Jan 08 2023 Tutorials On Python & Data Science - Python + Data Science BaDshaH LEARNING / e-learning - Tutorials 10:35 0 Last updated 5/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 8.69 GB | Duration: 26h 45mPython Tutorials - Master Python Programming Online - Python How to Learn + Python Data Scientist - NLP, IBM Watson, ... What you'll learnDevelop python based applicationsDevelop marketing applications with PythonMine twitter data with Python to get grasp of people's opinion on trending mattersDevelop Natural Language Processing (NLP) applications with Python to process everyday languageCreate Machine Learning applications with Python to make your computer smart and automate the boring tasksCreate Deep Learning applications with Python to add Artificial Intelligence to your machine learning models and create even smarter modelsUse IBM Watson to unlock the vast world of unstructured data and create your own language translator applications with PythonCreate Big Data applications with the help of the Relational Databases and Python clear and concise syntaxUse Data Science to predict business predictions and business intelligenceAutomate everyday tasks and save timeRequirementsNo programming experience needed. You will learn everything you need to knowA computer with Windows, Mac, Linux, ChromeOS operating system installedDescriptionThe main goal of this course is to teach you how to code using Python 3 & Data Science. My name is Morteza Kordi, Senior Python Programmer & Data Science Specialist and Udemy instructor with over 70,000 satisfied students, and I've designed Tutorials on Python & Data Science - Python + Data Science with one thing in mind: you should learn by practicing your skills and building apps. I'll personally be answering any questions you might have and I'll be happy to provide links, resources, and any help I can offer to help you master Python 3 & Data Science as well as Machine Learning. In this course, I will demonstrate the power of Python & Data Science, and how I dramatically increased my career prospects as a Programmer. New to Programming or Python? I'll personally teach you the fundamentals of programming & Python. you will master the basics before diving into the advanced stuff. So no programming experience is required.Want to learn about Natural Language Processing (NLP)? This Course contains a comprehensive course about NLP too. Want to learn about IBM Watson and Cognitive Computing? If you want to process unstructured data, deal with human limitations, improve performance and abilities or handle enormous quantities of data then you should learn IBM Watson and Cognitive Computing. This Course has the answer for you.Want to learn Machine Learning? If you want to simplify your product marketing, get accurate sales forecasts, facilitate accurate medical predictions and diagnoses, simplify time-intensive documentation in data entry, improve the precision of financial rules and models, and easy spam detection then you should learn Machine Learning. Again This Course has the answer for you.Want to learn Deep Learning? Do you struggle with processing large numbers of features? If yes, then you should learn Deep Learning. Again This Course covers this topic too!So... Why This Course?!Learn to code like the pros - not just copy and pasteLearn the Latest Python 3 APIs and services - we don't teach old junkLearn to build apps - a lot of themNo Programming Experience is neededBuild Real-world AppsLifetime SupportDon't wait and join us now by clicking the BUY NOW button!OverviewSection 1: IntroductionLecture 1 IntroductionSection 2: Download & Install the Required SoftwaresLecture 2 Install AnacondaLecture 3 Update AnacondaLecture 4 Our package managersLecture 5 Install jupyter-matplotlibLecture 6 Download and Install Visual Studio CodeSection 3: Learn to Use IPyton & Jupyter NotebooksLecture 7 IPythonLecture 8 Jupyter NotebookSection 4: Python Programming BasicsLecture 9 VariablesLecture 10 Source codeLecture 11 ArithmeticLecture 12 