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Python Tutorials - Master Python Programming Online - Python How to Learn + Python Data Scientist - NLP, IBM Watson, ... What you'll learn Develop python based applications Develop marketing applications with Python Mine twitter data with Python to get grasp of people's opinion on trending matters Develop Natural Language Processing (NLP) applications with Python to process everyday language Create Machine Learning applications with Python to make your computer smart and automate the boring tasks Create Deep Learning applications with Python to add Artificial Intelligence to your machine learning models and create even smarter models Use IBM Watson to unlock the vast world of unstructured data and create your own language translator applications with Python Create Big Data applications with the help of the Relational Databases and Python clear and concise syntax Use Data Science to predict business predictions and business intelligence Automate everyday tasks and save Requirements No programming experience needed. You will learn everything you need to know A computer with Windows, Mac, Linux, ChromeOS operating system installed Description The 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 -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 AppsLife SupportDon't wait and join us now by clicking the BUY NOW button! Overview Section 1: Introduction Lecture 1 Introduction Section 2: & Install the Required Softwares Lecture 2 Install Anaconda Lecture 3 Update Anaconda Lecture 4 Our package managers Lecture 5 Install jupyter-matplotlib Lecture 6 and Install Visual Studio Code Section 3: Learn to Use IPyton & Jupyter Notebooks Lecture 7 IPython Lecture 8 Jupyter Notebook Section 4: Python Programming Basics Lecture 9 Variables Lecture 10 Source code Lecture 11 Arithmetic Lecture 12 Source code Lecture 13 Strings - Single Quoted & Double Quoted Strings Lecture 14 Source code Lecture 15 Triple-quoted Strings Lecture 16 Source code Lecture 17 Get input from user Lecture 18 Source code Lecture 19 Decision making Lecture 20 Objects Lecture 21 Source code Section 5: Control Statements in Python Lecture 22 if, elif and else Lecture 23 Source code Lecture 24 while loop Lecture 25 Source code Lecture 26 for loop Lecture 27 Source code Lecture 28 Augmented assignments Lecture 29 Source code Lecture 30 Sequence iteration Lecture 31 Source code Lecture 32 Sentinel iteration Lecture 33 Source code Lecture 34 Range function Lecture 35 Source code Lecture 36 Decimal type Lecture 37 Source code Lecture 38 Logical operators Lecture 39 Source code Section 6: Functions in Python Lecture 40 Defining functions Lecture 41 Source code Lecture 42 Functions with multiple parameters Lecture 43 Source code Lecture 44 Random number generation Lecture 45 Source code Lecture 46 math Module Lecture 47 Source code Lecture 48 Default Argument Value Lecture 49 Source code Lecture 50 Keyword Arguments Lecture 51 Source code Lecture 52 Arbitrary Parameter List Lecture 53 Source code Lecture 54 Methods Lecture 55 Source code Lecture 56 Scoping Lecture 57 Source code Lecture 58 Import statement Lecture 59 Source code Lecture 60 Function arguments Lecture 61 Source code Lecture 62 Reproducibility Lecture 63 Source code Section 7: Sequences in Python Programming - Master Lists & Tuples Lecture 64 Intro - What we are going to learn in this section of the course Lecture 65 Install Code-Runner Extension in Visual Studio Code Lecture 66 A List of Integer Values & Accessing List Elements With Positive Indices Lecture 67 Source Code Lecture 68 Negatives Indices & Math Operations to access elements & Mutable Lists Lecture 69 Source Code Lecture 70 Populating list with a range & Concatenation Operator & Boolean Operations Lecture 71 Source Code Lecture 72 Tuples Lecture 73 Tuples Source Code Lecture 74 Why you should learn about sequence unpacking in Python Lecture 75 Unpacking Tuples, Strings & Lists Lecture 76 Unpacking Tuples, Strings & Lists - Source Code Lecture 77 Unpacking Range of Integer Values Lecture 78 Unpacking Range of Integer Values - Source Code Lecture 79 Use "Unpacking" to add swapping feature to your app Lecture 80 Use "Unpacking" to add swapping feature to your app - Source Code Lecture 81 Unpacking Enumerated Sequences With their Indices & Corresponding Values Lecture 82 Unpacking Enumerated Sequences - Source Code Lecture 83 Create a primitive bar chart with # ;) Lecture 84 Source Code Lecture 85 Slice an ordered subset of sequence values