What you'll learn

Machine Learning, Deep Learning, AI and Data Science Basic Concepts

Applications of ML/AI/DS and Job prospects

Supervised, Un-supervised Learning

Environment Setup : Anaconda and Jupyter Notebook

Python package “Numpy” for numerical computation, Python package “Matplotlib” for visualization and plotting, Python package “pandas” for data analysis

Basics of Probability Theory

Understanding different types of data

Examining distribution of the variables

Examining relationship among variables

Exploratory data analysis using Python

Linear regression model / hypothesis

Linear regression on bi-variate data

Multivariate Regression

Polynomial regression

Python implementation of Gradient descent algorithm for regression.

Using in-built Python libraries for solving linear regression problem.

Logistic regression for binary classification problem.

Logistic regression for multiclass classification problem.

Python implementation of Gradient Descent update rule for logistic regression.

Using Python built in library for logistic regression problem.

K-Nearest Neighbour Classifier, Naïve Bayes Classifier, Decision Tree Classifier, Support Vector Machine Classifier, Random Forest Classifier (We shall use Python built-in libraries to solve classification problems using above mentioned classification algorithms)

High dimensionality in data set and its problems.

Linear Algebra Review: Eigen value decomposition.

Feature Selection and Feature Extraction techniques

Principal Component Analysis (PCA)

Implementation of PCA in python.

k-Means clustering algorithm and its limitation

Implementation of k-Means clustering algorithm in python

Hierarchical Clustering.

Implementation of Hierarchical clustering in Python.

Perceptron and its learning rule and its limitations.

Multi-layered Perceptron (MLP) and its architecture.

Learning Rule : Back-Propagation

Building an MLP in Python.

Requirements

Mathematics Prerequisite : Basic concepts of Function & Curve tracking, basics of Multivariable Calculus : Partial Derivatives, Optimization : finding maxima and minima of a function, Linear Algebra: Vector & Matrices

Statistics Prerequisite : Basic Concepts of frequency distribution and histogram plot, Cumulative frequency distribution and ogive, Basic understanding of probablity

Python Prerequisite : Basic Idea, Data Type, Function, OOPS concepts

Description

This course will guide you to learn this thing:

Installation of Anaconda Distribution and Jupyter Notebook

Introduction to NumpyIntroduction to Matplotlib

Introduction to PandasProbablity Theory

 IntroductionExploratory Data Analysis 

Basic ConceptsDistribution of Variable

Anyone interested in Machine learning can take the course.

Here in the course, we are going to use Python as a Programming Language.

Who this course is for:

Anyone interested to learn Machine Learning with Python

Homepage:

https://www.udemy.com/course/machine-learning-with-python-/