BEPEC Career Transition Programs | www.bepec.in | Bangalore | Learn Real-Time Project Pipeline /Data Science/Artificial Intelligence CT Program-Holistic Learning Approach!!!

  • ₹45,000

Data Science/Artificial Intelligence CT Program-Holistic Learning Approach!!!

  • Course
  • 598 Lessons

Contents

End-to-End Real-Time Project Demonstrations

End-to-End KPI Dashboard Creation with Power BI.mp4
  • (1h 25m 00s)
  • 253 MB
Star Schema Design Using Power Query.mp4
  • (1h 23m 08s)
  • 279 MB
End-to-End Data Analytics Project using Python, SQL & Power BI.mp4
  • 24 mins
  • 341 MB
End-to-End Data Analytics using AWS.mp4
  • (1h 22m 48s)
  • 319 MB
Real-Time Project Demonstration on EDA.mp4
  • (1h 01m 55s)
  • 180 MB
Real-Time Project Demonstration using Probability & Hypothesis.mp4
  • (1h 34m 19s)
  • 282 MB
Data Warehousing End-To-End Using SnowFlake & Window Functions.mp4
  • (1h 32m 33s)
  • 524 MB
Data Science End-To-End Project using SnowPark, Stored Procedures & Power BI!!!.mp4
  • (1h 31m 06s)
  • 599 MB

Data Analytics & Data Science Roadmap

1. Data Analytics BEPEC Roadmap.mp4
  • 14 mins
  • 349 MB
2. Data Science BEPEC Roadmap.mp4
  • 7 mins
  • 350 MB
3. AI BEPEC Roadmap.mp4
  • 6 mins
  • 296 MB

Mindmaps

Statistics Mindmapv1.pdf
  • 167 KB
Types of Analytics.pdf
  • 273 KB
Real-Time Project Procedure.pdf
  • 45.3 KB
NLP Bird Eye View.pdf
  • 21.7 KB
ML FlowChart.pdf
  • 19.9 KB
ML Training Recap.pdf
  • 39.8 KB
Machine Learning_Project Approach.pdf
  • 904 KB
Machine Learning Math Deep Dive.pdf
  • 311 KB
Data Science Interview Revision.pdf
  • 1.2 MB
Data Analytics Project Procedure.pdf
  • 140 KB
Python Programming_Mindmap.pdf
  • 227 KB

SQL NOTES

SQL for Beginners.pdf
  • 783 KB
SQL Data Base.pdf
  • 653 KB
SQL Interview Questions.pdf
  • 616 KB
SQL Cheat Sheet.pdf
  • 767 KB
Top 10 SQL Commands.pdf
  • 831 KB
SQL Tips & Tricks.pdf
  • 761 KB

1. Induction Classes

1. What is Data, type of data & Importance of data.mp4
  • 20 mins
  • 207 MB
2. What are math equations? Different math equations.mp4
  • 12 mins
  • 277 MB
3. Fourier Series & Fourier Transformation.mp4
  • 11 mins
  • 236 MB
4. History of Probability & Types.mp4
  • 16 mins
  • 214 MB
5. Why Linear Algebra for ML?(Easy way).mp4
  • 20 mins
  • 718 MB
6. Importance of Calculus.mp4
  • 8 mins
  • 205 MB
7. Elements of RL.mp4
  • 18 mins
  • 237 MB

Applied Statistics with Excel

1-introduction-to-statistics.mp4
  • 11 mins
  • 331 MB
2-properties-of-data.mp4
  • 8 mins
  • 336 MB
3-sample-vs-population.mp4
  • 7 mins
  • 319 MB
4-types-of-sampling-techniques.mp4
  • 6 mins
  • 291 MB
5-what-is-eda.mp4
  • 9 mins
  • 307 MB
6-analysing-data-with-eda-using-excel.mp4
  • 27 mins
  • 350 MB
7-measures-of-dispersion.mp4
  • 9 mins
  • 326 MB
8-skewness-normality.mp4
  • 5 mins
  • 317 MB
9-correlation-scatter-plot.mp4
  • 11 mins
  • 310 MB
10-barplot-lineplot.mp4
  • 5 mins
  • 311 MB
11-recap-of-topics.mp4
  • 2 mins
  • 286 MB

Mastering Python(New Version)

