Tutorials on Python & Data Science – Python + Data Science

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Python Tutorials - Master Python Programming Online - Python How to Learn + Python Data Scientist - NLP, IBM Watson, ...

1 - Introduction

1 - Introduction

2 - Download & Install the Required Softwares

2 - Install Anaconda

3 - Update Anaconda

4 - Our package managers

5 - Install jupytermatplotlib

6 - Download and Install Visual Studio Code

3 - Learn to Use IPyton & Jupyter Notebooks

7 - IPython

8 - Jupyter Notebook

4 - Python Programming Basics

9 - Variables

10 - Source code.html

11 - Arithmetic

12 - Source code.html

13 - Strings Single Quoted & Double Quoted Strings

14 - Source code.html

15 - Triplequoted Strings

16 - Source code.html

17 - Get input from user

18 - Source code.html

19 - Decision making

20 - Objects

21 - Source code.html

5 - Control Statements in Python

22 - if elif and else

23 - Source code.html

24 - while loop

25 - Source code.html

26 - for loop

27 - Source code.html

28 - Augmented assignments

29 - Source code.html

30 - Sequence iteration

31 - Source code.html

32 - Sentinel iteration

33 - Source code.html

34 - Range function

35 - Source code.html

36 - Decimal type

37 - Source code.html

38 - Logical operators

39 - Source code.html

6 - Functions in Python

40 - Defining functions

41 - Source code.html

42 - Functions with multiple parameters

43 - Source code.html

44 - Random number generation

45 - Source code.html

46 - math Module

47 - Source code.html

48 - Default Argument Value

49 - Source code.html

50 - Keyword Arguments

51 - Source code.html

52 - Arbitrary Parameter List

53 - Source code.html

54 - Methods

55 - Source code.html

56 - Scoping

57 - Source code.html

58 - Import statement

59 - Source code.html

60 - Function arguments

61 - Source code.html

62 - Reproducibility

63 - Source code.html

7 - Sequences in Python Programming Master Lists & Tuples

64 - Intro What we are going to learn in this section of the course

65 - Install CodeRunner Extension in Visual Studio Code

66 - A List of Integer Values & Accessing List Elements With Positive Indices

67 - Source Code.html

68 - Negatives Indices & Math Operations to access elements & Mutable Lists

69 - Source Code.html

70 - Populating list with a range & Concatenation Operator & Boolean Operations

71 - Source Code.html

72 - Tuples

73 - Tuples Source Code.html

74 - Why you should learn about sequence unpacking in Python

75 - Unpacking Tuples Strings & Lists

76 - Unpacking Tuples Strings & Lists Source Code.html

77 - Unpacking Range of Integer Values

78 - Unpacking Range of Integer Values Source Code.html

79 - Use Unpacking to add swapping feature to your app

80 - Use Unpacking to add swapping feature to your app Source Code.html

81 - Unpacking Enumerated Sequences With their Indices & Corresponding Values

82 - Unpacking Enumerated Sequences Source Code.html

83 - Create a primitive bar chart with

84 - Source Code.html

85 - Slice an ordered subset of sequence values

86 - Source Code.html

87 - Slice an intermittent subset of sequence values

88 - Source Code.html

89 - Use negative indices to slice a reversed subset of sequence values

90 - Source Code.html

91 - Count backwards the sequence The HARD way

92 - Source Code.html

93 - Update a subset of sequence values

94 - Source Code.html

95 - Delete a subset of sequence values

96 - Source Code.html

97 - Modify an intermittent subset of sequence values

98 - Source Code.html

99 - Determine the identity of your sequence object after slicing

100 - Source Code.html

101 - Del Statement

102 - Source Code.html

103 - Pass a list object to a function Passing by reference explained

104 - Source Code.html

105 - The Sort Method

106 - Source Code.html

107 - The Sorted Function

108 - Source Code.html

109 - Sequence Searching

110 - Source Code.html

111 - Usages of in and not in keywords when it comes to sequence searching

112 - Source Code.html

113 - Inserting & Appending

114 - Source Code.html

115 - Extend your list

116 - Source Code.html

117 - Remove & Clear List Elements

118 - Source Code.html

119 - Count up the list items and determine the occurrence

120 - Source Code.html

121 - Reverse your list elements

122 - Source Code.html

123 - How to create a shallow list copy of your list elements

124 - Source Code.html

125 - How to create a shallow list copy of your list elements

126 - Source Code.html

127 - Stack Data Structure and the pop function

128 - Source Code.html

129 - Simple List Comprehension Creation

130 - Source Code.html

131 - Complex List Comprehension Creation

132 - Source Code.html

133 - Add decision making to your list comprehension

134 - Source Code.html

135 - Apply List Comprehension other sorts of sequences

136 - Source Code.html

137 - Generator Expression Vs List Comprehension Which one is better

138 - Source Code.html

139 - Generator Expressions

140 - Source Code.html

