Master in Data Science

Start from basics, become a industry ready data scientist!

Instructors
Rahul Sharma
Course
English

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, 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.


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

Syllabus/ Course Content


Course Introduction

3 Items | Duration : 40 Mins

Data Science Course Introduction
25 Mins
C++ Header Files
20 Mins
C++ Functions
10 Mins

Data Science Quickstart Mode

7 Items | Duration : 1 hr

Python Libraries Quikstart Mode
15 Mins
Quikstart Python
12 Mins
Quikstart Numpy
12 Mins
Quikstart - Data Visualization
6 Mins
Quikstart Open CV
11 Mins
Quikstart Pandas
10 Mins
Challenge- Instagram Photo Collage
12 Mins

Read More..

Python 01 - Basics

13 Items | Duration : 1 hr

Python 3 Installation[Windows]
3 Mins
Getting Started I
8 Mins
Getting Started II
8 Mins
Working with Jupyter Notebooks
7 Mins
Setup Jupyter themes
2 Mins
Python basics
14 Mins
Python- Variables and Arthimetic Operators
6 Mins
Python- Variables [PDF]
15 Mins
Control Flow I
13 Mins
Control Flow II
7 Mins
Operator and Expression I
7 Mins
Operator and Expression II
7 Mins
Operator and Expression III
9 Mins

Python 02 - Function

6 Items | Duration : 54 min

Introduction to Functions
6 Mins
Python functions- Return, Local, Global
11 Mins
Python functions-packing arguments
12 Mins
Python functions-Lambda Function
6 Mins
Python functions- Decorators
12 Mins
Python functions- args and kwargs
4 Mins

Python 03 - Builtin Data Structures

9 Items | Duration : 54 min

Python- Introduction to Data Structures
6 Mins
Python- Introduction to Strings
9 Mins
Python-Strings Operations
13 Mins
Python- Introduction to Lists
13 Mins
Python- Introduction to Tuples
11 Mins
Python-Sequences [PDF]
15 Mins
Python- Introduction to Dictionaries
9 Mins
Python- Introduction to Sets
5 Mins
Comprehension of Data Structure in Python
5 Mins

Python 04 - Object Oriented Programming & Modules

9 Items | Duration : 1hrs

Python Class 01
16 Mins
Python Class 02
15 Mins
Python Class 03
10 Mins
Python Class 04
9 Mins
Python Class 05
8 Mins
Python Class 06
11 Mins
Python Modules 1
11 Mins
Python Modules 2
10 Mins
Python Modules 3
10 Mins

Python 05 - File and Error Handling

6 Items | Duration : 47min

Python-File Handling
11 Mins
Python - Working with JSON
15 Mins
Error Handling 1
5 Mins
Error Handling 2
4 Mins
Error Handling 3
5 Mins
Error Handling 4
11 Mins

Iteration Protocol and Generators (Intermediate Python)

3 Items | Duration : 30min

Python-Iteration Protocol
11 Mins
Iterators in Python
9 Mins
Generators in Python
9 Mins

Asynchronous Programming in Python (Optional)

5 Items | Duration : 1hrs

Async Programming in Python-1
10 Mins
Async Programming in Python-2
10 Mins
Async Programming in Python-3
11 Mins
Python- AsyncIO 1
17 Mins
Python- AsyncIO 2
12 Mins

Basics of Git/Github

3 Items | Duration : 21min

Introduction to Git and Github
10 Mins
Making a repository on Github
7 Mins
Cloning a repository on Github
4 Mins

Data Acquisition - Web Scrapping

6 Items | Duration : 37min

Web Scraping 01 - Fetching Data
6 Mins
Web Scraping 02 - Using Beautiful Soup
6 Mins
Web Scraping 03 - Parsing HTML Tables
9 Mins
Web Scraping 04 - Creating CSV
3 Mins
Web Scraping 05 - Cleaning Data
9 Mins
Web Scraping 06 - Scraping Local Files
1 Mins

Data Acquisition - Using Web APIs

5 Items | Duration : 30min

Web APIs 01 - OpenWeatherMap API
8 Mins
Web APIs 03 - Google API/Authentication
5 Mins
Web APIs 02-Using Facebook API
4 Mins
Web Scraping -Image Scrapping -I
5 Mins
Web Scraping -Scrapping Images- II
7 Mins

Data Acquisition - Web Crawler using Scrapy

6 Items | Duration : 46min

Scrapy - Getting Started
2 Mins
Scrapy installation & Troubleshoot
15 Mins
Scrapy - Creating our first Spider
6 Mins
Scrapy - Using Shell & Selectors
10 Mins
Scrapy - Parsing response as JSON
3 Mins
Scrapy - Recursive Crawler
7 Mins

Getting started with Machine Learning

4 Items | Duration : 40min

Machine Learning Pipeline
4 Mins
Supervised Learning Introduction
14 Mins
Supervised Learning Introduction
7 Mins
RL1- Reinforcement Learning Introduction
13 Mins