introduction-to-python.mp4
  • 16 mins
  • 362 MB
1-introduction-to-python.mp4
  • 6 mins
  • 321 MB
2-basics-of-python.mp4
  • 4 mins
  • 293 MB
3-python-arithmetic-operators.mp4
  • 5 mins
  • 323 MB
4-python-variables.mp4
  • 5 mins
  • 322 MB
5-python-data-structures.mp4
  • 6 mins
  • 321 MB
6-python-control-flow.mp4
  • 7 mins
  • 338 MB
7-python-functions.mp4
  • 4 mins
  • 288 MB
8-python-libraries.mp4
  • 5 mins
  • 310 MB
different-list-commands.mp4
  • 29 mins
  • 343 MB
different-tuple-commands.mp4
  • 11 mins
  • 317 MB
different-set-commands.mp4
  • 13 mins
  • 298 MB
different-dict-commands.mp4
  • 12 mins
  • 309 MB
shallow-copy-deep-copy.mp4
  • 7 mins
  • 327 MB
string-modulo-operator.mp4
  • 12 mins
  • 342 MB
for-loop.mp4
  • 4 mins
  • 304 MB
list-comprehension.mp4
  • 5 mins
  • 313 MB
while-loop.mp4
  • 4 mins
  • 318 MB
conditional-statements.mp4
  • 28 mins
  • 355 MB
user-defined-functions.mp4
  • 7 mins
  • 313 MB
keyword-positional-default-arguments.mp4
  • 5 mins
  • 321 MB
variable-length-arguments.mp4
  • 3 mins
  • 309 MB
local-global-variables.mp4
  • 3 mins
  • 300 MB
lambda-expressions.mp4
  • 4 mins
  • 324 MB
regular-expressions.mp4
  • 4 mins
  • 324 MB
Introduction to Pandas.mp4
  • (1h 31m 45s)
  • 232 MB
leading data 1.xlsx
  • 2.04 MB
iris.csv
  • 3.63 KB
Pandas Deep Dive-2.ipynb
  • 1.19 MB
clevelanda (2).csv
  • 11.1 KB
2.8.+Skewness_lesson.xlsx
  • 34.9 KB
Statistics using Python.ipynb
  • 424 KB
Marketing_conversion_data.xlsx
  • 84 KB
Assignment DataSet - Implement Stats on this Dataset
  • 67.5 KB
Statistic powerpoint.pdf
  • 1.47 MB
Statistics Mindmap.pdf
  • 248 KB
Statistics using Python.mp4
  • (1h 37m 08s)
  • 281 MB
Python Stats Recap with Project Approach.pdf
  • 31.1 KB
Excel Stats File.xlsx
  • 14.1 KB
Automobile price data _Raw_.csv
  • 24.5 KB
Statistics with Python -2.mp4
  • (1h 36m 43s)
  • 386 MB
Project Allocation.mp4
  • 56 mins
  • 192 MB
Lending Data Assignment.pdf
  • 35.3 KB
Data Cleaning_Complete-2-3.ipynb
  • 146 KB
Pandas Deep Dive-2 (1).ipynb
  • 1.2 MB
clevelanda (1).csv
  • 11.1 KB
iris (1).csv
  • 3.63 KB
Lending Data.xlsx
  • 2.04 MB
Lending Data (1).xlsx
  • 2.04 MB
Data Cleaning.mp4
  • (1h 31m 09s)
  • 213 MB
Data Visualization with Seaborn-3.ipynb
  • 4.64 MB
plotly-4.ipynb
  • 5.54 MB
clevelanda.csv
  • 11.1 KB
Data Visualization with Seaborn and Plotly.mp4
  • (1h 28m 30s)
  • 209 MB
Numpy Commands-1.ipynb
  • 32 KB
Different Numpy Commands.mp4
  • 45 mins
  • 78.7 MB
Customer Analytics Roadmap_BEPEC (1).pdf
  • 583 KB
4.2.The-z-table.xlsx
  • 41.8 KB
Confidence Interval & Hypothesis Testing-3.ipynb
  • 34.6 KB
Types of Probability Distribution.ipynb
  • 150 KB
Statistics Mindmap (1).pdf
  • 2.91 MB
Real-Time Project with Confidence Interval & Probability.mp4
  • (1h 55m 49s)
  • 439 MB
Statistical Testing (1).pptx
  • 1.17 MB
Hypothesis Testing-3.ipynb
  • 25.1 KB
Hypothesis Testing.mp4
  • (1h 36m 07s)
  • 203 MB
Time Series.pdf
  • 752 KB
Moving Average Forecasting.ipynb
  • 224 KB
Forecasting Theory + Coding.mp4
  • (1h 37m 10s)
  • 294 MB
oops-1.mp4
  • (1h 17m 51s)
  • 270 MB
oops-2.mp4
  • (1h 34m 40s)
  • 405 MB
OOPS MaterialV1.pptx
  • 586 KB
OOPS Deep Dive.ipynb
  • 12.2 KB
OOPS Deep Dive 2.ipynb
  • 27.3 KB
Python Functions-2.ipynb
  • 22 KB
Loops in python-2.ipynb
  • 7.05 KB
Lists in Python-6.ipynb
  • 37.5 KB
Basics of Python v3-6.ipynb
  • 1020 KB
Python Basics Day - 4-TUPLES.ipynb
  • 23.8 KB
python Basics Day - 4 - SETS.ipynb
  • 12.2 KB
Python Basics Day - 8 (Lambda function)-2.ipynb
  • 9.18 KB
python-Basics Day - 5 - DICT-2.ipynb
  • 12.5 KB