Source codeLecture 13 Strings - Single Quoted & Double Quoted StringsLecture 14 Source codeLecture 15 Triple-quoted StringsLecture 16 Source codeLecture 17 Get input from userLecture 18 Source codeLecture 19 Decision makingLecture 20 ObjectsLecture 21 Source codeSection 5: Control Statements in PythonLecture 22 if, elif and elseLecture 23 Source codeLecture 24 while loopLecture 25 Source codeLecture 26 for loopLecture 27 Source codeLecture 28 Augmented assignmentsLecture 29 Source codeLecture 30 Sequence iterationLecture 31 Source codeLecture 32 Sentinel iterationLecture 33 Source codeLecture 34 Range functionLecture 35 Source codeLecture 36 Decimal typeLecture 37 Source codeLecture 38 Logical operatorsLecture 39 Source codeSection 6: Functions in PythonLecture 40 Defining functionsLecture 41 Source codeLecture 42 Functions with multiple parametersLecture 43 Source codeLecture 44 Random number generationLecture 45 Source codeLecture 46 math ModuleLecture 47 Source codeLecture 48 Default Argument ValueLecture 49 Source codeLecture 50 Keyword ArgumentsLecture 51 Source codeLecture 52 Arbitrary Parameter ListLecture 53 Source codeLecture 54 MethodsLecture 55 Source codeLecture 56 ScopingLecture 57 Source codeLecture 58 Import statementLecture 59 Source codeLecture 60 Function argumentsLecture 61 Source codeLecture 62 ReproducibilityLecture 63 Source codeSection 7: Sequences in Python Programming - Master Lists & TuplesLecture 64 Intro - What we are going to learn in this section of the courseLecture 65 Install Code-Runner Extension in Visual Studio CodeLecture 66 A List of Integer Values & Accessing List Elements With Positive IndicesLecture 67 Source CodeLecture 68 Negatives Indices & Math Operations to access elements & Mutable ListsLecture 69 Source CodeLecture 70 Populating list with a range & Concatenation Operator & Boolean OperationsLecture 71 Source CodeLecture 72 TuplesLecture 73 Tuples Source CodeLecture 74 Why you should learn about sequence unpacking in PythonLecture 75 Unpacking Tuples, Strings & ListsLecture 76 Unpacking Tuples, Strings & Lists - Source CodeLecture 77 Unpacking Range of Integer ValuesLecture 78 Unpacking Range of Integer Values - Source CodeLecture 79 Use "Unpacking" to add swapping feature to your appLecture 80 Use "Unpacking" to add swapping feature to your app - Source CodeLecture 81 Unpacking Enumerated Sequences With their Indices & Corresponding ValuesLecture 82 Unpacking Enumerated Sequences - Source CodeLecture 83 Create a primitive bar chart with # ;)Lecture 84 Source CodeLecture 85 Slice an ordered subset of sequence valuesLecture 86 Source CodeLecture 87 Slice an intermittent subset of sequence valuesLecture 88 Source CodeLecture 89 Use negative indices to slice a reversed subset of sequence valuesLecture 90 Source CodeLecture 91 Count backwards the sequence - "The HARD way"Lecture 92 Source CodeLecture 93 Update a subset of sequence valuesLecture 94 Source CodeLecture 95 Delete a subset of sequence valuesLecture 96 Source CodeLecture 97 Modify an intermittent subset of sequence valuesLecture 98 Source CodeLecture 99 Determine the identity of your sequence object after slicingLecture 100 Source CodeLecture 101 Del StatementLecture 102 Source CodeLecture 103 Pass a list object to a function - Passing by reference explained!Lecture 104 Source CodeLecture 105 The Sort MethodLecture 106 Source CodeLecture 107 The Sorted FunctionLecture 108 Source CodeLecture 109 Sequence SearchingLecture 110 Source CodeLecture 111 Usages of "in" and "not in" keywords when it comes to sequence searchingLecture 112 Source CodeLecture 113 Inserting & AppendingLecture 114 Source CodeLecture 115 Extend your listLecture 116 Source CodeLecture 117 Remove & Clear List ElementsLecture 118 Source CodeLecture 119 Count up the list items and determine the occurrenceLecture 120 Source CodeLecture 121 Reverse your list elementsLecture 122 Source CodeLecture 123 How to create a shallow list copy of your list elementsLecture 124 Source CodeLecture 125 How to