Machine Learning with Python Course

Machine Learning with Python

Become a Expert & Specialization in Machine Learning with Python

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About Machine Learning with Python

Machine learning is becoming over the modern data-driven world and it is a growing technology among many companies to extensively support many fields, such as search engines, robotics, self-driving cars, and so on. Here in this course you can explore various real-time scenarios where you can use machine learning. This course will be making you to understand and implement all Machine learning algorithms with exciting examples. Every trainer will be delivering machine learning algorithms followed by practical session. Most importantly you will be learning from trainers Bottom level to Top level all algorithms, such as Linear Regression, Logistic Regression, Support Vector Machines, Principal Component Analysis, Time Series Analysis Deep Neural Networks, and so on. This is going to friendly to Python programmers and data analyzers to play around with the code to implement machine learning techniques.

Course Outline

Introduction To Python

The Print Statement
Comments
Python Data Structures & Data Types
String Operations in Python
Simple Input & Output
Simple Output Formatting

Python Program Flow

The IF Statement and it’s Related Statements
And Example with IF and it’s Related Statements
The While Loop
The For Loop
The Range Statement
Break & Continue
Assert
Examples for Looping

Functions & Modules

Create your own Functions
Functions Parameters
Variable Arguments
Scope of A Function
Function Documents/Docstrings
Lambda Functions & Map
An Exercise with Functions
Create A Module
Standard Modules

Exception Handling

Errors
Exception Handling with Try
Handling Multiple Exceptions
Writing your Own Exceptions

File Handling

File Handling Modes
Reading Files
Writing & Appending to Files
Handling File Exceptions
The WITH Statement

Classes In Python

New Style Classes
Creating Classes
Instance Methods
Inheritance
Polymorphism
Exception Classes & Custom Exceptions

Loading Data In Python For Data Analysis

Import Libraries
Load Dataset
Dimensions of the Dataset
Peek at the Data
Statistical Summary
Class Distribution
Data Visualization
Univariate Plots
Multivariate Plots

Different Types Of Plotting Using Python

Simple Plots
Standard Time Plot
Plots with Different Strokes
Coloured Plot
Another Coloured Plot
Dotted Plot
Curve and Point
Bar Plot
Multi-Coloured Plot
Polar Plot
2D Data Plot
3D Bar Graph

Classification Of Model Building

Classification of Linear Regression
Implementing Linear Regression
Classification of Logistic Regression
Implementing Logistic Regression
Classification of Naive Bayes
Implementing Naive Bayes

Foundational Machine Learning

Introduction to Predictive Modeling
Understanding the Support Vector Machines (SVM’s)
Using SVM’s
Introduction to Clustering
Introduction to Unsupervised Learning
Using the K-Means Algorithm
Evaluating the Performance of Clustering Algorithms
Using DBScan Algorithm

Advanced Machine Learning

Introduction to Principal Component Analysis (PCA)
Implementing the Principal Components with Clusters
Understanding the Concept of Nearest Neighbours Algorithm
Implement K-Nearest Neighbours
Introduction to Text Data Analyzing
Preprocessing Data using Tokenization
Implementing Text Analysis
Introduction to Speech Recognition
Reading and Plotting Audio Data
Introduction to Time Series Analysis
Slicing Time Series Data
Operating on Time Series Data
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Deep Learning & Neural Networks

Understanding the Components and Structure of Artificial Neural Networks
Understanding and Implementing a Perceptron
Implementing a Single Layer Neural Network
Implementing a Deep Neural Network
Creating a Vector Quantizer
Describing the Recurrent Neural Network for Sequential Data Analysis

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