2. Python

Session-1.mp4
  • (1h 14m 09s)
  • 151 MB
Session-2.mp4
  • (1h 35m 37s)
  • 175 MB
Session-3.mp4
  • (1h 02m 53s)
  • 135 MB
Session-4.mp4
  • (1h 04m 10s)
  • 135 MB
Session-5.mp4
  • (1h 01m 53s)
  • 203 MB
Python basics day-1 (Intro to Python) (1).ipynb
  • 37 KB
Python basics day -2 .ipynb
  • 2.15 MB
Session6.mp4
  • (1h 03m 11s)
  • 66.5 MB
Session-7.mp4
  • 56 mins
  • 106 MB
Session-8.mp4
  • (1h 04m 52s)
  • 158 MB
Python Basics Day - 4-TUPLES.ipynb
  • 23.8 KB
Python Basics Day- 3 - LIST.ipynb
  • 206 KB
Session-9.mp4
  • (1h 00m 49s)
  • 131 MB
python Basics Day - 4 - SETS.ipynb
  • 12.2 KB
Session-10.mp4
  • 51 mins
  • 118 MB
python-data-structures-05-STRINGS.ipynb
  • 29.4 KB
python-Basics Day - 5 - DICT.ipynb
  • 10.5 KB
Session-11.mp4
  • (1h 03m 14s)
  • 126 MB
assignment_on_loops.txt
  • 222 Bytes
Session-12.mp4
  • 24 mins
  • 48.8 MB
Session-13.mp4
  • (1h 01m 17s)
  • 141 MB
Python Basics Day - 6 (operators).ipynb
  • 15.8 KB
python-Basics Day -7-functions (part-1).ipynb
  • 25.3 KB
Python Basics Day - 6 - (Conditional - loops).ipynb
  • 147 KB
Python Basics Day- 7 functions (part 2) .ipynb
  • 76.3 KB
Session-14.mp4
  • 53 mins
  • 129 MB
Python Basics Day - 8 (Lambda function) - Copy.ipynb
  • 7.42 KB
Python Basics Day - 9 (Escape sequence).ipynb
  • 60 KB
Session-15.mp4
  • 55 mins
  • 103 MB
Hackathon-1(Questions).ipynb
  • 6.29 KB
Session-16.mp4
    ( INTRO TO OOPs).ipynb
    • 676 KB
    Hackathon-1(Answers).ipynb
    • 16.8 KB
    Session-17.1[Lecture-1 OOPs].mp4
    • 37 mins
    • 86.7 MB
    Session-17.2[OOPS Part-1]
    • 50 mins
    • 143 MB
    python Advance Day(11,12) Class & objects.ipynb
    • 595 KB
    Session-18[OOPS-Part2}
    • 57 mins
    • 120 MB
    Session-19[OOPS-Part3]
    • (1h 07m 06s)
    • 188 MB
    Session-20[OOPS-Part4]
    • 51 mins
    • 116 MB
    (Encapsulation, Abstraction,Inheritance,Polymorphism).ipynb
    • 27.4 KB
    Overloading,Overriding.ipynb
    • 17.4 KB
    Session-21.mp4
    • 54 mins
    • 127 MB
    Session-22.mp4
    • (1h 05m 22s)
    • 135 MB
    Basics of Pandas_(part-1).ipynb
    • 165 KB
    Pandas_Complete.ipynb
    • 135 KB
    Company_Data.csv
    • 14.3 KB
    Pandas_Cheat_Sheet.pdf
    • 338 KB
    Session-23.mp4
    • (1h 34m 29s)
    • 223 MB
    Sesssion-24.mp4
    • 58 mins
    • 183 MB
    Numpy Commands.ipynb
    • 146 KB
    numpy_live(aug) .ipynb
    • 649 KB
    python-NUMPY-00.ipynb
    • 79.9 KB
    Session-25.mp4
    • (1h 02m 59s)
    • 159 MB
    Hackathon-2(Questions).ipynb
    • 7.06 KB
    Session-26.mp4
    • 46 mins
    • 167 MB
    Hackathon-2(Answers).ipynb
    • 48.7 KB
    Session-27.mp4
    • 52 mins
    • 135 MB
    Data visualization table.png
    • 339 KB
    Matplotlib Material.pdf
    • 1.83 MB
    matplotlib_aug.ipynb
    • 123 KB
    Data visualization with Matplotlib.ipynb
    • 295 KB
    Session-28.mp4
    • (1h 00m 19s)
    • 199 MB
    Session-29.mp4
    • 54 mins
    • 179 MB
    clevelanda.csv
    • 11.1 KB
    seaborn_live_aug.ipynb
    • 301 KB
    Session-30.mp4
    • 60 mins
    • 149 MB
    plotly.ipynb
    • 4.41 MB
    Session-31.mp4
    • 48 mins
    • 197 MB
    Session-32.mp4
    • 42 mins
    • 157 MB
    Why_data_cleaning.ipynb
    • 30.9 KB
    Session-33.mp4
    • 48 mins
    • 96.9 MB
    Session-34.mp4
    • 50 mins
    • 153 MB
    Data Cleaning using Sklearn & Pandas.ipynb
    • 60.7 KB
    Session-35.mp4
    • 55 mins
    • 132 MB
    Lending Data.xlsx
    • 2.04 MB
    Session-36.mp4
    • 37 mins
    • 96 MB
    Data Cleaning_Complete.ipynb
    • 70.6 KB
    project-1(lending).ipynb
    • 46.2 KB
    Automobile price data _Raw_.csv
    • 25.8 KB
    Session-37.mp4
    • 41 mins
    • 140 MB

    3. Statistics with Real-Time Project Demonstration on EDA

    Basics of Statistics, What is Data? What are Features
    • (1h 08m 30s)
    • 118 MB
    Real-Time Project Demonstration on EDA with Real-World Project
    • (1h 01m 55s)
    • 180 MB
    regulalr_expressions_basics.ipynb
    • 19.9 KB
    Customer Analytics Roadmap_BEPEC.pdf
    • 583 KB
    Real-Time Project on EDA.ipynb
    • 934 KB
    1. Why Statistics and When.mp4
    • 8 mins
    • 418 MB
    2. Descriptive Statistics.mp4
    • 7 mins
    • 331 MB
    3. Descriptive Stats using Python.mp4
    • 19 mins
    • 932 MB
    PD Part-1.mp4
    • 6 mins
    • 328 MB
    PD - 2.mp4
    • 11 mins
    • 659 MB
    Continuous Probability Distribution.mp4
    • 5 mins
    • 197 MB
    Confidence Interval.mp4
    • 7 mins
    • 437 MB
    Law of Large Numbers and CLT.mp4
    • 4 mins
    • 143 MB
    Hypothesis Testing Part-1.mp4
    • 32 mins
    • 1.52 GB
    Hypothesis Part-2.mp4
    • 3 mins
    • 173 MB

    4. Project-1: Data Analytics Project to Improve Employee Efficiency

    Project Details, What to do? What to Submit?
    • 41 mins
    • 86.6 MB
    summary of project.docx
    • 11 KB
    INX_Future_Inc_Employee_Performance_CDS_Project2_Data_V1.8.xls
    • 401 KB

    5. Probability Distributions, Hypothesis Testing with Real-Time Project Demonstration

    Probability Distribution.mp4
      Normal Distribution.mp4
      • 23 mins
      • 266 MB
      Hypothesis Testing.mp4
      • 14 mins
      • 219 MB
      Real-Time Project Demonstration on Probability Distribution & Hypothesis Testing
      • (1h 34m 19s)
      • 282 MB
      Hypothesis Part-2.mp4
      • 6 mins
      • 99.7 MB
      How to calculate p-value?Basics of Hypothesis Coding
      • (1h 15m 22s)
      • 221 MB
      Probability Distribution, Hypothesis.pdf
      • 1.28 MB
      Hypothesis.pptx
      • 2.44 MB
      Statistical Testing.pptx
      • 1.17 MB
      Real-Time Project on Probability Distribution & Hypothesis Testing.ipynb
      • 431 KB
      Hypothesis Testing.ipynb
      • 7.64 KB
      How to solve PDF-CDF-PMF.mp4
      • 11 mins
      • 214 MB
      Chebyshevs, Log, Power Law, Q-Q, CLT.mp4
      • 23 mins
      • 281 MB

