Start Exam
Login / Register

Basics of Artificial Intelligence – Machine Learning – Data Science A – Z

0 (0 Ratings)
Wishlist

Course Curriculum

1 – .tde files

2 – AB Testing Techniques

3 – Ad – Hoc

4 – Advanced Machine Learning

5 – Advanced Charts

6 – Advanced Data Analysis

7 – Advanced Data Structures

8 – Advanced Hashing Techniques

9 – Aggregate Functions

10 – Aggregations

11 – AI Bias

12 – AI Fairness

13 – AI Future Trends

14 – AI Generative Tools

15 – Alphanumeric

16 – Anaconda

17 – Analysis Types

18 – Apache Spark

19 – Apex

20 – Application Programming Interface – APIs

21 – Application Of Hash Tables

22 – Application Of Queues

23 – Application Of Stacks

24 – Applied Analytics

25 – Approximation Methods

26 – Area Charts

27 – ARIMA Explanatory Variables

28 – ARIMA Model Calculations

29 – ARIMA Models

30 – Array Overview

31 – Arrays

32 – Assess Data Quality

33 – Auto Complete

34 – Automating Tasks

35 – Automation Bot

36 – Average Probability

37 – Average Lines

38 – AVL Lines

39 – B – Trees

40 – B+ Trees

41 – Background Image

42 – Back-Propagation

43 – Backtracking

44 – Backtracking Algorithm

45 – Bar Chart

46 – Bard

47 – Basic Charts

48 – Bayes Theorem

49 – Bellman – Ford Algorithm

50 – BI Reports

51 – Bias – Fairness

52 – Big Data

53 – Binary Search Tree

54 – Binary Tree Traversals

55 – Binary Trees

56 – Bing

57 – Binomial

58 – Bins

59 – Blend Data

60 – Bottom N

61 – Box Plots

62 – Breadth First Search – BFS

63 – Bubble Chart

64 – Business Case Studies

65 – Cardinality

66 – CartPole

67 – Central Tendency

68 – Chi-Squared

69 – Chloropeth Maps

70 – Classification Models

71 – Cleansing Datasets

72 – Cloud Based

73 – Cloud Deployments

74 – Clustering

75 – Code Automation

76 – Cohort Orientation

77 – Column Level Shading

78 – Column Level Bands

79 – Columns

80 – Combining Sheets

81 – Combo Chart

82 – Comma Delimited

83 – Compliance Reports

84 – Compound Growth Rate

85 – Comprehensive Data Structures

86 – Computer Vision

87 – Concatenation

88 – Conditional Formatting

89 – Conditional Loops

90 – Confidence Intervals

91 – Configuring Filter Settings

92 – Conformity

93 – Connect To Relational Databases

94 – Connect To Extracts

95 – Connect To Hyper Files

96 – Connect To Spreadsheets

97 – Connecting Dataset

98 – Consistent Hashing

99 – Containerizing ML Models

100 – Containers – Layout Options

101 – Convolutional Neural Network – CNN

102 – Creating Dashboards – Stories

103 – Creating Visuals

104 – Creating Aliases

105 – Creating Joins

106 – Cross Validation

107 – CRUD

108 – Currency

109 – Custom Geocoding

110 – Custom Table Calculations

111 – Custom Sort

112 – Custom SQL Queries

113 – Customer Experience Mapping

114 – Customer Support Bot

115 – DALL-E

116 – Dashboard Actions

117 – Dashboard Stories

118 – Data Accuracy

119 – Data Acquisition

120 – Data Analysis

121 – Data Analysis – Numpy

122 – Data Analysis – Pandas

123 – Data Analytics Tools

124 – Data Append

125 – Data Attribute

126 – Data Audits

127 – Data Blending

128 – Data Breach

129 – Data Cleansing

130 – Data Collection

131 – Data Collection Methods

132 – Data Completeness

133 – Data Consistency

134 – Data Constraints

135 – Data Content

136 – Data Deletion

137 – Data Encryption

138 – Data Exploration

139 – Data File Formats

140 – Data Filtering

141 – Data Frame Programming

142 – Data Frames

143 – Data Integrity

144 – Data Intrahops

145 – Data Lake

146 – Data Locality

147 – Data Manipulation

148 – Data Mart

149 – Data Merge

150 – Data Mining

151 – Data Mode

152 – Data Modelling

153 – Data Modelling Techniques

154 – Data Outliers

155 – Data Preparation

156 – Data Pre-Processing

157 – Data Processing

