Machine Learning Tutorial

What is Machine Learning?

As all of us is very much clear about the leaning concept of humans, they learn from their past experiences. But can we expect the same from computers or any machine to learn itself from the given raw data and past experiences? Thereby the concept of machine learning came into existence.

Machine learning is a subset of artificial intelligence that learns through the raw data and past experiences without being actually programmed explicitly, to give some sense to the data exactly in same manner as humans can do. In other words we can say that ML is a field of Computer Science that deals in extracting out some sensible data on being processed by some ML algorithms. Machine learning was introduced by Arthur Samuel in 1959.

“Machine learning uses statistical tools on data to output a predicted value. It is an application of artificial intelligence that provides the system with the ability to learn and improve from experience without being explicitly programmed automatically”.

What is the need of Machine Learning?

Nowadays, humans have become more advanced and work quite intelligently, especially the way they handle difficult problems and solve them. While on the other hand there is AI which is still undergrowth and has not beaten the human intelligence yet. And so, machine learning is needed for decision making on the basis of some raw data in an efficient manner at a large scale.

As of now, the developers are much more into developing technologies like artificial intelligence, deep learning, and machine learning to extract some information from the given data and performs different algorithm to solve some actual real-world problems especially on a huge scale working as a helping hand to the organization. It can also be known as data-driven decision making. The decision making does not require any programming logic, rather the driven data can be used itself. And for that, it does require human intelligence. Also, the human itself is not enough to solve the real-world problem at a huge scale. So this is when machine learning is needed. As more the data, better the model and higher would be its accuracy.

Working of Machine learning

In traditional programming, we used to provide the data and program, and the computer is used to generate the output.

Whereas, in the case of machine learning, we used to provide data as well as predicted output to the machine, and it learns from the data, find the hidden insights, and creates a model. It takes the output data and repeats its training and growing accordingly so that the model gets better with time on getting trained with new data or the output data.

Applications of Machine Learning

Machine learning is growing so rapidly that in the golden era of AI, where ML is incorporated in our day to day life, and we did not even acknowledge it such as; Alexa, Google maps, Google Assistant and Google maps. Some other real-world applications are enlisted below:-

1. Emotion analysis
2. Image Recognition
3. Speech Recognition
4. Trading of Stock Market
5. Fraud detection and its prevention
6. Product suggestions on online shopping apps
7. Weather forecasting
8. Traffic prediction
9. Friends suggestion on social media apps
10. Diagnosis of medical issues

Machine Learning Tutorial

• Machine Learning Tutorial
• Machine Learning Life Cycle
• Python Anaconda setup
• Difference between ML/ AI/ Deep Learning
• Understanding different types of Machine Learning
• Data Pre-processing
• Supervised Machine Learning

ML Regression Algorithm

• Linear Regression

ML Classification Algorithm

• Introduction to ML Classification Algorithm
• Logistic Regression
• Support Vector Machine
• Decision Tree
• Naïve Bayes
• Random Forest

ML Clustering Algorithm

• Introduction to ML Clustering Algorithm
• K-means Clustering
• Hierarchical Clustering

ML Association Rule learning Algorithm

• Introduction to association Rule Learning Algorithm

How To

• How to Learn AI and Machine Learning
• How Many Types of Learning are available in Machine Learning
• How to Create a Chabot in Python Using Machine Learning

ML Questions

• What is Cross Compiler
• What is Artificial Intelligence And Machine Learning
• What is Gradient Descent in Machine Learning
• What is Backpropagation in a Neural Network
• Why is Machine Learning Important
• What Machine Learning Technique Helps in Answering the Question
• Is Data Science and Machine Learning Same

Differences

• Difference between Machine Learning and Deep Learning
• Difference between Machine learning and Human Learning

Miscellaneous

• Top 5 programming languages and their libraries for Machine Learning
• Basics Vectors in Linear Algebra in ML
• Decision Tree Algorithm in Machine Learning
• Bias and Variances in Machine Learning
• Machine Learning Projects for the Final Year Students
• Top Machine Learning Jobs
• Machine Learning Engineer Salary in Different Organisation
• Best Python Libraries for Machine Learning
• Regularization in Machine Learning
• Some Innovative Project Ideas in Machine Learning
• Decoding in Communication Process
• Working of ARP
• Hands-on Machine Learning with Scikit-Learn, TensorFlow, and Keras
• Kaggle Machine Learning Project
• Machine Learning Gesture Recognition
• Machine Learning IDE
• Pattern Recognition and Machine Learning a MATLAB Companion
• Chi-Square Test in Machine Learning
• Heart Disease Prediction Using Machine Learning
• Machine Learning and Neural Networks
• Machine Learning for Audio Classification
• Standardization in Machine Learning
• Student Performance Prediction Using Machine Learning
• Automated Machine Learning
• Hyper Parameter Tuning in Machine Learning
• IIT Machine Learning
• Image Processing in Machine Learning
• Recall in Machine Learning
• Handwriting Recognition in Machine Learning
• High Variance in Machine Learning
• Inductive Learning in Machine Learning
• Instance Based Learning in Machine Learning
• International Journal of Machine Learning and Computing Iris Dataset Machine Learning
• Disadvantages of K-Means Clustering
• Machine Learning in Healthcare
• Machine Learning is Inspired by the Structure of the Brain
• Machine Learning with Python
• Machine Learning Workflow
• Semi-Supervised Machine Learning
• Stacking in Machine Learning
• Top 10 Machine Learning Projects For Beginners in 2023
• Train and Test datasets in Machine Learning
• Unsupervised Machine Learning Algorithms
• VC Dimension in Machine Learning
• Accuracy Formula in Machine Learning
• Artificial Neural Networks Images
• Autoencoder in Machine Learning
• Bias Variance Tradeoff in Machine Learning
• Disadvantages of Machine Learning
• Haar Algorithm for Face Detection
• Haar Classifier in Machine Learning
• Introduction to Machine Learning using C++