      6. Tableau

      Dashboard Explanations,Do_s_Dont_s of a Dashboard -Connecting With Cloud Era.mp4
      • 49 mins
      • 73.2 MB
      Day 1 Introduction to Tableau.mp4
      • 27 mins
      • 32.6 MB
      Day 2 Principles Of Data Visualization.mp4
      • 54 mins
      • 70.3 MB
      Day 3 Data Interpretation,Pivot,Split Tables.mp4
      • (1h 16m 44s)
      • 108 MB
      Day 4 Time Series,Dual Axis Charts,Usage of Markscard.mp4
      • 33 mins
      • 40 MB
      Day 5 Bar Plot,Stacked Bar Plot,Color Encoded,Nested Bar Plots,Customized Sql,Traditional Context.mp4
      • 50 mins
      • 59 MB
      Day 6 Bullet Graphs,Correlation Analysis,Scatter Diagram,Prediction Model.mp4
      • (1h 14m 19s)
      • 88.3 MB
      Day 7 Part -1 Histogram,Varies Bin Sizes.mp4
      • 39 mins
      • 46.5 MB
      Day 8 Part -2 What is box plot..etc.mp4
      • 17 mins
      • 21.3 MB
      Day 9 Text Tables,Tree Maps,Circle Charts,Word Cloud,Bubble Chart,Field Map,Symbol Map,Action in Maps.mp4
      • (1h 15m 16s)
      • 94.3 MB
      Day 10 Cal - Part -1 Pareto Charts,Quick Table Calculations,Table Calculations,Lookup Z-N Functions.mp4
      • 55 mins
      • 65.5 MB
      Day 11 Cal - Part -2 Else If Functions,Left,Right Manipulation,Parameters,Sets.mp4
      • 50 mins
      • 60.9 MB
      Day 12 LOD Expressions.mp4
      • 43 mins
      • 48.7 MB
      Project Work: E-Commerce.docx
      • 607 KB
      Project Work: Products.xlsx
      • 1.08 MB
      Tableau Interview Questions.pdf
      • 797 KB

      7. Power BI

      1-introduction-to-power-bi.mp4
      • 5 mins
      • 360 MB
      2-creating-a-bar-plot.mp4
      • 10 mins
      • 309 MB
      3-creating-pie-chart.mp4
      • 4 mins
      • 339 MB
      4-creating-ribbon-chart.mp4
      • 3 mins
      • 338 MB
      5-creating-scatter-plot.mp4
      • 5 mins
      • 348 MB
      6-creating-waterfall-charts.mp4
      • 5 mins
      • 365 MB
      7-creating-funnel-chart.mp4
      • 2 mins
      • 334 MB
      8-creating-line-plot-area-plot.mp4
      • 6 mins
      • 359 MB
      9-creating-matrix-conditional-formatting.mp4
      • 7 mins
      • 344 MB
      10-creating-decomposition-tree.mp4
      • 3 mins
      • 344 MB
      11-creating-kpi-card.mp4
      • 4 mins
      • 321 MB
      12-creating-gauge-card.mp4
      • 5 mins
      • 334 MB
      13-creating-slicers.mp4
      • 4 mins
      • 348 MB
      14-creating-animated-bar-plot.mp4
      • 3 mins
      • 308 MB
      15-creating-sunburst-chart.mp4
      • 3 mins
      • 302 MB
      16-different-filers-in-power-bi.mp4
      • 5 mins
      • 331 MB
      17-include-exclude-operations-in-power-bi.mp4
      • 3 mins
      • 331 MB
      18-introduction-to-power-query.mp4
      • 7 mins
      • 325 MB
      19-groupby-replace-in-power-query.mp4
      • 2 mins
      • 315 MB
      20-merge-and-append-operations.mp4
      • 3 mins
      • 325 MB
      21-prefix-suffix-length.mp4
      • 3 mins
      • 341 MB
      22-pivot-in-power-query.mp4
      • 4 mins
      • 333 MB
      23-introduction-to-dax-expressions.mp4
      • 4 mins
      • 341 MB
      24-creating-dax-measures.mp4
      • 4 mins
      • 344 MB
      25-creating-dax-columns.mp4
      • 4 mins
      • 344 MB
      26-more-dax-expressions.mp4
      • 3 mins
      • 317 MB
      27-publishing-power-bi-visualizations.mp4
      • 7 mins
      • 304 MB
      [Real-Time]End-to-End Data Analytics Project Pipeline
      • 24 mins
      • 341 MB
      power-query-data-cleaning.mp4
      • 34 mins
      • 340 MB

      8. MySQL

      1. Introduction to MySQL.mp4
      • 24 mins
      • 71.5 MB
      2. Creating DataBases in MySQL.mp4
      • 20 mins
      • 66.6 MB
      3. Deep Dive into MySQL Interface.mp4
      • 16 mins
      • 46.4 MB
      4. Queries in MySQL.mp4
      • 30 mins
      • 105 MB
      5.1. Clauses in MYSQL.mp4
      • 19 mins
      • 64.7 MB
      5.2. Clauses in MySQL -2.mp4
      • 24 mins
      • 83.7 MB
      5.3. Clauses in MYSQL-3.mp4
      • 32 mins
      • 50 MB
      6.1. MYSQL Data Handling -1.mp4
      • 42 mins
      • 61.6 MB
      6.2. MYSQL Data Handling -2.mp4
      • 42 mins
      • 61.6 MB
      7. MYSQL Aggregate Functions.mp4
      • 17 mins
      • 23.7 MB
      MYSQL Scripts File.zip
      • 4.54 MB

      MySQL End-to-End

      Data Analysis with MySQL
      • (1h 29m 40s)
      • 176 MB
      Different Join with MySQL
      • (1h 22m 47s)
      • 306 MB
      Create a Database.sql
      • 2 KB
      Functions.sql
      • 1.24 KB
      Views.sql
      • 2.24 KB
      SQL Joins.sql
      • 1.81 KB