create a shallow list copy of your list elementsLecture 126 Source CodeLecture 127 Stack Data Structure and the pop() functionLecture 128 Source CodeLecture 129 Simple List Comprehension CreationLecture 130 Source CodeLecture 131 Complex List Comprehension CreationLecture 132 Source CodeLecture 133 Add decision making to your list comprehensionLecture 134 Source CodeLecture 135 Apply List Comprehension other sorts of sequencesLecture 136 Source CodeLecture 137 Generator Expression Vs List Comprehension - Which one is better?Lecture 138 Source CodeLecture 139 Generator ExpressionsLecture 140 Source CodeLecture 141 Functional Programming With Filter()Lecture 142 Source CodeLecture 143 Use Lambda Expression to Simplify the Process of FilteringLecture 144 Source CodeLecture 145 Functional Programming With Map()Lecture 146 Source CodeLecture 147 Functional Programming With Reduce()Lecture 148 Source CodeLecture 149 The ord fucntion - Get the numeric value of your sequence!Lecture 150 Source CodeLecture 151 Sequence processing with min() and max()Lecture 152 Source CodeLecture 153 The Zip FunctionLecture 154 Source CodeLecture 155 Two Dimensional ArraysLecture 156 Source CodeSection 8: Dictionaries & Sets in PythonLecture 157 Intro - What is dictionary & setLecture 158 How to create a dictionary in PythonLecture 159 Source CodeLecture 160 Iterate through a dictionaryLecture 161 Source CodeLecture 162 Access, Update and Insert new Entities to your DictionaryLecture 163 Source CodeLecture 164 Remove Entities From your DictionaryLecture 165 Source CodeLecture 166 Get FunctionLecture 167 Source CodeLecture 168 Keys & Values Methods and OperationsLecture 169 Source CodeLecture 170 Dictionary ComparisonLecture 171 Source CodeLecture 172 SetsLecture 173 Source CodeLecture 174 Comparing SetsLecture 175 Source CodeLecture 176 Union FunctionLecture 177 Source CodeLecture 178 Intersection FunctionLecture 179 Source CodeLecture 180 Difference FunctionLecture 181 Source CodeLecture 182 Symmetric Difference FunctionLecture 183 Source CodeLecture 184 IsDisjoint FunctionLecture 185 Source CodeLecture 186 Update MethodLecture 187 Source CodeLecture 188 Add MethodLecture 189 Source CodeLecture 190 Remove MethodLecture 191 Source CodeSection 9: Array Oriented Programming With NumpyLecture 192 IntroLecture 193 Creating Arrays & Two Dimensional Arrays Using NumpyLecture 194 Source CodeLecture 195 Numpy Array AttributesLecture 196 Source CodeLecture 197 Populate your array with special valuesLecture 198 Source CodeLecture 199 Create Arrays Using RangesLecture 200 Source CodeSection 10: Master Strings in PythonLecture 201 IntroLecture 202 Presentation TypesLecture 203 Source CodeLecture 204 Field Widths & AlignmentLecture 205 Source CodeLecture 206 Numeric FormattingLecture 207 Source CodeLecture 208 String's Format MethodLecture 209 Source CodeLecture 210 Concatenating & Repeating StringsLecture 211 Source CodeLecture 212 Stripping Whitespace From StringsLecture 213 Source CodeSection 11: Files & Exceptions in PythonLecture 214 IntroLecture 215 Learn about files in Python - How Python treats them?Lecture 216 How to write to a text fileLecture 217 Source CodeLecture 218 How to read data from a text fileLecture 219 Source CodeLecture 220 Update your text fileLecture 221 Source CodeLecture 222 Exception HandlingLecture 223 Facing Invalid Data or InputLecture 224 Source CodeLecture 225 Try StatementLecture 226 Source CodeLecture 227 Finally ClauseLecture 228 Source CodeLecture 229 Extra point: Wrap the with statement with try suitLecture 230 Source CodeSection 12: Object Oriented ProgrammingLecture 231 IntroLecture 232 Create your custom classLecture 233 Source CodeLecture 234 Attribute access controlLecture 235 PropertiesLecture 236 Source CodeLecture 237 Private attribute simulationLecture 238 Source CodeLecture 239 InheritanceLecture 240 Source CodeLecture 241 PolymorphismLecture 242 Source CodeLecture 243 Duck typingLecture 244 Source CodeLecture 245 Object classLecture 246 Operator