Data science and Machine Learning

Assignments

Data Pre Process

Assignment

Python Basics

Assignment

Function and Classes

Assignment

Python Libraries

Assignment

Python Pandas and File Reading

Assignment

Python Data Science Basics

Assignment

Python Visualization

Assignment

Presentation of Data Science

Assignment

Start Level Program

Assignment

Final Project

Assignment

DATASETS

Datasets

ZIP

Day -1 "Data Science Pyramid and Job Roles No-Code Data Science Tool AI vs ML vs DS vs DL"

"Data Science Pyramid and Job Roles No-Code Data Science Tool AI vs ML vs DS vs DL"

ZIP

Day-2 "Data Cleaning Data Preprocessing"

Data Cleaning Data Preprocessing

ZIP

Day-3"Statistical Analysis Feature Engineering"

Statistical Analysis Feature Engineering

ZIP

Day - 4 "Machine Learning Basics Supervised Machine Learning Cross-Validation and Model Evaluation"

Machine Learning Basics Supervised Machine Learning Cross-Validation and Model Evaluation

ZIP

Day - 5"Feature Importance and Model Interpretation Unsupervised Machine Learning"

Feature Importance and Model Interpretation Unsupervised Machine Learning

ZIP

Day -6Model Deployment and Maintenance

Model Deployment and Maintenance

ZIP

Day - 7 Introduction to Python and Virtual Environment

Introduction to Python and Virtual Environment

ZIP

Day - 9 Data Pre-Processing Basics

session - 9

ZIP

Day - 10 Introduction to Matplotlib and Seaborn

Introduction to Matplotlib and Seaborn

ZIP

Day -11 EDA (Exploratory Data Analysis)

EDA (Exploratory Data Analysis)

ZIP

Day-12Deep Dive into Plots and its attributes

session12

ZIP

Day-13Deep Dive into Plots and its attributes

session13

ZIP

Day-14Data Pre-Processing Advanced - Feature Engineering, Dimensionality Reduction (PCA, t-SNE)

session14

ZIP

Day-17Case Study 2: Program Guidance Implementing all the above process

session17

ZIP

Day -18Introduction to Machine Learning and its Types

session 18

ZIP

Day -19Basics of Models, Model Evaluation, and Cross Validation

session19

ZIP

Day - 20 Basics of Models, Model Evaluation, and Cross Validation

Basics of Models, Model Evaluation, and Cross Validation

ZIP

Day - 21 Supervised Learning - Regression

Supervised Learning - Regression

ZIP

Day -22Supervised Learning - Evalutaion Metrics - MSE, F1 score, R2, RMSE, Precision, Recall, Confusion mat

session22

ZIP

Day - 23 Supervised Learning - Classification

Supervised Learning - Classification

ZIP

Day - 24 Supervised Learning - Decision Tree and Ensemble Model

Supervised Learning - Decision Tree and Ensemble Model

ZIP

Day - 25Unsupervised Learning: association rule and anamoly detection

session25

ZIP

Day -26Unsupervised Learning - Clustering - KMeans, Hierarchical Clustering, Noise Reduction

session26

ZIP

Day -27Unsupervised Learning - Evaluation Metrics - Cross Validation and Confusion Matrix

session27

ZIP

Day -28Hyperparameter Tuning - Grid Search, Bayesian, Random Search

session28

ZIP

Day-29Basics of Model Deployment

session29

ZIP

Day - 30 Case Study 3: Program Guidance Implementing all the above process

Case Study 3: Program Guidance Implementing all the above process

ZIP

Day -32Introduction to Deep Learning, Neural Network, and NLP

session32

ZIP

Day -33Final Project - Day 2 - Presentation

session33

ZIP

Day - 34 LinkedIn Profile Optimization

LinkedIn Profile Optimization

ZIP

Day-35Fundamentals of Power BI and Tableau

session 35

ZIP

Day-36Fundamentals of DB (SQL and No SQL)

session36

ZIP

Day -37 Jenkins or Concourse Tool

session37

ZIP

Data science and Machine Learning

Course Overview

Schedule of Classes

Course Curriculum

Data science Batch 110

Assignments

Data Pre Process

Python Basics

Function and Classes

Python Libraries

Python Pandas and File Reading

Python Data Science Basics

Python Visualization

Presentation of Data Science

Start Level Program

Final Project

DATASETS

Datasets

Day -1 "Data Science Pyramid and Job Roles No-Code Data Science Tool AI vs ML vs DS vs DL"

"Data Science Pyramid and Job Roles No-Code Data Science Tool AI vs ML vs DS vs DL"

Day-2 "Data Cleaning Data Preprocessing"

Data Cleaning Data Preprocessing

Day-3"Statistical Analysis Feature Engineering"

Statistical Analysis Feature Engineering

Day - 4 "Machine Learning Basics Supervised Machine Learning Cross-Validation and Model Evaluation"

Machine Learning Basics Supervised Machine Learning Cross-Validation and Model Evaluation

Day - 5"Feature Importance and Model Interpretation Unsupervised Machine Learning"

Feature Importance and Model Interpretation Unsupervised Machine Learning

Day -6Model Deployment and Maintenance

Model Deployment and Maintenance

Day - 7 Introduction to Python and Virtual Environment

Introduction to Python and Virtual Environment

Day - 9 Data Pre-Processing Basics

session - 9

Day - 10 Introduction to Matplotlib and Seaborn

Introduction to Matplotlib and Seaborn

Day -11 EDA (Exploratory Data Analysis)

EDA (Exploratory Data Analysis)

Day-12Deep Dive into Plots and its attributes

session12

Day-13Deep Dive into Plots and its attributes

session13

Day-14Data Pre-Processing Advanced - Feature Engineering, Dimensionality Reduction (PCA, t-SNE)

session14

Day-17Case Study 2: Program Guidance Implementing all the above process

session17

Day -18Introduction to Machine Learning and its Types

session 18

Day -19Basics of Models, Model Evaluation, and Cross Validation

session19

Day - 20 Basics of Models, Model Evaluation, and Cross Validation

Basics of Models, Model Evaluation, and Cross Validation

Day - 21 Supervised Learning - Regression

Supervised Learning - Regression

Day -22Supervised Learning - Evalutaion Metrics - MSE, F1 score, R2, RMSE, Precision, Recall, Confusion mat

session22

Day - 23 Supervised Learning - Classification

Supervised Learning - Classification

Day - 24 Supervised Learning - Decision Tree and Ensemble Model

Supervised Learning - Decision Tree and Ensemble Model

Day - 25Unsupervised Learning: association rule and anamoly detection

session25

Day -26Unsupervised Learning - Clustering - KMeans, Hierarchical Clustering, Noise Reduction

session26

Day -27Unsupervised Learning - Evaluation Metrics - Cross Validation and Confusion Matrix

session27

Day -28Hyperparameter Tuning - Grid Search, Bayesian, Random Search

session28

Day-29Basics of Model Deployment

session29

Day - 30 Case Study 3: Program Guidance Implementing all the above process

Case Study 3: Program Guidance Implementing all the above process

Day -32Introduction to Deep Learning, Neural Network, and NLP

session32

Day -33Final Project - Day 2 - Presentation

session33

Day - 34 LinkedIn Profile Optimization

LinkedIn Profile Optimization

Day-35Fundamentals of Power BI and Tableau

session 35