      Advanced MySQL

      1. Data Integrity & Referential Integrity.mp4
      • 5 mins
      • 257 MB
      2. Data Normalization.mp4
      • 4 mins
      • 122 MB
      3-first-second-normal-form.mp4
      • 8 mins
      • 293 MB
      4-functional-dependency-transitive-dependency-3rd-normal-form.mp4
      • 9 mins
      • 398 MB
      5-boyce-codd-normal-form.mp4
      • 10 mins
      • 405 MB
      6-denormalization.mp4
      • 4 mins
      • 184 MB
      7-temporary-table-cte-r-cte.mp4
      • 11 mins
      • 417 MB
      8-when-to-use-tt-cte-r-cte.mp4
      • 3 mins
      • 81.9 MB
      9-subquery-in-mysql.mp4
      • 3 mins
      • 118 MB
      10-views-in-mysql.mp4
      • 3 mins
      • 138 MB
      11. Stored Functions.mp4
      • 13 mins
      • 395 MB
      12-stored-procedures.mp4
      • 3 mins
      • 133 MB
      13-triggers-in-mysql.mp4
      • 5 mins
      • 223 MB
      14-create-events.mp4
      • 5 mins
      • 204 MB
      15-different-functions.mp4
      • 6 mins
      • 190 MB
      16-different-ddl-commands-indexes.mp4
      • 8 mins
      • 356 MB

      9. Unsupervised Learning with Real-Time Demonstration

      Fundamentals of ML Part-1.mp4
      • 33 mins
      • 66.2 MB
      Fundamentals of Machine Learning Part-2.mp4
      • 49 mins
      • 80.5 MB
      Insight's from Train & Test Accuracy.mp4
      • 22 mins
      • 30.6 MB
      ML Blueprint.ipynb
      • 10.3 KB
      Associate Rules.mp4
      • 24 mins
      • 267 MB
      Associate-rules-2.mp4
      • 3 mins
      • 28.5 MB
      K-Means & KNN.mp4
      • 13 mins
      • 217 MB
      Problem Identification & Approach Designing for Data Science Projects
      • (1h 02m 12s)
      • 120 MB
      Associate Rules Scripting
      • 58 mins
      • 185 MB
      Data Analytics Project Approach.mp4
      • (1h 07m 05s)
      • 145 MB
      Recommendation Engine.pdf
      • 711 KB
      K-Means Clustering.ipynb
      • 16.9 KB
      Associate Rules Script.ipynb
      • 51.1 KB
      Why to do Dimensionality Reduction? Project Implementation on PCA, T-SNE
      • (1h 03m 36s)
      • 235 MB
      PCA Calculation Step by Step.ipynb
      • 24.4 KB
      T-SNE Math & Coding.ipynb
      • 16.6 KB

      10. Supervised Learning: Linear Regression Real-Time

      Covariance.mp4
      • 11 mins
      • 149 MB
      Linear Regression Material - Applied ML(BEPEC).pdf
      • 1.62 MB
      Flowchart of Supervised Learning with Different ML Methodologies
      • (1h 07m 46s)
      • 230 MB
      Simple Linear Regression & Multiple Scirpt.py
      • 3.46 KB
      Different ML Equations like Loss Functions, Optimisers, Activation, Model Architecture
      • (1h 16m 38s)
      • 230 MB
      Linear Part-1.mp4
      • 10 mins
      • 269 MB
      Linear Part2.mp4
      • 15 mins
      • 248 MB
      Linear Part 3.mp4
      • 7 mins
      • 117 MB
      Linear Regression Evaluation.mp4
      • 11 mins
      • 192 MB
      Gradient Descent.mp4
      • 20 mins
      • 229 MB
      Linear Regression Script End-to-End.mp4
      • (1h 24m 53s)
      • 241 MB
      R-Squared.mp4
      • 8 mins
      • 207 MB
      Lasso & Ridge Regression
      • 8 mins
      • 258 MB
      Linear Regression Model Accuracy Improving Techniques & Evaluation Metrics
      • (1h 04m 32s)
      • 139 MB
      concrete.csv
      • 40.4 KB
      Automobile price data _Raw_.csv
      • 25.8 KB
      Regression using Tensorflow 2x0.ipynb
      • 283 KB
      Tensorflow 2x0 Classification Model.ipynb
      • 6.76 KB

      11. Advanced ML & Deep Learning with Real-Time Project

      Logistic Regression Math.mp4
      • 13 mins
      • 147 MB
      Confusion Matrix.mp4
      • 17 mins
      • 195 MB
      ROC & AUC Curve.mp4
      • 12 mins
      • 130 MB
      Classification Script End-to-End.mp4
      • (1h 05m 16s)
      • 160 MB
      ML/DL Algorithm Flow Chart, Which makes your learning so easy(Ice Breaker).mp4
      • 13 mins
      • 115 MB
      Logistic Explanation.py
      • 1.93 KB
      Logistic Regression_Script,KNN, PCA.py
      • 884 Bytes
      Logistic Regression Material - Applied ML(BEPEC) copy.pdf
      • 1.24 MB
      KNN.csv
      • 122 KB
      .png
      • 487 KB
      Regression Algorithms Blueprint.ipynb
      • 21.3 KB
      Difference b/w Regression vs Classification.mp4
      • 21 mins
      • 276 MB
      ML Blueprint Detailed.ipynb
      • 2.06 KB
      Decision Tree Maths Deep Dive.mp4
      • 55 mins
      • 95.9 MB
      Decision Tree Part-1.mp4
      • 21 mins
      • 238 MB
      Random Forest Maths.mp4
      • 44 mins
      • 66.2 MB
      Random Forest.mp4
      • 15 mins
      • 173 MB
      Gradient Boosting.mp4
      • 14 mins
      • 156 MB
      Introduction to Gradient Boosting.mp4
      • 21 mins
      • 25.3 MB
      KNN Math.mp4
      • 33 mins
      • 39.8 MB
      SVM.mp4
      • 48 mins
      • 64.5 MB
      SVM,Logistic, KNN & DT.ipynb
      • 38.2 KB
      Decision Tree_ROC.ipynb
      • 21.2 KB
      ey_streamlit.py
      • 902 Bytes
      Decision-Tree Learning . (1).pptx
      • 2.01 MB
      Hyper Parameter Tuning.pdf
      • 2.72 MB
      All Regressors.py
      • 5.87 KB
      Boosting & Bagging.py
      • 1.4 KB
      Decision Tree_ROC.ipynb
      • 21.2 KB
      K-NN Material.pptx
      • 1010 KB
      Support Vector Machines.pptx
      • 228 KB
      SVM,Logistic, KNN & DT (1).ipynb
      • 37.3 KB
      letterdata.csv
      • 715 KB
      Data Science Process-V1.docx
      • 295 KB
      120 finalized Questions of machine learning.docx
      • 148 KB