158 – Data Profiling

159 – Data Retention

160 – Data Schemes

161 – Data Source

162 – Data Structures

163 – Data Transformation

164 – Data Transmission

165 – Data Types

166 – Data Understanding

167 – Data Visualization

168 – Data Visualisation Basics

169 – Data Visualisation Tools

170 – Data Warehousing

171 – Data Extract Refresh

172 – Data Forecasting Model

173 – Data Interpreter

174 – Data Tables

175 – Databases

176 – Datorama

177 – Dataset

178 – Datatype Variation

179 – Date Functions

180 – Date Range

181 – Date Values

182 – Date Calculations

183 – DateName

184 – DateParse

185 – DBMS

186 – Debugging Techniques

187 – Decision Tree Model

188 – Decisions

189 – Decision Control

190 – Deep CNN

191 – Deep Learning

192 – Deep Learning Algorithms

193 – Deep Q-Learning Algorithms

194 – Default Field Properties

195 – Delta Load

196 – Density Maps

197 – Depth First Search – DFS

198 – Derived Variables

199 – Descriptive Statistics

200 – Detrending

201 – Dijkshetra’s Algorithm

202 – Dimension

203 – Dimension Measures

204 – Discrete Probability Distributions

205 – Discrete-Continous

206 – Dispersion

207 – Displaying

208 – Distribution

209 – Distribution List

210 – Distribution Bands

211 – Domo

212 – Double Ended Queue – Deque

213 – Drill Down

214 – Dual Axis Chart

215 – Duplicate Data

216 – Dynamic Programming

217 – Dynamic Reports

218 – EDA

219 – Enabling Interactivity

220 – Encoding the Data

221 – Error – ANOVA

222 – Essential Data Structures

223 – Establishing Relationships

224 – Ethical Challenges

225 – Ethical Considerations

226 – Ethical Implications

227 – Ethical Limitations

228 – Ethics of Generative AI

229 – Evaluation Metrics

230 – Exception Handling

231 – Execution Plan

232 – Exploratory Data Analysis

233 – Exponentially Weighted Moving Average

234 – Extract Filters In Tableau

235 – Extract, Load, Transform – ELT

236 – Feature Engineering

237 – Feedback Loops in ML Deployment

238 – Filled Maps

239 – FIXED LOD Calculations

240 – Flavors of Python

241 – Flow Controls

242 – Forms Menu

243 – Frequency

244 – Frozen Lake Equipment

245 – F1 Score

246 – Functions

247 – Function Modules

248 – Fundamentals Of Math

249 – Fundamentals Of Probability

250 – Fundamentals Of Statistics

251 – Gantt Chart

252 – Generative Adversarial Network – GAN

253 – Generative AI

254 – Generative Modelling

255 – Geographic Map

256 – Graph Neural Networks – GNN

257 – Graph Size

258 – Graphs

259 – Greedy Algorithm

260 – Gridlines

261 – GROUP BY

262 – Grouping

263 – Groups

264 – Haar Cascade

265 – Hadoop

266 – Hash Tables

267 – Hashing

268 – Hashing Techniques

269 – Having Clauses

270 – HBase

271 – Heap

272 – Hiding

273 – Hierarchial Clustering

274 – Hierarchies

275 – High Dimensional Data Analysis

276 – Highlight Table

277 – Hyperparameter Tuning

278 – Hypothesis Testing

279 – Image Classification

280 – Image Enhancement

281 – Image Filtering

282 – Image Processing

283 – Image Recognition

284 – Image Segmentation

285 – Images

286 – Imputation

287 – Indexing

288 – Inferential Statistics

289 – Infographic

290 – Information Retrieval

291- Inserting Cells

292 – Integrated Prompt Engineering

293 – Integration

294 – Intelligent News Aggregator

295 – Interview Bots

296 – Invalid Data

297 – Javascript Object Notation – JSON

298 – Join Clause

299 – JSON Data

300 – K-means Clustering

301 – K-Nearest Neighbour Model – KNN

302 – Kart Model Business

303 – Keras API

304 – Key Performance Indicators – KPI

305 – Key Value Pairs

306 – Knowledge Discovery Bot

307 – Kruskal’s Algorithm

308 – Labels

309 – LangChain Concepts

310 – LangChain Tools

311 – Language Identification

312 – Large Language Models – LLM

