Learn Machine Learning With Python

Learn Machine Learning With Python


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About The Event

Syllabus Overview 


Learn Python as a Language

Machine leaning using python 

Project development skills & techniques 

1 Month internship with industry project 


What will you learn?


The workshop is meant to provide you with a solid base to build your machine learning skills. In particular, you will learn to:


Python Language
Recognize problems that can be solved with Machine Learning
Select the right technique (is it a classification problem? a regression? needs preprocessing?)
Load and manipulate data with Pandas
Visualize and explore data with Matplotlib
Build regression, classification and clustering models with Scikit-Learn
Evaluate model performance with Scikit-Learn
Build, train and serve a predictive model using Python


Workshop day wise Plan 

Day 1: Learn Python as a language
Module 1: Introduction to Python 
The Python Philosophy 
Using python interpreter 
Running Simple python programs 


Module 2: Data structures & Control flow 
A detailed treatment of the string facilities of Python 
Conditional Statements 
Variables revisited 


Module 3: Functions, File Handling 
Recursive functions 
File handling, I/O 


Day 2: Learn Python as a language Cont., Project development


Module 4: Object Oriented Programming 
Overview of Object-oriented programming 
Objects, Instances and classes 
Inheritance and the object hierarchy


Module 5: Project Development Skills & Techniques
Coding Standards
Version control
Dynamic code analysis 
Continuos integration
Live project for hands-on experience 


Day 3: Machine learning using Python
Module 1: Theoretical Foundation of Machine Learning
Overview of Machine Learning
Supervised Learning, Unsupervised Learning
Classification, Clustering & Regression


Phases of ML Project
Data Collection
Data Preprocessing
Data Visualization
Data Preprocessing
Model Creation
Evaluation and Fine Tuning


Module 2: Data Collection
BeautifulSoup & Scrappy for web collection
Twitter, linkedin, facebook api's for data collection


Day 4: Machine learning using Python Cont.
Module 1: Data Visualization / EDA
Using matplotlib & Seaborn for Data visualization for inference
Correlation doesn't mean causation


Module 2: Data Preprocessing
Using Pandas for Data Preprocessing, Cleaning dataset, filling missing values
Feature engineering


Day 5: Machine learning using Python Cont. 
Module 1: Model Creation
Choosing features for ml model using scikit-learn
Model creation & evaluation
Fine Tuning the model


Module 2: Accuracy & Fine Tuning
Checking model accuracy
Accuracy parameters
Cross validation
Advanced ml techniques
Bagging, Boosting


Note: Carry your personal laptops. Laptops are compulsory. 


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