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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

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