Sale Date Ended
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
Note: Carry your personal laptops. Laptops are compulsory.