About this course
What you'll learn
Machine learning Introduction
What Are the Skills Required to Become a Machine Learning Expert?
Probability and Statistics
Data Modeling and Evaluation
Applying Machine Learning Algorithms and Libraries
What Is Data Science?
Data Science versus Machine Learning
Machine Learning versus Deep Learning
Machine Learning Applications Day-to-Day Life
Use cases of machine learning in retail
Machine Learning in Finance - Current Applications
Machine Intelligence in the Travel & Transportation Industry
Types of machine learning
What is supervised learning
What is sklearn?
Algorithms inbuilt in sklearn
How to import algorithms from sklearn
How to train test and split the data based on algorithms from sklearn
What is regression?
What is classification?
Supervised learning work flow
What is unsupervised learning?
What is clustering?
What is Reinforcement Learning?
Types of supervised and unsupervised learning algorithms
What is Linear Regression?
What is Multiple Linear Regression?
What is Logistic Regression?
What is KNN (K-Nearest Neighbour) Classifier
Whatis a Naive bayesAlgorithm?
What is Decision Tree Algorithm?
Random forest Algorithm
Types of Clustering
Introduction to Deep Learning
Artificial Neural Network(ANN)(Concept and maths)
Introduction to python
Features in Python
Keywords in python
Global and Local Variables
Reading and saving files and amending using Pandas
To Students or professionals who want to get good amount of idea about ML and the concepts and maths Behind Algorithms with Hands on One Project in ML.
The audience gets ample knowledge of ML concepts and mathematics and AI concept with maths behind and also hands on with one project with python language. This course would help them to implement machine learning projects.