Numpy

4 Items | Duration : 1hr

Python Numpy Basics
25 Mins
Python - Random Generators Numpy
6 Mins
Python - Statiscal Computation using Numpy
9 Mins
Numpy- A Visual Introduction
15 Mins

Linear Algebra

4 Items | Duration : 1hr

Linear Algebra
15 Mins
Linear Algebra - Matrices, Tensors, Transpose
26 Mins
Linear Algebra- Broadcasting, Matrix, Hadamard product
10 Mins
Linear Algebra- Norm, Det, Inverse, Linear Equations
20 Mins

Data Visualisation

7 Items | Duration : 1hr

DV 01- Line Plots
11 Mins
DV 02- Scatters Plots
4 Mins
DV 03- Bar Graphs
8 Mins
DV 04- Pie Charts
6 Mins
DV 05- Normal Distribution
11 Mins
DV 06- Histograms
6 Mins
DV 07-Movie Data Visualization
11 Mins

Seaborn

4 Items | Duration : 1hr

Seaborn -1
17 Mins
Seaborn -2
18 Mins
Seaborn -3
20 Mins
Seaborn -4
12 Mins

Pandas

4 Items | Duration : 1hr

Pandas basic -1
17 Mins
Pandas basic -2
12 Mins
Pandas - MNIST Dataset
23 Mins
Pandas - Movie Dataset
8 Mins

Probability Distribution & Statistics

4 Items | Duration : 54min

Data Visualization - Normal Distribution and Histogram
16 Mins
Data Visualization - Normal Distribution -II
8 Mins
Multivariate Normal/Gaussian Disribution using Numpy
8 Mins
ML Interview Question - Std Deviation in a Running Stream
7 Mins

K-Nearest Neighbours

2 Items | Duration : 1hr

Knn- Introduction
21 Mins
Knn- Implementation
38 Mins

Linear Regression

9 Items | Duration : 2hr

Linear Regression
21 Mins
Gradient Descent Implementation
9 Mins
Gradient Descent Algorithm
21 Mins
Gradient Descent Update Rule for Regression
10 Mins
Linear Regression - Data Preparation
8 Mins
Linear Regression - Implementing Gradient Descent
12 Mins
Surface Plots and Countors
13 Mins
Linear Regression - Visualising Loss Function & Gradient Descent Trajectory
20 Mins
Interactive Plots using Matplotlib
5 Mins

Linear Regression - II Multiple Features

4 Items | Duration : 1hr

Linear Regression- Maths for Multiple Features
19 Mins
Boston Housing Dataset
11 Mins
Linear Regression- Loop Based Implementation for Multiple Features
16 Mins
Linear Regression- Efficient Code using Vectorization
14 Mins

Sci-kit Learn Introduction

2 Items | Duration : 11min

Sklearn 01- Generating Regression Data
8 Mins
Sklearn 01- Implementation Regression Model
3 Mins

Optimisation Algorithms

3 Items | Duration : 35min

01 GD vs Mini Batch vs SGD
8 Mins
02 Mini Batch GD
8 Mins
03 Mini Batch GD Implementation & Advantanges
18 Mins

Locally Weighted Regression (LOWESS)

9 Items | Duration : 1hr

Closed Form Solution of Linear Regression
15 Mins
Closed Form Solution - Code Tutorial
15 Mins
Closed Form Solution - Derivation Notes
15 Mins
Locally Weighted Regression (LOWESS)
11 Mins
LOWESS - Deriving Closed Form Solution
13 Mins
LOWESS Implementation 1 - Data Preparation
6 Mins
LOWESS Implementation 2 - Computing W
11 Mins
LOWESS Implementation 3 - Making Predictions
8 Mins
LOWESS Implementation 4 - Effect of Bandwidth Parameter
5 Mins

Maximum Likelihood Estimate (MLE) [Proof]

2 Items | Duration : 39min

Linear Regression - Maximum Likelihood Estimation - I (Optional)
25 Mins
Linear Regression - Maximum Likelihood Estimation - II (Optional)
14 Mins

Logistic Regression

12 Items | Duration : 2hr

Logistic 01 - Introduction
15 Mins
Logistic 02 - Loss Function
13 Mins
Logistic 03 - Maximum Likelihood Estimates
12 Mins
Logistic 04 - Importance of Maximising Likelihood
6 Mins
Logistic 05 - Gradient Descent Update
9 Mins
Logistic 06 - Data Preparation
14 Mins
Logistic 07 - Data Normalisation
8 Mins
Logistic 08 - Implementation - I
12 Mins
Logistic 09 - Implementation - II
15 Mins
Logistic 10 - Decision Surface Visualisation
4 Mins
Logistic 11 - Prediction & Accuracy
8 Mins
Logistic Regression Using Sk-Learn
3 Mins

Logistic Regression

1 Items | Duration : 2hr

Logistic 01 - Introduction
15 Mins

Enroll Now

PLAN FEES

  • 50 Live Classes
  • Certificate
  • Live Project
  • LMS

₹ 4999 + GST

Valid for 6 Months

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