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Python 3 And Data Science Mastery - Practical Python 3

4-09-2022, 14:29 | 0nelove | Tutorials | 177 | 0

Last updated 5/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.69 GB | Duration: 26h 45m

Develop Python 3 and Data Science Apps - Python 3 and Data Science Class - Real World Python 3 and Data Science Projects
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 time
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 The Ultimate Hands-On Python 3 and Data Science Bootcamp 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!
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Download & Install the Required Softwares
Lecture 2 Install Anaconda
Lecture 3 Update Anaconda
Lecture 4 Our package managers
Lecture 5 Install jupyter-matplotlib
Lecture 6 Download 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 intermittent 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 intermittent subset of sequence values
Lecture 98 Source Code
Lecture 99 Determine 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 determine 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 sentiment 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,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 data
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