overloadingSection 13: Natural Language Processing (NLP)Lecture 247 IntroLecture 248 Get TextblobLecture 249 Create TextblobgLecture 250 Source CodeLecture 251 Text tokenizingLecture 252 Source CodeLecture 253 Parts of speech taggingLecture 254 Source CodeLecture 255 Noun phrase extractionLecture 256 Source CodeLecture 257 Textblob's default sentiment analyzerLecture 258 Source CodeLecture 259 NaiveBayesAnalyzerLecture 260 Source CodeLecture 261 Language detection and translationLecture 262 Source CodeLecture 263 Pluralization & SingularizationLecture 264 Source CodeLecture 265 Spell checking & CorrectionLecture 266 Source CodeSection 14: Twitter Data MiningLecture 267 IntroLecture 268 Create your twitter developer accountLecture 269 Get yourself comfortable with reading Twitter API docsLecture 270 Create your first twitter app project and access the private credentialsLecture 271 Install the tweepy module on your systemLecture 272 Authenticate with twitterLecture 273 Source CodeLecture 274 Access information of a twitter accountLecture 275 Source CodeLecture 276 Access user's followers and friends by using cursor objectLecture 277 Source CodeLecture 278 Find out who the user's followers are!Lecture 279 Source CodeLecture 280 Find out who the user's followings are!Lecture 281 Source CodeLecture 282 Get user's latest tweetsLecture 283 Source CodeLecture 284 Search the recent tweetsLecture 285 Source CodeSection 15: IBM Watson & Cognitive ComputingLecture 286 IntroLecture 287 IBM Watson explainedLecture 288 Create an IBM cloud accountLecture 289 Install the necessary componentsLecture 290 Translator app demoLecture 291 Translator app to do listLecture 292 Register for the speech to text serviceLecture 293 Register for the text to speech serviceLecture 294 Register for the language translator serviceLecture 295 Import Watson SDK classes and media modulesLecture 296 Source codeLecture 297 Translate function & entry pointLecture 298 Source CodeLecture 299 Record user's voice functionLecture 300 Source codeLecture 301 Step #1 : Record english audioLecture 302 Source codeLecture 303 Speech to text functionLecture 304 Source codeLecture 305 Step #2: Transcribe english speech to english textLecture 306 Source codeLecture 307 Translate functionLecture 308 Source codeLecture 309 Step #3: Translate the english text into french textLecture 310 Source codeLecture 311 Text to speech functionLecture 312 Source codeLecture 313 Step #4: Convert the french text into spoken french audioLecture 314 Source codeLecture 315 Play functionLecture 316 Source codeLecture 317 Step #5: Play french audioLecture 318 Source codeLecture 319 Step #6: Record french audioLecture 320 Source codeLecture 321 Step #7: Transcribe the french speech to french textLecture 322 Source codeLecture 323 Step #8: Translate the french text into english textLecture 324 Source codeLecture 325 Step #9: Convert the english text into spoken english audioLecture 326 Source codeLecture 327 Step #10: Play english audio & finishing touchesLecture 328 Source codeLecture 329 Project source codeSection 16: Machine learning in PythonLecture 330 IntroLecture 331 Machine Learning TypesLecture 332 Classification modelLecture 333 Scikit-Learn libraryLecture 334 DatasetsLecture 335 Digits datasetLecture 336 K-Nearest Neighbors AlgorithmLecture 337 HyperparametersLecture 338 Loading the digits datasetLecture 339 Source codeLecture 340 Target & Data attributesLecture 341 Source codeLecture 342 Set up dataLecture 343 Source codeLecture 344 Create a diagramLecture 345 Source codeLecture 346 Display digit imagesLecture 347 Source codeLecture 348 Splitting data for training and testing purposesLecture 349 Source codeLecture 350 Training & Testing size customizationLecture 351 Source codeLecture 352 Create the ModelLecture 353 Source codeLecture 354 Train the ModelLecture 355 Source codeLecture 356 Predict data & Test your modelLecture 357 Source codeLecture 358 Final source codeSection 17: Deep learning in PythonLecture 359 IntroductionLecture 