      12. Deep Learning Foundation(Neural Networks)

      1-introduction-to-neural-networks.mp4
      • 37 mins
      • 70 MB
      2-neural-network-architecture.mp4
      • 55 mins
      • 142 MB
      3. Different Optimizers.mp4
      • 56 mins
      • 181 MB
      4-different-activation-functions.mp4
      • 36 mins
      • 61.8 MB
      5. Different Loss Functions.mp4
      • 29 mins
      • 73.8 MB
      Loss Functions.pdf
      • 804 KB
      Gradient Descent .pdf
      • 5.8 MB
      Activation Functions.pdf
      • 785 KB
      Deep Learning Introduction .pdf
      • 1.02 MB

      13. R Programming

      Introduction to Data Science.mp4
      • 24 mins
      • 30.3 MB
      Installation of R _ R Studio.mp4
      • 20 mins
      • 36.3 MB
      Basics of R Studio.mp4
      • 40 mins
      • 74 MB
      Data Visualization with R Studio.mp4
      • 34 mins
      • 71.1 MB
      Data Modelling with R Studio.mp4
      • 14 mins
      • 28.2 MB
      Data Joining with R Studio.mp4
      • 10 mins
      • 23 MB
      Data Visualization with ggplot2.mp4
      • 12 mins
      • 43.6 MB
      Data Preparation with RStudio.mp4
      • 10 mins
      • 14.5 MB
      Data Cleaning with RegExp.mp4
      • 35 mins
      • 89.5 MB
      Company Casestudy_ Data Visualization.mp4
      • 54 mins
      • 161 MB
      Data Visualization with Plotly.mp4
      • 45 mins
      • 244 MB
      Data Cleaning with Regexp.R
      • 2.19 KB
      Data Joining with DPLYR.R
      • 550 Bytes
      DecisionTreeRegressor.R
      • 322 Bytes
      Decision Tree_Item classification.R
      • 1.36 KB
      Diabetes Case Study_Logistic Regression.R
      • 1.92 KB
      Hiring challenges_Random Forest Script.R
      • 2.08 KB
      Linear Regression_Sales Prediction.R
      • 2.51 KB
      KNN_CaseStudy on Credit.R
      • 1.78 KB
      Encoder_s Script.R
      • 585 Bytes
      Linear Regression Simple _ Multiple.R
      • 1.24 KB
      KNN - Train Accuracy _ Test Accuracy.R
      • 1.12 KB
      KNN_Normalization Script.R
      • 1.03 KB
      Logistic Regression Script_Claims.R
      • 1.25 KB
      Modeling with Dplyr.R
      • 1.68 KB
      Decision Tree_Credit with Factor Conversion.R
      • 467 Bytes
      Basics of R Studio.R
      • 2.41 KB
      Cancer Casestudy_Logistic Regression.R
      • 1.48 KB
      Data Visualization with R Studio.R
      • 2.88 KB
      Linear Regression_Construction.R
      • 2.59 KB
      Data Visualization with ggplot2.R
      • 1.51 KB
      Data Preparation with R.R
      • 521 Bytes
      Data Manipulation 2-1.R
      • 1.61 KB

      15. POC on ML from Problem Identification to Deployment Demonstration

      Deploying Webapps using Streamlit(Easy way for Non-Technical Learners).mp4
      • 7 mins
      • 154 MB
      End-to-End ML POC
        Gymapp.rar
        • 292 KB
        dataGYM.xlsx
        • 50.1 KB

        14. Advanced Time Series with Real-Time Project Demonstration

        1.0. Why Time Series.mp4
        • 13 mins
        • 224 MB
        1.1. Introduction to Time Series Modelling.mp4
        • 21 mins
        • 26.6 MB
        1.2. Time Series Modelling - 2.mp4
        • 51 mins
        • 101 MB
        2.1. Characteristics of Time Series Modelling.mp4
        • 48 mins
        • 70.8 MB
        3.1. Time Series Modelling Techniques.mp4
        • 48 mins
        • 95.8 MB
        5. Coding Time Series Modelling using R.mp4
        • 51 mins
        • 149 MB
        Simple Time Series Project End-to-End.mp4
        • (1h 13m 03s)
        • 236 MB
        Simple Time Series Forecasting .ipynb
        • 583 KB
        LSTM Example on Cashflow. ipynb
        • 13.4 KB
        End-to-end project on Time Series Modelling.mp4
        • (1h 06m 10s)
        • 171 MB
        POCM_HISTORICAL.xlsx
        • 19.8 MB
        Forecasting Script End-to-End.ipynb
        • 423 KB
        5. Time Series Modelling using RNN.mp4
        • 28 mins
        • 52.8 MB
        Script.zip
        • 1.28 KB
        Time-Series Modelling.pdf
        • 4.47 MB

