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 
Fundamentals 
Using python interpreter 
Running Simple python programs 
Variables 

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

Module 3: Functions, File Handling 
Functions 
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 
Constructor 
Objects, Instances and classes 
Encapsulation 
Polymorphism 
Inheritance and the object hierarchy

Module 5: Project Development Skills & Techniques
SDLC
Coding Standards
Version control
Dynamic code analysis 
Continuos integration
Logging
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
PCA/LDA
Regularization
Bagging, Boosting