Description

Course Highlights

• Course :Workshop with complete “Hands on Training Methodology”
• Duration :8 Hours
• Certification :Certificate will be provided at the end of the workshop
• Seats are filled on ‘first come first serve’ basis.
• Registration: 8.30 AM onwards
• Session timings:  9.00AM to 4.30PM (Break -30 to 45 Mins).
• Tea & Snacks will be provided.
• No accommodation will be provided.
• College ID card Must for Registration
• Open to all Engineering disciplines, Arts and Science and Interested

Recommendation:

It is strongly recommended to bring your own LAPTOP during the training so that you can easily practice the exercises at home/College.

Group offer (Applicable for Same College):

• 5% Discount for Group of 5 and above.
• 10% Discount for Group of 10 and above.

What is this course about?

The purpose of this course is to help you build your own Machine learning projects using python . Here’s what you’ll learn by hands-on sessions:

• Introduction to Machine Learning
• Broad Categories of Machine Learning Algorithms
• Machine Learning Algorithms – Analysis
• Introduction to Python Programming
• Python Programming Session
• Sorting and Searching Algorithms and Implementing them in Python
• Introduction to Anaconda Python
• Implementing machine learning algorithms in Python with a real time Dataset and comparing the efficiency of algorithms

SESSION	TIME	CLASS TOPICS
1	9:00 to 10:00	Introduction to Machine learning, Introduction to algorithm Design, algorithmic problem solving.
		****TEA BREAK****
2	10:15 to 10:45	Introduction to Python programming and Anaconda navigator.
3	10:45 to 12:00	Python Crash course: Data Types. Compression operators. If, elif and else statement. For, While Loops. Range() List Comprehension. Functions, Lambda Expressions. Map and Filter.
4	12:00 to 12:30	Numpy module for data analysis and Machine Learning.
	12:30 to 1:00	****LUNCH BREAK****
5	1:00 to 1:30	Data Visualization: 2D plotting with Matplotlib Advanced data visualization with Seaborn
6	1:30 to 2:00	Exploring Data with Pandas module for Machine learning.
7	2:00 to 2:30	Introduction to essentials of Machine learning Algorithm.
		****TEA BREAK****
8	2:45 to 4:00	Project: Supervised learning Unsupervised leaning.