        15.1 Pycaret, Docker, MLFlow, Gradio, FastAPI, SHAP

        1. Setup the Environment.mp4
        • 5 mins
        • 280 MB
        2. Docker Installation.mp4
        • 4 mins
        • 220 MB
        3. How to activate virtual environment.mp4
        • 4 mins
        • 177 MB
        4. Docker Desktop.mp4
        • 3 mins
        • 166 MB
        5. Regression Models Using Pycaret.mp4
        • 14 mins
        • 365 MB
        6. Regression Models Using Pycaret Part II.mp4
        • 3 mins
        • 125 MB
        7. Interpretability Of Models Using SHAP_1.mp4
        • 5 mins
        • 277 MB
        8. What is SHAP.mp4
        • 2 mins
        • 73.2 MB
        9. Application Development With Gradio.mp4
        • 4 mins
        • 375 MB
        10. Creating API With Pycaret & FastAPI.mp4
        • 4 mins
        • 372 MB
        11. Creating a Docker Image.mp4
        • 6 mins
        • 374 MB
        12. Model Versioning Pycaret & MLflow.mp4
        • 7 mins
        • 373 MB
        Notebook_API creation with Pycaret and FastAPI (1).ipynb
        • 34.9 KB
        Regression Models with Pycaret (1).ipynb
        • 1.49 MB
        Model registration and versioning with MLFlow (1).ipynb
        • 5.84 KB
        MLOps with Pycaret and MLflow (1).ipynb
        • 191 KB
        Create container for an API (1).ipynb
        • 291 KB
        Interpretability of models with SHAP (1).ipynb
        • 496 KB
        1. Application development with Gradio (1).ipynb
        • 57 KB

        15.2. Statistics, ML, NLP Theory Interview Questions

        ML FlowChart.pdf
        • 19.9 KB
        Data Science Interview Revision.pdf
        • 1.2 MB
        ML Project Framework.pdf
        • 29.2 KB
        Machine Learning Math Deep Dive.pdf
        • 311 KB
        What is ML.mp4
        • 36 mins
        • 465 MB
        1. How to Prepare Machine Learning Math for Interviews.mp4
        • 7 mins
        • 374 MB
        2. Why lr.mp4
        • 7 mins
        • 375 MB
        3. What is Linear Regression.mp4
        • 5 mins
        • 385 MB
        4. CaseStudy on Linear Regression.mp4
        • 7 mins
        • 382 MB
        5. Math behind Linear Regression - 1.mp4
        • 8 mins
        • 374 MB
        6. Math behind Linear Regression - 2.mp4
        • 7 mins
        • 381 MB
        7. Math behind OLS Linear Regression.mp4
        • 5 mins
        • 380 MB
        8. Assumptions of Linear Regression.mp4
        • 24 mins
        • 384 MB
        9. Evaluation Metrics of Regression Models.mp4
        • 16 mins
        • 376 MB
        10. Accuracy Improving Techniques.mp4
        • 15 mins
        • 386 MB
        11. Regularization Techniques.mp4
        • 9 mins
        • 375 MB
        End-to-End Linear Regression Coding Part-1
        • 23 mins
        • 363 MB
        End-to-End Linear Regression Part-2
        • 15 mins
        • 359 MB
        1. Why Logistic Regression.mp4
        • 5 mins
        • 378 MB
        2. Math behind Logistic Regression.mp4
        • 18 mins
        • 383 MB
        3 Evaluation Metrics behind Classification Algorithms.mp4
        • 18 mins
        • 380 MB
        4. ROC & AUC Curve.mp4
        • 11 mins
        • 384 MB
        End-to-End Logistic Regression Coding.mp4
        • 8 mins
        • 353 MB
        Cross Validation Coding.mp4
        • 3 mins
        • 330 MB
        ROC & AUC Curve Coding.mp4
        • 5 mins
        • 334 MB
        5. Introduction to Decision Tree.mp4
        • 7 mins
        • 378 MB
        6. Intuition Behind Decision Tree.mp4
        • 8 mins
        • 380 MB
        7. Math Behind Decision Tree.mp4
        • 17 mins
        • 386 MB
        8. Math behind Decision Tree using GINI.mp4
        • 5 mins
        • 364 MB
        9. Drawbacks of Decision Tree.mp4
        • 6 mins
        • 377 MB
        10. Random Forest & Gradient Boosting.mp4
        • 14 mins
        • 374 MB
        Plotting Decision Tree Coding.mp4
        • 4 mins
        • 338 MB
        Hyperparameter Tuning Techniques Coding.mp4
        • 9 mins
        • 367 MB
        11. Handling Imbalanced Dataset.mp4
        • 7 mins
        • 383 MB
        12. Feature Selection Techniques.mp4
        • 9 mins
        • 385 MB
        Feature Selection Techniques Coding.mp4
        • 9 mins
        • 338 MB
        Wrapper Method Coding.mp4
        • 7 mins
        • 343 MB
        OverSampling & UnderSampling Coding.mp4
        • 7 mins
        • 357 MB
        What Matters in Interviews.mp4
        • 3 mins
        • 344 MB
        1. Introduction to PCA.mp4
        • 5 mins
        • 315 MB
        2. Math Behind PCA.mp4
        • 16 mins
        • 300 MB
        2a. Coding PCA.mp4
        • 4 mins
        • 322 MB
        3. Math behind KNN.mp4
        • 12 mins
        • 315 MB
        4. Math behind SVM Part-1.mp4
        • 5 mins
        • 289 MB
        5. Math behind SVM Part-2.mp4
        • 8 mins
        • 324 MB
        6.Plotting SVM.mp4
        • 4 mins
        • 308 MB
        7. Classification using AutoML.mp4
        • 8 mins
        • 310 MB
        1_Introduction to NLP & NLP Applications.mp4
        • 10 mins
        • 156 MB
        2_NLP Challenges.mp4
        • 12 mins
        • 223 MB
        3_NLP Pre-Processing Steps.mp4
        • 15 mins
        • 264 MB
        4_NLP Feature Extraction.mp4
        • 11 mins
        • 191 MB
        5_NLP Practical Example.mp4
        • 16 mins
        • 318 MB
        End-to-End ML Deployment using AWS.mp4
        • (1h 04m 52s)
        • 344 MB

        16. AWS Sagemaker Studio with MLOps Tutorial with Investment Domain Real-Time Project Demonstration

        Setting up AWS Sagemaker & Deploying Simple Model on Sagemaker.mp4
        • 16 mins
        • 196 MB
        AIOps with AWS Sagemaker Studio.mp4
        • (1h 02m 43s)
        • 133 MB
        ML End-to-End Project on Sagemaker Studio.ipynb
        • 1.22 MB
        Deployment using Sagemaker
        • 50.9 KB
        fashion.py
        • 4.19 KB

