In this The Machine Learning Series in Python: Level 1 Course you will master the foundations of Machine Learning and practice building ML models with real-world case studies. We will start from scratch and explain: What Machine Learning is, The Machine Learning Process of how to build a ML model, Regression: Predict a continuous number, Simple Linear Regression, Ordinary Least Squares, Multiple Linear Regression, R-Squared, Adjusted R-Squared. We will also do the following the three following practical activities: Real-World Case Study: Build a Multiple Linear Regression model, Real-World Case Study: Build a Logistic Regression model, Real-World Case Study: Build a K-Means Clustering model.

IN the last part we discussed what is machine learning, the history of machine learning, how the data is used and the use cases of machine learning. Now in this part we are going to discuss the difference between AI vs ML vs DL. Most of the beginners when try to get into this field, they are curious to know about what actually is the difference between AI, ML and DL. When we google this term we got to see this picture. The outermost section represents AI, the middle section represents ML and the innermost represents DL.

FROM the past few years I have seen huge evolution in the Machine learning industry. The motive of this series is to know actually what is Machine Learning and algorithms used to build the model. Lets start Machine Learning by knowing its history like how Machine Leaning evolved and when ML become famous. ML evolved a long time ago around 1940's. Let's relate this to an Indian actor "Pankaj Tripathi".

Previously we saw how to use linear regression to plot a straight line that could predict the co-relation between diabetes disease progression with body mass index and blood pressure. The dataset that we got from Scikit-learn was prepared for model building beforehand, in reality datasets do not come prepared like that for an effective model-building. We need to prepare the dataset and use visualizing techniques to actually transform dataset into something that could be used by our machine learning model effectively. The quality of the result the model produces heavily depends on the dataset that we use.

This first video in the logistic regression series introduces this powerful classification algorithm. The logistic regression algorithm is used when the dependent variable or target variable is categorical. Simple Logistic Regression and Multinomial Logistic Regression are explained. This second video in the logistic regression series compares logistic regression with linear regression in terms of their purpose, use cases, equations, error minimizations, and assumptions. This third video in the logistic regression series covers the four ways of preprocessing data before performing logistic regression: missing data handling, categorical data handling, splitting into train and test set, and feature scaling.

In Unsupervised Learning; data have no target attribute. In this learning algorithm takes as training examples the set of attributes/features alone. This is our second post in this sub series "Machine Learning Types". Our master series for this sub series is "Machine Learning Explained". Unsupervised Learning; is one of three types of machine learning i.e. This post is limited to Unsupervised Machine Learning to explorer its details.

Machine Learning is hottest subject of today's time, DataScientist is the sexiest job of today but implementing these buzz words in real life business is most important need. Machine Learning is the hottest subject of today's time, DataScientist is the sexiest job of today but implementing these buzz words in real life business is most important need. The real need for today's time and business is to clarify, demonstrate and extract real values to benefit every one from this golden key word "Machine Learning". As on date sadly most of the machine learning methods are based on supervised learning. Which means we still have long long way to go.

This is our first post in this sub series "Machine Learning Type" under master series "Machine Learning Explained". We will only talk about supervised machine learning in details here. Machine learning algorithms "learns" from the observations. When exposed to more observations, the algorithm improves its predictive performance. Supervised Learning is becoming a good friend for marketing business in particular.