Lecture 86 Source Code Lecture 87 Slice an intetent subset of sequence values Lecture 88 Source Code Lecture 89 Use negative indices to slice a reversed subset of sequence values Lecture 90 Source Code Lecture 91 Count backwards the sequence - "The HARD way" Lecture 92 Source Code Lecture 93 Update a subset of sequence values Lecture 94 Source Code Lecture 95 Delete a subset of sequence values Lecture 96 Source Code Lecture 97 Modify an intetent subset of sequence values Lecture 98 Source Code Lecture 99 Detee the identity of your sequence object after slicing Lecture 100 Source Code Lecture 101 Del Statement Lecture 102 Source Code Lecture 103 Pass a list object to a function - Passing by reference explained! Lecture 104 Source Code Lecture 105 The Sort Method Lecture 106 Source Code Lecture 107 The Sorted Function Lecture 108 Source Code Lecture 109 Sequence Searching Lecture 110 Source Code Lecture 111 Usages of "in" and "not in" keywords when it comes to sequence searching Lecture 112 Source Code Lecture 113 Inserting & Appending Lecture 114 Source Code Lecture 115 Extend your list Lecture 116 Source Code Lecture 117 Remove & Clear List Elements Lecture 118 Source Code Lecture 119 Count up the list items and detee the occurrence Lecture 120 Source Code Lecture 121 Reverse your list elements Lecture 122 Source Code Lecture 123 How to create a shallow list copy of your list elements Lecture 124 Source Code Lecture 125 How to create a shallow list copy of your list elements Lecture 126 Source Code Lecture 127 Stack Data Structure and the pop function Lecture 128 Source Code Lecture 129 Simple List Comprehension Creation Lecture 130 Source Code Lecture 131 Complex List Comprehension Creation Lecture 132 Source Code Lecture 133 Add decision making to your list comprehension Lecture 134 Source Code Lecture 135 Apply List Comprehension other sorts of sequences Lecture 136 Source Code Lecture 137 Generator Expression Vs List Comprehension - Which one is better? Lecture 138 Source Code Lecture 139 Generator Expressions Lecture 140 Source Code Lecture 141 Functional Programming With Filter Lecture 142 Source Code Lecture 143 Use Lambda Expression to Simplify the Process of Filtering Lecture 144 Source Code Lecture 145 Functional Programming With Map Lecture 146 Source Code Lecture 147 Functional Programming With Reduce Lecture 148 Source Code Lecture 149 The ord fucntion - Get the numeric value of your sequence! Lecture 150 Source Code Lecture 151 Sequence processing with min and max Lecture 152 Source Code Lecture 153 The Zip Function Lecture 154 Source Code Lecture 155 Two Dimensional Arrays Lecture 156 Source Code Section 8: Dictionaries & Sets in Python Lecture 157 Intro - What is dictionary & set Lecture 158 How to create a dictionary in Python Lecture 159 Source Code Lecture 160 Iterate through a dictionary Lecture 161 Source Code Lecture 162 Access, Update and Insert new Entities to your Dictionary Lecture 163 Source Code Lecture 164 Remove Entities From your Dictionary Lecture 165 Source Code Lecture 166 Get Function Lecture 167 Source Code Lecture 168 Keys & Values Methods and Operations Lecture 169 Source Code Lecture 170 Dictionary Comparison Lecture 171 Source Code Lecture 172 Sets Lecture 173 Source Code Lecture 174 Comparing Sets Lecture 175 Source Code Lecture 176 Union Function Lecture 177 Source Code Lecture 178 Intersection Function Lecture 179 Source Code Lecture 180 Difference Function Lecture 181 Source Code Lecture 182 Symmetric Difference Function Lecture 183 Source Code Lecture 184 IsDisjoint Function Lecture 185 Source Code Lecture 186 Update Method Lecture 187 Source Code Lecture 188 Add Method Lecture 189 Source Code Lecture 190 Remove Method Lecture 191 Source Code Section 9: Array Oriented Programming With Numpy Lecture 192 Intro Lecture 193 Creating Arrays & Two Dimensional Arrays Using Numpy Lecture 194 Source Code Lecture 195 Numpy Array Attributes Lecture 196 Source Code Lecture 197 Populate your array with special values Lecture 198 Source Code Lecture 199 Create Arrays Using Ranges Lecture 200 Source Code Section 10: Master Strings in Python Lecture 201 Intro Lecture 202 Presentation Types Lecture 203 Source Code Lecture 204 Field Widths & Alignment Lecture 205 Source Code Lecture 206 Numeric Formatting Lecture 207 Source Code Lecture 208 String's Format Method Lecture 209 Source Code Lecture 210 Concatenating & Repeating Strings Lecture 211 Source Code Lecture 212 Stripping Whitespace From Strings Lecture 213 Source Code Section 11: Files & Exceptions in Python Lecture 214 Intro Lecture 215 Learn about files in Python - How Python treats them? Lecture 216 How to write to a text file Lecture 217 Source Code Lecture 218 How to read data from a text file Lecture 219 Source Code Lecture 220 Update your text file Lecture 221 Source Code Lecture 222 Exception Handling Lecture 223 Facing Invalid Data or Input Lecture 224 Source Code Lecture 225 Try Statement Lecture 226 Source Code Lecture 227 Finally Clause Lecture 228 Source Code Lecture 229 Extra point: Wrap the with statement with try suit Lecture 230 Source Code Section 12: Object Oriented Programming Lecture 231 Intro Lecture 232 Create your custom class Lecture 233 Source Code Lecture 234 Attribute access control Lecture 235 Properties Lecture 236 Source Code Lecture 237 Private attribute simulation Lecture 238 Source Code Lecture 239 Inheritance Lecture 240 Source Code Lecture 241 Polymorphism Lecture 242 Source Code Lecture 243 Duck typing Lecture 244 Source Code Lecture 245 Object class Lecture 246 Operator overloading Section 13: Natural Language Processing (NLP) Lecture 247 Intro Lecture 248 Get Textblob Lecture 249 Create Textblobg Lecture 250 Source Code Lecture 251 Text tokenizing Lecture 252 Source Code Lecture 253 Parts of speech tagging Lecture 254 Source Code Lecture 255 Noun phrase extraction Lecture 256 Source Code Lecture 257 Textblob's default sennt analyzer Lecture 258 Source Code Lecture 259 NaiveBayesAnalyzer Lecture 260 Source Code Lecture 261 Language detection and translation Lecture 262 Source Code Lecture 263 Pluralization & Singularization Lecture 264 Source Code Lecture 265 Spell checking & Correction Lecture 266 Source Code Section 14: Twitter Data Mining Lecture 267 Intro Lecture 268 Create your twitter developer account Lecture 269 Get yourself comfortable with reading Twitter API docs Lecture 270 Create your first twitter app project and access the private credentials Lecture 271 Install the tweepy module on your system Lecture 272 Authenticate with twitter Lecture 273 Source Code Lecture 274 Access information of a twitter account Lecture 275 Source Code Lecture 276 Access user's followers and friends by using cursor object Lecture 277 Source Code Lecture 278 Find out who the user's followers are! Lecture 279 Source Code Lecture 280 Find out who the user's followings are! Lecture 281 Source Code Lecture 282 Get user's latest tweets Lecture 283 Source Code Lecture 284 Search the recent tweets Lecture 285 Source Code Section 15: IBM Watson & Cognitive Computing Lecture 286 Intro Lecture 287 IBM Watson explained Lecture 288 Create an IBM cloud account Lecture 289 Install the necessary components Lecture 290 Translator app demo Lecture 291 Translator app to do list Lecture 292 Register for the speech to text service Lecture 293 Register for the text to speech service Lecture 294 Register for the language translator service Lecture 295 Import Watson SDK classes and media modules Lecture 296 Source code Lecture 297 Translate function & entry point Lecture 298 Source Code Lecture 299 Record user's voice function Lecture 300 Source code Lecture 301 Step #1 : Record english audio Lecture 302 Source code Lecture 303 Speech to text function Lecture 304 Source code Lecture 305 Step #2: Transcribe english speech to english text Lecture 306 Source code Lecture 307 Translate function Lecture 308 Source code Lecture 309 Step #3: Translate the english text into french text Lecture 310 Source code Lecture 311 Text to speech function Lecture 312 Source code Lecture 313 Step #4: Convert the french text into spoken french audio Lecture 314 Source code Lecture 315 Play function Lecture 316 Source code Lecture 317 Step #5: Play french audio Lecture 318 Source code Lecture 319 Step #6: Record french audio Lecture 320 Source code Lecture 321 Step #7: Transcribe the french speech to french text Lecture 322 Source code Lecture 323 Step #8: Translate the french text into english text Lecture 324 Source code Lecture 325 Step #9: Convert the english text into spoken english audio Lecture 326 Source code Lecture 327 Step #10: Play english audio & finishing touches Lecture 328 Source code Lecture 329 Project source code Section 16: Machine learning in Python Lecture 330 Intro Lecture 331 Machine Learning Types Lecture 332 Classification model Lecture 333 Scikit-Learn library Lecture 334 Datasets Lecture 335 Digits dataset Lecture 336 K-Nearest Neighbors Algorithm Lecture 337 Hyperparameters Lecture 338 Loading the digits dataset Lecture 339 Source code