        17. CMLA [Certified Machine Learning Architect]

        Course - Agenda.xlsx
        • 9.71 KB
        Interview Questions from Data Scientist Corporate Lifecycle
        • (3h 05m 23s)
        • 252 MB
        Building Project Charter, Network Diagrams for Data Science Project
        • (2h 34m 21s)
        • 387 MB
        Model Evaluation & Model Deployment on Web, Mobile on Premise.mp4.mp4
        • (2h 41m 21s)
        • 298 MB
        Interview Questions from Model Deployment using Heroku, Mobile Deployment and Project Closing .mp4
        • (1h 36m 21s)
        • 163 MB
        CMLA - BP.pdf
        • 6.1 MB
        Password For Material
          app.py
          • 251 Bytes
          model.py
          • 1 KB
          multiclass_svm_tensorflow.py
          • 5.41 KB
          request.py
          • 214 Bytes
          Pre-Processing_Sklearn.py
          • 1.31 KB
          R Shiny APP.R
          • 108 Bytes
          Serialisation and deserialisation.py
          • 299 Bytes
          server.py
          • 766 Bytes
          time_series_data_preprocessing.ipynb
          • 272 KB
          Video_to_frames.ipynb
          • 43.6 KB
          1.01_PCoE_Project_Charter_Guide.doc
          • 59 KB
          2.05_PCoE_Assumption_and_Constraint_Log_Guide.doc
          • 270 KB
          2.06_PCoE_WBS_Diagram.doc
          • 260 KB
          Decision Tree_Pickle Model Saving.py
          • 1.14 KB
          Decision Tree_CV.py
          • 1.08 KB
          gitignore.txt
          • 123 Bytes
          model_GYM.pkl
          • 1.22 KB
          app-2.py
          • 1.43 KB
          requirements.txt
          • 594 Bytes
          Procfile.dms
          • 21 Bytes

          18. NLP, NLU with Real time Project

          1. Introduction to Text Mining.mp4
          • 52 mins
          • 159 MB
          2. NLP Processing Deep Dive.mp4
          • 28 mins
          • 101 MB
          3. NLP Classification Coding.mp4
          • 31 mins
          • 89.8 MB
          4. Language Identification using R.mp4
          • 41 mins
          • 99.7 MB
          5. Phrase Extraction .mp4
          • 54 mins
          • 166 MB
          NLP Part-1.mp4
          • (1h 40m 45s)
          • 238 MB
          NLU using RNN(NLP Part-2).ipynb
          • 45.8 KB
          NLU, Word2Vec.mp4
          • (2h 39m 52s)
          • 297 MB
          Customer_NER_Model.ipynb
          • 23.8 KB
          Word2Vec - Spam & Non-Spam.ipynb
          • 309 KB
          Text_Similarity.ipynb
          • 12.7 KB
          Script.zip
          • 435 KB
          Spam & Non_spam Classification-2.ipynb
          • 18.1 KB
          NB.csv
          • 464 KB
          Topic Modelling.ipynb
          • 4.82 MB
          NLP Complete - BEPEC.pdf
          • 1.42 MB
          BEPEC - DT_Project CRM.pdf
          • 86.7 KB
          Dataset.xlsx
          • 155 KB

          19. SoftSkills for Interviews

          1. Why Interview skills are important.mp4
          • 7 mins
          • 973 MB
          2. How to choose right course?.mp4
          • 12 mins
          • 266 MB
          3. Do's & Don't while marketing your profile.mp4
          • 13 mins
          • 296 MB
          4. Constructing Great Resume.mp4
          • 11 mins
          • 226 MB
          5. Do's & Don'ts in Linkedin/Naukri/Job Portals.mp4
          • 10 mins
          • 222 MB
          6. 3 Elements to crack any interviews.mp4
          • 12 mins
          • 275 MB

          20. Interview Preparation for Data Science

          1. What is Data, type of data & Importance of data.mp4
          • 20 mins
          • 207 MB
          2. What are math equations? Different math equations.mp4
          • 12 mins
          • 277 MB
          3. Fourier Series & Fourier Transformation.mp4
          • 11 mins
          • 236 MB
          4. History of Probability & Types.mp4
          • 16 mins
          • 214 MB
          5. Why Linear Algebra for ML?(Easy way).mp4
          • 20 mins
          • 718 MB
          6. Importance of Calculus.mp4
          • 8 mins
          • 205 MB
          How to solve PDF,CDF, PMF.mp4
          • 11 mins
          • 214 MB
          Chebyshev's Inequality, Log Transformations, Power Law Distribution, Central Limit theorem.mp4
          • 23 mins
          • 281 MB
          How to choose right ML Algorithm?
          • 14 mins
          • 247 MB
          ML Project Flow
          • 10 mins
          • 44 MB
          What is Reinforcement Learning? And Why?.mp4
          • 14 mins
          • 982 MB
          How to grow model accuracy?.mp4
          • 57 mins
          • 73.4 MB
          Tips & Tricks to prepare ML Algorithms.mp4
          • 10 mins
          • 217 MB
          What is Feature Engineering.mp4
          • 5 mins
          • 175 MB
          ML Tips.m4a
          • 2 mins
          • 981 KB
          What is AIOps?.mp4
          • 12 mins
          • 243 MB
          Setting up AWS Sagemaker & Deploying Simple Model on Sagemaker.mp4
          • 16 mins
          • 196 MB
          deploy_to_sagemaker.ipynb
          • 7.65 KB
          mnist.py
          • 3.95 KB
          How to speak in Data Science Interview and Interview Process..m4a
          • 8 mins
          • 1.27 MB
          How to speak in Data Science Interview.m4a
          • 15 mins
          • 5.93 MB
          How to build Linkedin Portfolio & Real-Time Project Experience.mp4
          • (1h 10m 26s)
          • 169 MB
          Resume Preparation & Data Science Interview Preparation.mp4
          • 53 mins
          • 116 MB
          How to start with data scientist the math company data scientist.mp3
          • 9 mins
          • 6.02 MB