141 - Functional Programming With Filter

142 - Source Code.html

143 - Use Lambda Expression to Simplify the Process of Filtering

144 - Source Code.html

145 - Functional Programming With Map

146 - Source Code.html

147 - Functional Programming With Reduce

148 - Source Code.html

149 - The ord fucntion Get the numeric value of your sequence

150 - Source Code.html

151 - Sequence processing with min and max

152 - Source Code.html

153 - The Zip Function

154 - Source Code.html

155 - Two Dimensional Arrays

156 - Source Code.html

8 - Dictionaries & Sets in Python

157 - Intro What is dictionary & set

158 - How to create a dictionary in Python

159 - Source Code.html

160 - Iterate through a dictionary

161 - Source Code.html

162 - Access Update and Insert new Entities to your Dictionary

163 - Source Code.html

164 - Remove Entities From your Dictionary

165 - Source Code.html

166 - Get Function

167 - Source Code.html

168 - Keys & Values Methods and Operations

169 - Source Code.html

170 - Dictionary Comparison

171 - Source Code.html

172 - Sets

173 - Source Code.html

174 - Comparing Sets

175 - Source Code.html

176 - Union Function

177 - Source Code.html

178 - Intersection Function

179 - Source Code.html

180 - Difference Function

181 - Source Code.html

182 - Symmetric Difference Function

183 - Source Code.html

184 - IsDisjoint Function

185 - Source Code.html

186 - Update Method

187 - Source Code.html

188 - Add Method

189 - Source Code.html

190 - Remove Method

191 - Source Code.html

9 - Array Oriented Programming With Numpy

192 - Intro

193 - Creating Arrays & Two Dimensional Arrays Using Numpy

194 - Source Code.html

195 - Numpy Array Attributes

196 - Source Code.html

197 - Populate your array with special values

198 - Source Code.html

199 - Create Arrays Using Ranges

200 - Source Code.html

10 - Master Strings in Python

201 - Intro

202 - Presentation Types

203 - Source Code.html

204 - Field Widths & Alignment

205 - Source Code.html

206 - Numeric Formatting

207 - Source Code.html

208 - Strings Format Method

209 - Source Code.html

210 - Concatenating & Repeating Strings

211 - Source Code.html

212 - Stripping Whitespace From Strings

213 - Source Code.html

11 - Files & Exceptions in Python

214 - Intro

215 - Learn about files in Python How Python treats them

216 - How to write to a text file

217 - Source Code.html

218 - How to read data from a text file

219 - Source Code.html

220 - Update your text file

221 - Source Code.html

222 - Exception Handling

223 - Facing Invalid Data or Input

224 - Source Code.html

225 - Try Statement

226 - Source Code.html

227 - Finally Clause

228 - Source Code.html

229 - Extra point Wrap the with statement with try suit

230 - Source Code.html

12 - Object Oriented Programming

231 - Intro

232 - Create your custom class

233 - Source Code.html

234 - Attribute access control

235 - Properties

236 - Source Code.html

237 - Private attribute simulation

238 - Source Code.html

239 - Inheritance

240 - Source Code.html

241 - Polymorphism

242 - Source Code.html

243 - Duck typing

244 - Source Code.html

245 - Object class

246 - Operator overloading

13 - Natural Language Processing NLP

247 - Intro

248 - Get Textblob

249 - Create Textblobg

250 - Source Code.html

251 - Text tokenizing

252 - Source Code.html

253 - Parts of speech tagging

254 - Source Code.html

255 - Noun phrase extraction

256 - Source Code.html

257 - Textblobs default sentiment analyzer

258 - Source Code.html

259 - NaiveBayesAnalyzer

260 - Source Code.html

261 - Language detection and translation

262 - Source Code.html

263 - Pluralization & Singularization

264 - Source Code.html

265 - Spell checking & Correction

266 - Source Code.html

14 - Twitter Data Mining

267 - Intro

268 - Create your twitter developer account

269 - Get yourself comfortable with reading Twitter API docs

270 - Create your first twitter app project and access the private credentials

271 - Install the tweepy module on your system

272 - Authenticate with twitter

273 - Source Code.html

274 - Access information of a twitter account

275 - Source Code.html

276 - Access users followers and friends by using cursor object

277 - Source Code.html

278 - Find out who the users followers are

279 - Source Code.html

280 - Find out who the users followings are

281 - Source Code.html

282 - Get users latest tweets

283 - Source Code.html

284 - Search the recent tweets

285 - Source Code.html

15 - IBM Watson & Cognitive Computing

286 - Intro

287 - IBM Watson explained

288 - Create an IBM cloud account

289 - Install the necessary components

290 - Translator app demo

291 - Translator app to do list

292 - Register for the speech to text service

293 - Register for the text to speech service

294 - Register for the language translator service

295 - Import Watson SDK classes and media modules

296 - Source code.html

297 - Translate function & entry point

298 - Source Code.html

299 - Record users voice function

