AZeotropy'2020 IIT-Bombay is presenting a workshop in association with Entrench Electronics Pvt. Ltd.
Registration Fee? Rs. 1000/- INR / participant |2 Day
Discounted Price: Upto 5th March
What you will take after the course? Pure knowledge and lots of learning. Specialized Training from Domain Expert. Software tool kit & certification.
What do you need to carry during the workshop? Carry the payment receipt hard copy during the training. Carry Aadhar Id for entry in IIT-Bombay.
Prerequisite for the course Basic knowledge about any programming language Basic knowledge about mathematics Must be willing to learn and enthusiasm
System Requirements The laptop is must (you can work In a group of 2-3 if you are not carrying a laptop) Operating system ( Windows 7/8/10, Ubuntu, macOS) anyone is fine RAM - Minimum 4 GB For Internet connectivity (Data pack in your handset )
Terms & Conditions Charges for the Workshop/Training is NON REFUNDABLE, NON TRANSFERABLE, NON-EXTENDABLE. In case of cancellation of Workshop/Training from our side, the participants of the cancelled Workshop/Training will be given an option to be upgraded to another Workshop/Training. If the offer is denied by them, only then will they be considered for a refund.
If In Any Case Workshop is cancelled by Organizers due to any reason, we will refund the Workshop fee after deducting Bank Changes i.e. (INR150/-) per participant fee. The refund will be in 3 stages: Verification>>Approval>>Bank Credit through the same mode of payment. A cash refund is NOT possible under any circumstance. We are not responsible for any software failing to run/install on the participant's laptop owing to different configurations in laptops
Machine Learning with Python OBJECTIVES OF THE COURSE To understand the statistics and probability To understand the programming language Python To understand the evolution of Machine Learning over the years To get the knowledge about different types of algorithms present in Machine Learning Experience of working with real time data set Understanding how to implement algorithms from scratch and by open source library
1. Introduction To machine learning (1.30hrs) a. Application of machine learning b. What is machine learning? c. Understanding AI, ML, DL d. Comparing Human Intelligence with Artificial Intelligence
2. Types of Machine Learning ( 0.45 hrs) a. Supervised Learning b. Different types of Supervised Learning c. Unsupervised Learning d. Different types of unsupervised learning e. Reinforcement Learning f. Semi supervised learning
3. Quick Programming (Python) (2.00hrs) a. Installation of Jupyter Notebook using Annaconda/MiniConda/Python b. Introduction to Jupyter notebook c. History of Python d. Data types in Python e. Data Structure in Python f. Condition statement in Python g. Loops in Python h. Function,Class in Python i. Installing and importing Library in Python j. Overview of Numpy k. Overview of pandas l. Overview of matplotlib
4. Supervised Learning -1(Linear Regression) (2hrs) a. Application of Linear Regression b. What is Linear Regression? c. Deriving Linear Regression through Gradient Descents d. Implementing in Python on Dummy data set and stock market data
5. Supervised Learning - 2 (K Nearest Neighbours) (2hrs) a. Application of KNN b. What is KNN? c. Understanding algorithm of KNN d. Implementing in Python on Dummy data set and Breast cancer dataset
6. Data preprocessing ( 0.45 hrs) a. Load data b. Adding and removing columns and row c. Handling missing vales Practice on real-time data set Stock market d. /Breast cancer dataset
7. Training Testing Split (30hrs) a. What is train test split b. Why splitting is required? c. Different types of train test split d. Practice on real time datasets
8. Data Visualisation (30min) a. Understanding the importance of data visualisation b. Working with matplotlib
9. Unsupervised learning-1 (K means) (2hrs) a. Application of K means b. What is K means c. Understanding algorithm of K Means d. Implementing in Python on Dummy data set and Wine data set(SK learn )
10. Supervised learning-3 (Polynomial regressions) (30min) a. Disadvantage of Linear regression b. How polynomial regression overcome the disadvantage c. Understanding algorithm of Polynomial regression d. Implementing in Python on Dummy data set
11. Supervised learning-4 (Multilinear regressions) (30min) a. Disadvantage of Linear regression b. regression overcome the disadvantage c. Understanding algorithm of Multilinear regression d. Implementing in Python on Dummy data set
12. Neural Network overview (1 hr) a. What is neural network b. Perceptron ,a simple neural network c. Implementing in Python on Dummy data set d. Implementing AND/ OR gate through neural network