₹55,000

Full Stack Data Science CT Program with Job Simulation - Weekday Batch[Nov'2022]

Course
165 Lessons

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Induction Sessions about [AI,DS,ML,DA,DL,PY]

Study Plan for DS_DA_AI_ML_DL.mp4

Mindmaps

From Project Stage (One-on-One Mentoring).pdf

Types of Analytics.pdf

Statistics Mindmap.pdf

Python Programming_Mindmap.pdf

ML FlowChart.pdf

Machine Learning Math Deep Dive.pdf

ML Project Framework.pdf

Weekday Live Lectures!!!

Intro Sesson.mp4

Lecture-1[Nov-26].mp4

Lecture-2[Nov-27].mp4

Lecture-3(Nov-29).mp4

Practice Session-1[Nov-30].mp4

Lecture-4[Dec-2].mp4

Practice session-2(Dec-2).mp4

Lecture-5[Dec-5].mp4

Lecture-6[Dec-7].mp4

Lecture-7[Dec-8].mp4

Practice Session-3[Dec-9].mp4

Lecture-8[Dec-12].mp4

Practice Session-4[Dec-13].mp4

Lecture-9[Dec-14].mp4

Practice session-5(Dec-15).mp4

Lecture-10(Dec-16).mp4

Lecture-11[Dec-19].mp4

Developing Data Analytics Dashboard using Excel.mp4

Developing Data Analytics Dashboard using Tableau.mp4

Practice Session-6[Dec-20].mp4

Lecture-12[Dec-21].mp4

Practice session-7[Dec-22].mp4

Lecture-13[Dec-27].mp4

Lecture-14[Dec-28].mp4

Practice Session-8[Dec-29].mp4

Lecture-15[Jan-2].mp4

Lecture-16[Jan-3].mp4

Practice session -9[Jan-4].mp4

Lecture- 17[Jan-5].mp4

Practice session-10[Jan-6].mp4

Lecture-18[Jan-9].mp4

Practice Session-11[Jan-10].mp4

Lecture-19[Jan-11].mp4

Lecture-20[Jan-12].mp4

Practice Session-12[Jan-13].mp4

Lecture-21[Jan-17].mp4

Lecture-22[Jan-19].mp4

Practice Session-13 [Jan-20].mp4

Lecture-23[Jan-23].mp4

Lecture-24[Jan-25].mp4

Practice Session-14[Jan-30].mp4

Lecture-25[Feb-01].mp4

Practice session-15(Feb-3).mp4

Lecture-26[Feb-7].mp4

Lecture-27[Feb-13].mp4

Lecture-28[Feb-14].mp4

Project-CRM.zip

Lecture-29[Feb-16].mp4

Lecture-30[Feb-28].mp4

Materials

Data Science & AI 2.pdf

Statistics Full.pptx

Python Installation

Anaconda Installation Link

Installing+Python.1.pdf

Statistics Files

4.2.The-z-table.xlsx

EDA Excel File.xlsx

Statistics Full.pptx

2.8.+Skewness_lesson.xlsx

Fall Prediction(Statistics Assignment)

Fall Prediction Assignment Task

Python Script Files

Basics of Python Commands'Nov22.ipynb

1. Basics of Python.ipynb

Lists in Python-4.ipynb

Tuple in Python.py

Sets in Python.py

Dictionary in Python.py

If Statement-2.ipynb

Loops in python.ipynb

Python Basics Day- 7 functions (part 2) .ipynb

Python Basics Day - 8 (Lambda function).ipynb

OOPS Deep Dive.ipynb

OOPS Deep Dive 2.ipynb

OOPS MaterialV1.pdf

Hackathon[Programming]

Hackathon-1(Questions) 2.ipynb

Hackathon-1(Answers).ipynb

OOPS Hackathon

BEPEC Programming Final Assessment.docx

Pandas Commands

Pandas Commands[Nov'22].ipynb

CheatSheet_PandasDataFrame.pdf.pdf

clevelanda.csv

Pandas Assignment

Work on this 10 Minutes to Pandas

Company_Data (1).csv

Data Cleaning Commands

Data Cleaning using Sklearn & Pandas.ipynb

Data Cleaning_Complete-2-2.ipynb

Data Cleaning Assignment-1

Lending Data.xlsx

What to do in Assignment?

