming examples.
Step 3 − Explore Machine Learning Algorithms
Algorithms form the foundation of Machine learning, allowing computers to observe data patterns and predict output. Explore and understand essential algorithms like Naive Bayes, Random Forest, Decision tree, etc. This will help you understand the working flow of an algorithm.
Step 4 − Choose a Machine Learning Framework/ Library
There are different tools, frameworks, software, and platforms for Machine learning. The challenging task is to select the best tool as per your model. Mastering machine learning tools enables you to work with data, train your model, discover new methods, and create algorithms. Some commonly used Machine learning tools are Scikit-learn, TensorFlow, PyTorch, and many more.
In addition to the tools and algorithms having a good grip on libraries like NumPy, SciPy, Matplotlib, etc., serves you well in your Machine Learning journey.
Step 5 − Practice with Real Data
Dataset is the backbone of any Machine Learning algorithm. This involves a large amount of data grouped into a collection. Datasets are used to train and test algorithms, analyze patterns, and gain insights.
There are many websites like Kaggle, Google Dataset search, and others that provide publicly available datasets.
Practicing on real-time data will help you understand data cleaning, data preprocessing, and data analysis. Once you start working on data, you will be able to choose the right algorithm for a problem statement and dataset appropriate for it.
Step 6 − Build Your Own Projects
After mastering the basics, it's time to create your own project with a problem statement that you choose. This will help you apply what you have learned so far and will develop your skills further.
You can start with simple algorithms like classification or recommendation systems using pre-processed dataset, then move to developing complex algorithms once you are comfortable.
Step 7 − Participate in Machine Learning Communities
Join machine learning communities like Github, which is a great way to connect with people with similar interests as you. Through these communities, you will get a chance to learn from others, share experiences, and get feedback on your projects. This helps you stay motivated to learn and grow.