313 – Layout

314 – LCS Algorithm

315 – Learning Algorithms

316 – Legends

317 – Levenshtein Distance

318 – Line Chart

319 – Linear Algebra

320 – Linear Regression

321 – Link Prediction

322 – Linked Lists

323 – Lists

324 – Live Data Feed

325 – Live Connection

326 – Local Storage

327 – Log Based Differencing

328 – Logical Functions

329 – Logical Expressions

330 – Logistic Regression

331 – Looker

332 – LookML

333 – Loop Control

334 – Machine Data

335 – Machine Learning Pipelines

336 – Macros

337 – Managing Workloads

338 – Manual ARIMA Parameter Selection

339 – Map Data Geographically

340 – Map Reduce

341 – Map Reduce Use Cases

342 – Map Reduce Architecture

343 – Marketing Perfomance Analysis

344 – Master Data Management

345 – Master Slave Architecture

346 – Matplotlib

347 – Max

348 – Mean

349 – Median

350 – Medical Survey

351 – Microstrategy

352 – Min

353 – Minimum Spanning Tree

354 – Minitab

355 – Missing Values

356 – ML Deployment Lifecycle

357 – ML Deployments

358 – MLP Architecture

359 – Mode

360 – Model Deployments

361 – Model Evaluation

362 – Model Management

363 – Model Serialization

364 – Model Training

365 – Model Versioning

366 – Modelling Techniques

367 – Modules

368 – MongoDB

369 – MongoDB Shell

370 – Month-To-Date

371 – Moving Average Forecast

372 – Moving Toolbars

373 – Moving Average

374 – Multimodal Models

375 – Multiple Tables

376 – Multivariate Time Series

377 – Naive Bayes Model

378 – Natural Language Models – NLM

379 – Natural Language Processing – NLP

380 – Natural Language Toolkit – NLT

381 – Navigation

382 – Navigation Buttons

383 – Neural Network

384 – Neural Network Architectures

385 – N-GRAMS

386 – NLP Spacey

387 – Node Classification

388 – Node Conformity

389 – Non – Parametric Data

390 – Non – Relational Databases

391 – Normalization Constraint

392 – Normalize Data

393 – NoSQL

394 – Number Functions

395 – Numeric Data

396 – Numerical Programming

397 – NumPy

398 – Object Localization

399 – Observation

400 – One Time Report

401 – Online Analytical Processing – OLAP

402 – Online Platform Metrics

403 – Online Transactional Processing – OLTP

404 – Open Ended Project

405 – Operational Reports

406 – Optimizing Models

407 – Optimizing Models For Deployment

408 – Padding

409 – Pandas

410 – Parametrization

411 – Parsing

412 – Pattern Searching

413 – PCA

414 – Percent Change

415 – Percent Of Difference

416 – Percent Of Total

417 – Percentages

418 – Percentile

419 – Performance

420 – Performance Analysis

421 – Permutations – Combinations

422 – Persisting RDD

423 – Personalized News Recommendation

424 – Pie Chart

425 – Pivot Tables

426 – Point-In-Time

427 – Poission

428 – Polygon Maps

429 – Pooling

430 – Population

431 – Population – Sample

432 – Power BI

433 – PowerBI Services

434 – Practice Aggregation Queries

435 – Precision

436 – Predicting Smartphone User Behaviour

437 – Predictive Model

438 – Prim’s Algorithm

439 – Principle Component Analysis – PCA

440 – Probabilistic Graphical Models

441 – Probability

442 – Probability Distribution

443 – Problem – Solving Techniques

444 – Product Analytics

445 – Product Usage Onboarding

446 – Productionization

447 – Profiling Datasets

448 – Programming Environmental Setup

449 – Programming Functions

450 – Programming Fundamentals

451 – Programming Modules

452 – Pseudocode Flowcharts

453 – Public Databases

454 – P-Values

455 – PySpark

456 – Python

457 – Python Data Types

458 – Query Optimization

459 – QuerySet

460 – Queues

461 – Random Forest

462 – Range

463 – Rapid Mining

464 – RDD Programming

465 – Real Time

466 – Real-Time Projects

467 – Real World Applications

468 – Reasonable Expectations