360 Deep learning modelsLecture 361 Neural networksLecture 362 Artificial neuronsLecture 363 Artificial Neural Network DiagramLecture 364 Iterative learning processLecture 365 How synapses are activatedLecture 366 Backpropagation techniqueLecture 367 TensorsLecture 368 ConvnetsLecture 369 MNIST digits datasetLecture 370 Probabilistic classificationLecture 371 Keras reproducibilityLecture 372 Keras neural network componentsLecture 373 Loading MNIST DatasetLecture 374 Source codeLecture 375 Explore MNIST DataLecture 376 Source codeLecture 377 Digits visualizationLecture 378 Source codeLecture 379 Data preparation process - ReshapingLecture 380 Source codeLecture 381 Data preparation - NormalizationLecture 382 Source codeLecture 383 Data preparation - Converting labels to categorical dataLecture 384 Source codeLecture 385 Neural Network CreationLecture 386 Source codeLecture 387 Integrating layers into the networkLecture 388 Source codeLecture 389 The Convolution ProcessLecture 390 Add Conv2D LayerLecture 391 Source codeLecture 392 Conv2D Output DimensionalityLecture 393 OverfittingLecture 394 Add a Pooling LayerLecture 395 Source codeLecture 396 Add One More Convolution LayerLecture 397 Source codeLecture 398 Add one more pooling layerLecture 399 Source codeLecture 400 Add Flatten LayerLecture 401 Source codeLecture 402 Add a Dense Layer to reduce the featuresLecture 403 Source codeLecture 404 Add a Dense Layer to produce the final resultsLecture 405 Source codeLecture 406 Model's SummaryLecture 407 Source codeLecture 408 Model Structure VisualizationLecture 409 Source codeLecture 410 Compile the modelLecture 411 Source codeLecture 412 Train the modelLecture 413 Source codeLecture 414 Evaluate the modelLecture 415 Source codeLecture 416 Predict dataLecture 417 Source codeLecture 418 Display the incorrect predictionsLecture 419 Source codeLecture 420 Visualize the incorrect predictionsLecture 421 Source codeLecture 422 Access the wrong predictions' probabilitiesLecture 423 Source codeLecture 424 Saving & Loading our modelLecture 425 Source codeSection 18: Big DataLecture 426 DatabasesLecture 427 Relational databasesLecture 428 Create a sqlite databaseLecture 429 Source codeLecture 430 Create a tableLecture 431 Source codeLecture 432 Create a list of martial artsLecture 433 Source codeLecture 434 Insert data into the databaseLecture 435 Source codeLecture 436 Access the database dataLecture 437 Source codeLecture 438 Update the database dataLecture 439 Source codeLecture 440 Delete the database dataLecture 441 Source codeSection 19: Data ScienceLecture 442 Intro to datascienceLecture 443 Descriptive statisticsLecture 444 Source codeLecture 445 Measures of central tendencyLecture 446 MeanLecture 447 Source codeLecture 448 MedianLecture 449 Source codeLecture 450 ModeLecture 451 Source codeLecture 452 Measures of DispersionLecture 453 VarianceLecture 454 Source codeLecture 455 Standard deviationLecture 456 Source codeLecture 457 Static visualizationLecture 458 Import the necessary modulesLecture 459 Source codeLecture 460 Roll the diceLecture 461 Source codeLecture 462 Set the title and style of your visualizationLecture 463 Source codeLecture 464 Start the visualizationLecture 465 Source codeLecture 466 Setting up title for each barLecture 467 Source codePeople with no programming experience who are curious about creating their own Python & Data Science applications,Beginner Python developers who are curious about creating Data Science applications,People who are curious about Natural Language Processing (NLP) and want to develop their own NLP applications with Python,People who are curious about making their computers smart using Machine Learning & Deep Learning with Python,People who are curious about mining precious data from twitter and create their own marketing applications with Python,People who are curious about cognitive programming and want to create smart applications by taking advantage of unstructured 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