300 - Source code.html

301 - Step 1 Record english audio

302 - Source code.html

303 - Speech to text function

304 - Source code.html

305 - Step 2 Transcribe english speech to english text

306 - Source code.html

307 - Translate function

308 - Source code.html

309 - Step 3 Translate the english text into french text

310 - Source code.html

311 - Text to speech function

312 - Source code.html

313 - Step 4 Convert the french text into spoken french audio

314 - Source code.html

315 - Play function

316 - Source code.html

317 - Step 5 Play french audio

318 - Source code.html

319 - Step 6 Record french audio

320 - Source code.html

321 - Step 7 Transcribe the french speech to french text

322 - Source code.html

323 - Step 8 Translate the french text into english text

324 - Source code.html

325 - Step 9 Convert the english text into spoken english audio

326 - Source code.html

327 - Step 10 Play english audio & finishing touches

328 - Source code.html

329 - Project source code.html

16 - Machine learning in Python

330 - Intro

331 - Machine Learning Types

332 - Classification model

333 - ScikitLearn library

334 - Datasets

335 - Digits dataset

336 - KNearest Neighbors Algorithm

337 - Hyperparameters

338 - Loading the digits dataset

339 - Source code.html

340 - Target & Data attributes

341 - Source code.html

342 - Set up data

343 - Source code.html

344 - Create a diagram

345 - Source code.html

346 - Display digit images

347 - Source code.html

348 - Splitting data for training and testing purposes

349 - Source code.html

350 - Training & Testing size customization

351 - Source code.html

352 - Create the Model

353 - Source code.html

354 - Train the Model

355 - Source code.html

356 - Predict data & Test your model

357 - Source code.html

358 - Final source code.html

17 - Deep learning in Python

359 - Introduction

360 - Deep learning models

361 - Neural networks

362 - Artificial neurons

363 - Artificial Neural Network Diagram

364 - Iterative learning process

365 - How synapses are activated

366 - Backpropagation technique

367 - Tensors

368 - Convnets

369 - MNIST digits dataset

370 - Probabilistic classification

371 - Keras reproducibility

372 - Keras neural network components

373 - Loading MNIST Dataset

374 - Source code.html

375 - Explore MNIST Data

376 - Source code.html

377 - Digits visualization

378 - Source code.html

379 - Data preparation process Reshaping

380 - Source code.html

381 - Data preparation Normalization

382 - Source code.html

383 - Data preparation Converting labels to categorical data

384 - Source code.html

385 - Neural Network Creation

386 - Source code.html

387 - Integrating layers into the network

388 - Source code.html

389 - The Convolution Process

390 - Add Conv2D Layer

391 - Source code.html

392 - Conv2D Output Dimensionality

393 - Overfitting

394 - Add a Pooling Layer

395 - Source code.html

396 - Add One More Convolution Layer

397 - Source code.html

398 - Add one more pooling layer

399 - Source code.html

400 - Add Flatten Layer

401 - Source code.html

402 - Add a Dense Layer to reduce the features

403 - Source code.html

404 - Add a Dense Layer to produce the final results

405 - Source code.html

406 - Models Summary

407 - Source code.html

408 - Model Structure Visualization

409 - Source code.html

410 - Compile the model

411 - Source code.html

412 - Train the model

413 - Source code.html

414 - Evaluate the model

415 - Source code.html

416 - Predict data

417 - Source code.html

418 - Display the incorrect predictions

419 - Source code.html

420 - Visualize the incorrect predictions

421 - Source code.html

422 - Access the wrong predictions probabilities

423 - Source code.html

424 - Saving & Loading our model

425 - Source code.html

18 - Big Data

426 - Databases

427 - Relational databases

428 - Create a sqlite database

429 - Source code.html

430 - Create a table

431 - Source code.html

432 - Create a list of martial arts

433 - Source code.html

434 - Insert data into the database

435 - Source code.html

436 - Access the database data

437 - Source code.html

438 - Update the database data

439 - Source code.html

440 - Delete the database data

441 - Source code.html

19 - Data Science

442 - Intro to datascience

443 - Descriptive statistics

444 - Source code.html

445 - Measures of central tendency

446 - Mean

447 - Source code.html

448 - Median

449 - Source code.html

450 - Mode

451 - Source code.html

452 - Measures of Dispersion

453 - Variance

454 - Source code.html

455 - Standard deviation

456 - Source code.html

457 - Static visualization

458 - Import the necessary modules

459 - Source code.html

460 - Roll the dice

461 - Source code.html

462 - Set the title and style of your visualization

463 - Source code.html

464 - Start the visualization

465 - Source code.html

466 - Setting up title for each bar

467 - Source code.html

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