Data Science Master Course
About Course
Summary
Data Science is one of the hottest fields of the 21st century. It is in high demand across the globe with bigwigs like Amazon, Google, and Microsoft paying handsome salaries and perks to data scientists. This course provides you with a great kick-start in your data science journey by starting with Python Basics, Data Visualization, Data Scraping, Building Web Scrappers using Scrapy, Data Cleaning, and applying various machine learning algorithms like Linear Regression, Logistic Regression, Decision Trees, Naive Bayes, Principal Component Analysis, Feature Engineering, T-SNE Visualizations, Deep Learning & Reinforcement Learning for video games. The complete course is very practical and makes sure you do sufficient hands-on practice on the concepts taught by solving assignments and challenges.
Course Highlights
- Python Basics
- Data Science Pipeline
- Data Acquisition & Analysis
- Web Scrapping, Google API, FacebookAPI
- Scrapy, Beautiful Soup
- Data Cleaning & Visualization
- Machine Learning from scratch
- Supervised Learning
- Unsupervised Learning
- Deep Learning for Vision
- Deep Learning for NLP
- Frameworks – Numpy, Pandas, Sci-kit, Keras, Matplotlib, Scrapy, BeautifulSoup etc.
Course Content
Course Introduction
-
Data Science Course Introduction
00:00 -
New Lessons
00:00
Data Science Quickstart Mode
Python 01 – Basics
Python 02 – Functions
Python 03 – Builtin Data Structures
Python 04 – Object Oriented Programming & Modules
Python 05 – File and Error Handling
Iteration Protocol and Generators (Intermediate Python)
Python Practice Problems
Asynchronous Programming in Python (Optional)
Basics of Git/Github
Data Acquisition – Web Scrapping
Data Acquisition – Using Web APIs
Challenge – Chuk Norris Jokes
Data Acquisition – Web Crawler using Scrapy
Challenge – Scrape a Shopping Website
Project – Automating Codechef Submissions (Selenium)
Project – Creating a Telegram Bot
Getting started with Machine Learning
Numpy
Linear Algebra
Data Visualisation
Seaborn
Pandas
Project – Movie Recommendation System
Probability Distribution & Statistics
K-Nearest Neighbours
Challenge – Nearest Neighbors
Project – Face Recognition
Challenge – Create SnapChat Fillters
Linear Regression
Challenge – Hardwork Pays Off
Linear Regression – II Multiple Features
Sci-kit Learn Introduction
Optimisation Algorithms
Locally Weighted Regression (LOWESS)
Maximum Likelihood Estimate (MLE) [Proof]
Challenge – Air Pollution Regression
Logistic Regression
Challenge – Chemical Classifier
Data Prepreprocessing
Feature Selection
PCA
Natural Language Pre-preprocessing
Naive Bayes Classifier
Project – Movie Review Classification
Challenge – Movie Rating Prediction
Decision Trees & Random Forests
Challenge – Titanic Survivor Prediction
Support Vector Machines
Project – Image Classification using SVM
Challenge – PokeMon’s Goa Trip
Clustering Fundamentals (Unsupervised)
Project – Extracting Dominant Colors
Challenge – Clustering Challenges
Deep Learning Introduction
Neural Networks – MLP’s
Project – Image Classsification using Neural Network
Project – IMDB Sentiment Analysis
Neural Networks Challenges
Convolutional Neural Networks
Training – Data Loaders, Augmentation, Colab
Project – COVID Detection using CNN
CNN Case Studies
Digging Deeper into Convnets
Transfer Learning
Build a ‘Mobile Friendly CNN’ Challenge
Markov Chains for Text Generation (NLP)
Challenge – Lyrics Generator!
Recurrent Neural Networks
Word Embeddings – Word2Vec
Challenges – Word Embeddings
Project – Emoji Prediction
RNN/LSTM Challenges
Reinforcement Learning
Mountain Car Challenge
Generative Adversarial Networks
Deep Convolutional GAN’s (DCGANs)
All Quizzes
All Challenges
CAPSTONE PROJECT – AI Image Caption Bot
ML + Web Project: Creating ML Based Web Service (Django)
Integrating ML Models with Web (Flask)
Tensorflow Introduction
Introduction to PyTorch
Project Ideas
Live Sessions
SQL
Student Ratings & Reviews