Data Cleaning Assignment -2

Automobile price data _Raw_.csv

Numpy & Seaborn Commands

Numpy Commands.ipynb

Data Visualization with Seaborn (1).ipynb

Company_Data (1).csv

Power BI Dashboard

Sales Dashboard1.pbix

Sample - Superstore.xls

Employee Performance Project

INX_Future_Inc_Employee_Performance_CDS_Project2_Data_V1.8.xls

summary of project.docx

MySQL Files

Create a Database.sql

Data Analysis using SQL.sql

SQL Material_BEPEC.pdf

empl1.csv

empl2.csv

empl3.csv

SQL Joins.sql

Data Analytics Project

Food_services.docx-EmbeddedFile.xlsx

Food_services.docx

Probability Distribution

Probability -1.ipynb

Probability Distribution, Hypothesis.pdf

Probability Distribution.ipynb

ML Text Book

master_machine_learning_algorithms.pdf

ML Blueprint.ipynb

ML Classification Script

ML Blueprint Dec'22.ipynb

ML Regression Script

Regression Blueprint Nov'22.ipynb

Unsupervised Learning Script

K-Means Clustering.ipynb

PCA with K-Means Clustering.ipynb

Boston.csv

Linear Regression Math

How to Prepare Machine Learning Math for Interviews?.mp4

2.Why to use Linear Regression?.mp4

3. What is Linear Regression.mp4

4. CaseStudy on Linear Regression.mp4

5. Math behind Linear Regression - 1.mp4

6. Math behind Linear Regression - 2.mp4

7. Math behind OLS Linear Regression.mp4

8. Assumptions of Linear Regression.mp4

9. Evaluation Metrics of Regression Models.mp4

10. Accuracy Improving Techniques.mp4

11. Regularization Techniques.mp4

Linear Regression Material - Applied ML(BEPEC).pdf

Linear Regression Script

Linear Regression End to End Script.ipynb

concrete.csv

Logistic Regression

1. Why Logistic Regression.mp4

2. Math behind Logistic Regression.mp4

3 Evaluation Metrics behind Classification Algorithms.mp4

4. ROC & AUC Curve.mp4

Logistic Regression Material - Applied ML(BEPEC) copy.pdf

Logistic, DT, GridSearch, CV.ipynb

Plotting Decision Tree.ipynb

Decision Tree_ROC.ipynb

Decision Tree_ROC-2.ipynb

ML Simple Project

credit_card_default_case_study_0.docx

default_of_credit_card_clients_0.xlsx

Decision Tree, Random Forest, Gradient Boosting

5. Introduction to Decision Tree.mp4

6. Intuition Behind Decision Tree.mp4

7. Math Behind Decision Tree.mp4

8. Math behind Decision Tree using GINI.mp4

9. Drawbacks of Decision Tree.mp4

10. Random Forest & Gradient Boosting.mp4

11. Handling Imbalanced Dataset.mp4

12. Feature Selection Techniques.mp4

Deployment Files

ml_app.py

ML Streamlit Deployment .ipynb

Iris.csv

MLOps Material.pdf

NLP

1_Introduction to NLP & NLP Applications.mp4

2_NLP Challenges.mp4

3_NLP Pre-Processing Steps.mp4

4_NLP Feature Extraction.mp4

5_NLP Practical Example.mp4

NLP Complete - BEPEC (1).pdf

NB.csv

NLP Bird Eye View.pdf

Feature Selection Techniques.ipynb

Spam & Non_spam Classification-2-2.ipynb