469 – Recoding Data

470 – Record Link Restrictions

471 – Recurrent Neural Networks

472 – Recurring Reports

473 – Recursion

474 – Reduction

475 – Redundant Data

476 – Reference Bands

477 – Reference Lines

478 – Regression Models

479 – Regular Expression

480 – Reinforcement Learning

481 – Relational DataBase Management System – RDBMS

482 – Release Approvals

483 – Reporting Techniques

484 – Research Report

485 – Responsible AI

486 – Responsive Design

487 – RESTful API

488 – Revisiting HelpMate

489 – Reward Discounting

490 – Risk and Regulatory Reports

491 – RHLF

492 – RNN in skflow

493 – Role Based Access Control – RBAC

494 – Roll Up

495 – Row Level Shading

496 – Row Level Bands

497 – Rows

498 – Rows Failed

499 – Rows Passed

500 – R-Spark

501 – Rule Based Systems

502 – Running Total

503 – Sample

504 – SAS

505 – Scaling Strategies

506 – Scatter Plots

507 – Scheme Designing

508 – Seaborn

509 – Security Requirements

510 – Segment Tree

511 – Semantic Search

512 – Semantic Segmentation

513 – Sequence Triggers

514 – Sequential API

515 – Sets

516 – Shaping Data

517 – Shaping Data By Pivots

518 – Shared Drive

519 – Sheet Selectors

520 – Shortest Path Algorithms

521 – Similarity Checks

522 – Simple Computations

523 – Simple Constants

524 – Simple Regression

525 – Simple Variables

526 – Single Shot MultiBox Detector

527 – Skflow

528 – Sorting Data

529 – Spacy

530 – Spark

531 – Spark Analytics

532 – Spark RDD Programming

533 – Spark SQL

534 – Specialized Hardware

535 – Specific Device Layouts

536 – Specification Mismatch

537 – Split

538 – Sports Analytics

539 – Spot Check

540 – Spreadsheets

541 – SQL Fundamentals

542 – SQOOP

543 – Stacked Chart

544 – Stacks

545 – Standard Deviations

546 – Star Schema

547 – Stata

548 – Static Data

549 – Static Report

550 – Statistical Thinking

551 – Statistics

552 – Stemming

553 – Stock Prices

554 – Story Point

555 – String Manipulation

556 – String Functions

557 – Strings

558 – Strongly Connected Components

559 – Structure The Data

560 – Subqueries

561 – Subset Of Records

562 – Support Vector Machines – SVM

563 – Survey

564 – Swapping Sheets

565 – Symbol Maps

566 – Syntax

567 – Tab Delimited

568 – Tableau

569 – Tableau Prep Workflow

570 – Tableau Server

571 – Taxi Environment

572 – TensorBoard

573 – Tensorflow

574 – Text Analysis

575 – Text File

576 – Text Generation

577 – Text PreProcessing Techniques

578 – TF-IDF

579 – TFLearn APIs

580 – Time Series

581 – Time Series Analysis

582 – Time Series Classification

583 – Time Series Data

584 – Time Series Forecasting

585 – Titles

586 – Top N

587 – Toxic Comments

588 – Transformer Models

589 – Transpose

590 – Travelling Salesman Problem – TSP

591 – Tree Map

592 – Trees

593 – Trend Lines

594 – Trie

595 – T-tests

596 – Type 1 Errors

597 – Type 2 Errors

598 – Type Conversion Functions

599 – Undefined Fields

600 – Understanding AI Trends

601 – Union Tables

602 – Unstructured Data

603 – User Group Based

604 – User Guiding Sentences

605 – Value Approximisation

606 – Variables

607 – Variance

608 – Variational Autoencoders – VAEs

609 – Video

610 – Visual AI

611 – Visual Analytics

612 – Visual Representation

613 – Visualisation

614 – Waterfall

615 – Waterfall Diagrams

616 – Web Scraping

617 – Web Services

618 – Weight Initialization

619 – WMS – Warehouse Management System

620 – Word Cloud

621 – Word2Vec

622 – Year Over Year

623 – Year To Date

624 – Z-Score

625 – Z-score method

Student Ratings & Reviews

No Review Yet
No Review Yet
₹1,500.00
  • Level
    Intermediate
  • Last Updated
    November 19, 2025
  • Certificate
    Certificate of completion

A course by

M

Tags

error: Content is protected !!