Applied Data Science and Machine Learning | Amar iSchool

Applied Data Science and Machine Learning

From the amount of data that is being produced every day, its use in business, politics, administration, or social work is increasing day by day. The arrival of 5G will further speed up the amount of this data. Then we may have to work with big data all the time. Currently, the global job growth rate in data science is around 650%. Python is like the Swiss Army Knife of technology; Python solves any task starting from the web. So if you don’t enroll in this fantastic combo course in Python and Data Science, you will regret later.

Beginner 0(0 Ratings) 11 Students enrolled
Created by Mohammad Sabik Irbaz Last updated Tue, 08-Jun-2021 Bengali
What will i learn?
  • Learn Advanced Python3

Curriculum for this course
4 Lessons 00:00:00 Hours
Course Inauguration
1 Lessons 00:00:00 Hours
  • Kick Off class
  • Test-Doc
  • Testing
  • test
Requirements
  • Computer Fundamental
  • Basic Programming knowledge in C/Python
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Description


WeekDayTopic

Day 1

(Course Inauguration)

  • A Birds Eye View on Overall Course

  • Review on Basic Programming Techniques


    Installing Anaconda Python Distribution

1

Day 2

(Environment Setup)

  • Introduction to Kaggle and Google Collaboratory

  •  Jupyter Notebook [Coding Environment]

  • Introduction to VSCode


Weekly Assignment - 1

Mock Interview Test (Skill measurement before the course)


Day 1

( Python basics)

  • Basic Syntax

  • Python Data Types

  • Conditional Statements

  • Loops

  • Strings

2

Day 2

( Python Data Structures)

  • Lists

  • Tuples

  • Sets

  • Dictionaries

  • Functions

  • Exception Handling

  • File I/O


Weekly Assignment - 2



Live Class -  1



Day 1

(Other Advanced Python Techniques)

  • Concise Overview of Object-Oriented Programming

  • Numpy [Beginner]

3

Day 2

(Other Advanced Python Techniques (cont.))

  • Numpy [Advanced]

  • Scipy

  • Python Imaging Library


Weekly Assignment - 3



Live Class - 2

Live Class - 2


Day 1

(Data Science Pipeline)

  •   Data Collection

  •  Data Exploration

  • Data Preprocessing

  • Data Modeling

  • Model Validation

  •  Reporting

4

Day 2

(Data Collection)

  • Web scraping (from API)

  • Web scraping (from HTML)

Guidance for Academic Research related to ML



Weekly Assignment - 4

Weekly Assignment - 4


Live Class - 3

Live Class - 3


Day 1

(Data Exploration)

  • Matplotlib

  • Plots, Charts

  • Histograms

  • Heatmaps

  • Correlation Matrix

5

Day 2

(Data Exploration Tools)

  • Power BI

  • Tableau

Project Proposal (Propose a Real Life Project Topic)


Weekly Assignment - 5

Live Class - 4


  • Weekly Assignment - 5

  • Live Class - 4



Day 1

(Data Preprocessing)

  • Pandas

  • Pandas Object Creation

  • Pandas Data View

  • Data Selection

  •  Pandas Operations

6

Day 2

(Data Preprocessing (cont.))

  • Data Merging

  • Data Grouping

  • Data Reshaping

  • Time Series Data

  • Pandas Plotting

  • Pandas Data In/Out


Weekly Assesment-6

Live Class-5

Day 1 

(Advanced Data Preprocessing)

  • Handling Missing Values Handling Outliers

  • Handling Imbalanced Class Problem 

  • Handling Categorical Data 

  • Data Discretization 

  • Data Transformation

  • Data Segregation

  • Feature Selection 

  • Feature Engineering 

7

Day 2


Introduction to Knime

Weekly Assignment 7

Live Class -6


Day 1

Probability Review
8

Day 2


Statistics Review


Project Topic Finalizing 


Live Class - 7

Day 1 

  • Linear Algebra Review


9Day 2
  • Review on Database



Live Class 8

Day 1
  • Review on Calcululus

10Day 2
  • Git 

  •  Docker

Project Milestone Report - 1


Live Class 9

Day 1 

(Introduction to Machine Learning)

AI vs ML vs DL 

● ML Origins

● ML State of the Art

 ● Type of ML Algorithms

11

Day 2 

(Supervised Learning)

Cost Function

● Gradient Descent 

● Linear Regression

● Polynomial Regression 


Weekly Assignment 8

Live Class 10

Day 1 

(Supervised Learning (Cont))

 Logistic Regression 

● Naive Bayes 

● k-Nearest Neighbors

● Confusion Matrix 

● Precision, Recall, F1-Score, 

● AUC, IOU, ROC, Elbow Method

12

Day 2 

(Supervised Learning (cont.)) 



Weekly Assignment 9

Live Class 11

Day 1  

(Ensemble Learning)

 Bagging 

● Boosting 

● Stacking 

● Random Forest

13

Day 2

(Hyperparameter Tuning) 

 Bias-Variance Tradeoff

● Cross-Validation

● Hyperparameter Tuning


Weekly Assignment - 10

Live Class 12

Day 1 

(Unsupervised Learning)

 K-means Clustering 

● DBSCAN Clustering 

● Hierarchical Clustering 

 Principal Component Analysis (PCA) 
14

Day 2 

(Dimensionality Reduction)

Singular Value Decomposition (SVD) 

● Linear Discriminant Analysis (LDA)

Project Milestone Report - 2


Weekly Assignment - 11

Live Class 13

Day 1 

(Deep Learning)

 Introduction to Neural Network

● Activation Functions 

● Multi-layer Perceptron 

● Softmax Regression 

15

Day 2 

(Deep Learning(cont.))

Backpropagation 

● Gradient Descent

● Mini-batch gradient descent 


Weekly Assignment - 12

Live Class 14 

Day 1 

(PyTorch)

Introduction to PyTorch

 ● Autograd 

16

Day 2 

(Optimization, Normalization)

Optimization 

● Momentum, RMSProp, Adam


Weekly Assignment - 13

Live Class 15

Day 1 

(Initialization and Regularization)

 Initialization

● Regularization

● Dropout Regularization

17

Day 2 

(Normalization and Implementation Tricks)

 Batch Norm, Layer Norm 

● Learning Rate Scheduling

● Early Stopping

● Exploding and Vanishing Gradient


Weekly Assignment - 14

Live Class 16

Day 1 

(Convolutions)

 Introduction to Convolutions

● 1D, 2D Convolutions

● Pooling 

18

Day 2 

(ConvNets) 

Introduction to ConvNets

● Training ConvNets

● Transfer Learning 

● Different types of ConvNets

● Hyperparameter Tuning on existing work

Project Milestone Report - 3


Weekly Assignment - 15

Live Class 17

Day 1 

 (Flask) 

Introduction to Flask 

● Building REST APIs

● Exporting Machine Learning Models 


19

Day 2 

(Deployment in Cloud)

Deploying ML Models

Deploying APIs in Google Cloud, AWS


Day 3
  • More on ML Research and Development


Weekly Assignment - 16

Live Class 18 

Project Demonstration 



20

Mock Interview Test (Skill measurement after the course)

ML Interview Guideline

 Call for Internship






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