Machine Learning Foundations
Skill Level -Intermediate
Course cost -Free
About this course
Machine learning uses two types of techniques: one is supervised learning
which trains a model on known input and output data so that it can predict
future outputs. The second called unsupervised learning finds hidden
patterns or intrinsic structures in input data. In this course, you will
learn some of the most popular supervised learning algorithms such as
KNN and Naive Bayes.
Prerequisites
Python, Basic Statistics
Skills covered
ML basics
Supervised ML
Linear regression
KNN
Data cleaning
Data Visualization
Logistic regression
Course Syllabus
Machine Learning Foundation
Concepts of machine learning and its importance
Feature or Mathematical space
Introduction to Supervised machine learning
Linear regression and it’s Pearson’s coefficient
Linear regression mathematically and coefficient of Determinant
Advantages and Disadvantages of Linear Regression
Brief scenario of Data set and Descriptive analysis-3
Analyse the Distribution of dependent column
Missing Values imputation
Bivariate analysis using plots through Seaborn function
Building model using all information
Cleaning the data, plotting graphs and some mathematical expressions
Analysis of model and concept of Squared errors
Concept of fluke correlation
Logit function in Logistic regression
Probability examples and model predictions
Hands-on exercise on logistic regression
Introduction to Naive Bayes Classifier
Naive Baye’s Classifier and its example with 2 dimension
Bayes theorem and formula
Naive Bayes Classifier
