×

Python for Machine Learning

Home Python for Machine Learning

Card image cap

Machine Learning (ML) is basically that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do. In simple words, ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method. The key focus of ML is to allow computer systems to learn from experience without being explicitly programmed or human intervention.

After completing this Machine Learning Certification Training using Python, you should be able to:

  • In-depth knowledge of of Python

  • In-depth knowledge of Data Science

  • In-depth knowledge of Machine Learning

  • Work with real-time data

  • Learn data visualization

  • Learn tools and techniques for predictive modeling

  • Discuss Machine Learning algorithms and their implementation

  • Gain expertise to handle business in future, living the present

  • Basic Linear Algebra - Having a good knowledge of linear algebra would be very helpful.  For the beginning topics of ML like regression analysis, etc; knowing the basics of Linear Algebra would suffice. But as one goes on to more deeper in ML, a bit more knowledge of LA would be required.

  • Programming Experience - Prior  programming experience would be very useful. People mostly program in Python, Java and R. But Python and R clearly stand out to be the leaders in the recent days.  Learning Python is very easy and shouldn't take more than two months to get through with the basics.

  • Statistics and Probability - ML is all about Stats and Probability. So, having a prior knowledge in that area would come very handy. One need to be pretty good in identifying distributions and deal with them.

  • Having a solid Math background is very important for any starter in ML or AI. Don't panic if you don't have one, both of us are in the same league. Just take the online math courses at Coursera, Udacity and Khan Academy; they would be sufficient.

Lab Setup:-

Computer with the following software

Operating System: Red Hat Linux / Ubuntu/CentOS/ Windows ( Latest Version preferable )

Anaconda python 3.7 ( Latest Version preferable )

https://www.anaconda.com/download

Python 3.7 version *

Set Up Python Path Environment on OS ( During Installation ). [ Shortest Path ]

Internet Access will be needed to install python third party library

 

Hardware :

RAM: Minimum 4GB / 8GB ( Recommended ).

Internet Connectivity. ( Needed to Install Packages and Run Anaconda Server ).

80 GB HDD.

Pricing



Free


  • 1 Live/Recorded Session
  • Two Sample Modules PDF
  • Free Reference Ebook
  • Course Content
  • Senior Trainer
  • Interactive Learner Dashboard
  • Sample Module Quiz
  • Online Test
  • 24 by 7 System Support



Choose Plan

Silver

$100 $200

  • Fresher(Basic Course )
  • 10 Modules
  • 10 Lab Sessions
  • Course Content
  • Senior Trainer
  • Interactive Learner Dashboard
  • 10 Module Quiz
  • Online Test and Certificate
  • Learner Progress Report
  • 24 by 7 System Support


Choose Plan

Gold

$150 $300

  • Intermediate (Advance Course)
  • 20 Modules
  • 20 Lab Sessions
  • Course Content
  • Senior Trainer
  • Interactive Learner Dashboard
  • 20 Module Quiz
  • Online Test and Certificate
  • Learner Progress Report
  • 24 by 7 System Support


Choose Plan

Diamond

$250 $500

  • Expert Level(Project Oriented)
  • 30 Modules
  • 30 Lab Sessions
  • Course Content
  • Senior Trainer
  • Interactive Learner Dashboard
  • 30 Module Quiz
  • Online Test and Certificate
  • Learner Progress Report
  • Live Project Guidance
  • Discussion Forum
  • 24 by 7 System Support
Choose Plan


Course Outline


1. Control Structure
2. Functions
3. Built in Functions
4. Dictionary Case Study
5. List Comprehension and Dictionary Comprehension
6. Module
7. Built In Modules
8. File Handling and Exception Handling
9. OOPS Application
10. Inheritance, Polymorphism, Overloading and Overriding
11. Iterator, Generator and Collection Framework
12. Introduction to Data Science
13. Data Processing
14. Mathematical Computing with NumPy
15. Data Manipulation with Pandas
16. Data Visualization using matplotlib
17. Introduction to Python
18. Object Oriented Programming using Python
19. Regular Expression: With Case Study
20. Introduction to Machine Learning
21. Supervised learning
22. Unsupervised learning
23. Reinforcement learning
24. Scikit-learn , Linear regression
25. Linear Regression case study
26. Introduction to Deep Learning and Neural Networks
27. Convolutional Neural Networks , Sequence Models
28. Improving and Optimizing a Neural Network
29. Natural Language Processing in TensorFlow
30. Building deep learning models with keras, Fine-tuning keras models
31. Data Structure

Latest E-Learning Courses


Snow
ChatBot

Hello! How can I help you?