Machine Learning Life Cycle: Machine learning has given the computer systems the abilities to automatically learn without being explicitly programmed. But how does a machine learning system work? So, it can be described using the life cycle of machine learning. Machine learning life cycle is a cyclic process to build an efficient machine learning project. The main purpose of the life cycle is to find a solution to the problem or project. Machine learning life cycle involves seven major steps, which are given below: Gathering Data Data preparation Data Wrangling Analyse Data Train the model Test the model Deployment Deployment The most important thing in the complete process is to understand the problem and to know the purpose of the problem. Therefore, before starting the life cycle, we need to understand the problem because the good result depends on the better understanding of the problem. In the complete life cycle process...

  At a broad level, machine learning can be classified into three types: 1.   Supervised learning 2.   Unsupervised learning 3.   Reinforcement learning 1) Supervised Learning Supervised learning is a type of machine learning method in which we provide sample labeled data to the machine learning system in order to train it, and on that basis, it predicts the output. The system creates a model using labeled data to understand the datasets and learn about each data, once the training and processing are done then we test the model by providing a sample data to check whether it is predicting the exact output or not. The goal of supervised learning is to map input data with the output data. The supervised learning is based on supervision, and it is the same as when a student learns things in the supervision of the teacher. The example of supervised learning is  spam filtering . Supervised learning can be grouped further in two categories of algorithms...

 What is Machine Learning? Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Currently, it is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender system, and many more. In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn from experiences or past data like a human does? So here comes the role of  Machine Learning . How ML work with AI? Machine Learning is said as a subset of  artificial intelligence  that is mainly concerned with the development of algorithms which allow a computer to learn from the data and past experiences on their own. Th...

For machine learning we will use the Training dataset to predict new data hence Data science is required. 1)  Python knowledge 2)  Data science knowledge   :-    Numpy,scipy,matplotlib,panda 3) Different type of methods in ML:- Based on human supervision Unsupervised Learning Semi-supervised Learning Reinforcement Learning 4)  ML library:-  scikit-learn 5) Classification 5)  Tensor flow 6)  Clustering  (K-MEANS,MEANSHIFT,HIERARCHICAL) 7)  Regression(Linear regression,Logistic,SVM,Decision Tree) 8)  KNN algorithm

Applications of Machines Learning Machine Learning is the most rapidly growing technology and according to researchers, we are in the golden year of AI and ML. It is used to solve many real-world complex problems that cannot be solved with the traditional approach. Following are some real-world applications of ML −              Weather forecasting,              Image detection and manipulation Emotion analysis Sentiment analysis Error detection and prevention Stock market analysis and forecasting Speech synthesis Speech recognition Customer segmentation Object recognition Fraud detection Fraud prevention Recommendation of products to customer in online shopping