Lecture 340 Target & Data attributes Lecture 341 Source code Lecture 342 Set up data Lecture 343 Source code Lecture 344 Create a diagram Lecture 345 Source code Lecture 346 Display digit images Lecture 347 Source code Lecture 348 Splitting data for training and testing purposes Lecture 349 Source code Lecture 350 Training & Testing size customization Lecture 351 Source code Lecture 352 Create the Model Lecture 353 Source code Lecture 354 Train the Model Lecture 355 Source code Lecture 356 Predict data & Test your model Lecture 357 Source code Lecture 358 Final source code Section 17: Deep learning in Python Lecture 359 Introduction Lecture 360 Deep learning models Lecture 361 Neural networks Lecture 362 Artificial neurons Lecture 363 Artificial Neural Network Diagram Lecture 364 Iterative learning process Lecture 365 How synapses are activated Lecture 366 Backpropagation technique Lecture 367 Tensors Lecture 368 Convnets Lecture 369 MNIST digits dataset Lecture 370 Probabilistic classification Lecture 371 Keras reproducibility Lecture 372 Keras neural network components Lecture 373 Loading MNIST Dataset Lecture 374 Source code Lecture 375 Explore MNIST Data Lecture 376 Source code Lecture 377 Digits visualization Lecture 378 Source code Lecture 379 Data preparation process - Reshaping Lecture 380 Source code Lecture 381 Data preparation - Normalization Lecture 382 Source code Lecture 383 Data preparation - Converting labels to categorical data Lecture 384 Source code Lecture 385 Neural Network Creation Lecture 386 Source code Lecture 387 Integrating layers into the network Lecture 388 Source code Lecture 389 The Convolution Process Lecture 390 Add Conv2D Layer Lecture 391 Source code Lecture 392 Conv2D Output Dimensionality Lecture 393 Overfitting Lecture 394 Add a Pooling Layer Lecture 395 Source code Lecture 396 Add One More Convolution Layer Lecture 397 Source code Lecture 398 Add one more pooling layer Lecture 399 Source code Lecture 400 Add Flatten Layer Lecture 401 Source code Lecture 402 Add a Dense Layer to reduce the features Lecture 403 Source code Lecture 404 Add a Dense Layer to produce the final results Lecture 405 Source code Lecture 406 Model's Summary Lecture 407 Source code Lecture 408 Model Structure Visualization Lecture 409 Source code Lecture 410 Compile the model Lecture 411 Source code Lecture 412 Train the model Lecture 413 Source code Lecture 414 Evaluate the model Lecture 415 Source code Lecture 416 Predict data Lecture 417 Source code Lecture 418 Display the incorrect predictions Lecture 419 Source code Lecture 420 Visualize the incorrect predictions Lecture 421 Source code Lecture 422 Access the wrong predictions’ probabilities Lecture 423 Source code Lecture 424 Saving & Loading our model Lecture 425 Source code Section 18: Big Data Lecture 426 Databases Lecture 427 Relational databases Lecture 428 Create a sqlite database Lecture 429 Source code Lecture 430 Create a table Lecture 431 Source code Lecture 432 Create a list of martial arts Lecture 433 Source code Lecture 434 Insert data into the database Lecture 435 Source code Lecture 436 Access the database data Lecture 437 Source code Lecture 438 Update the database data Lecture 439 Source code Lecture 440 Delete the database data Lecture 441 Source code Section 19: Data Science Lecture 442 Intro to datascience Lecture 443 Descriptive statistics Lecture 444 Source code Lecture 445 Measures of central tendency Lecture 446 Mean Lecture 447 Source code Lecture 448 Median Lecture 449 Source code Lecture 450 Mode Lecture 451 Source code Lecture 452 Measures of Dispersion Lecture 453 Variance Lecture 454 Source code Lecture 455 Standard deviation Lecture 456 Source code Lecture 457 Static visualization Lecture 458 Import the necessary modules Lecture 459 Source code Lecture 460 Roll the dice Lecture 461 Source code Lecture 462 Set the title and style of your visualization Lecture 463 Source code Lecture 464 Start the visualization Lecture 465 Source code Lecture 466 Setting up title for each bar Lecture 467 Source code People with no programming experience who are curious about creating their own Python & Data Science applications,Bner 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 data HomePage: gfxtra__Tutorials_.part01.rar.html gfxtra__Tutorials_.part02.rar.html gfxtra__Tutorials_.part03.rar.html gfxtra__Tutorials_.part04.rar.html gfxtra__Tutorials_.part05.rar.html gfxtra__Tutorials_.part06.rar.html gfxtra__Tutorials_.part07.rar.html
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