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Learn Data Science, Deep Learning, Machine Learning, Natural Language Processing, R and Python Language with libraries. || DATA SCIENCE || Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. What Does a Data Scientist Do? In the past decade, data scientists have become necessary assets and are present in almost all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business. Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms. Glassdoor ranked data scientist as the #1 Best Job in America in 2018 for the third year in a row. 4 As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. The growing demand for data science professionals across industries, big and small, is being challenged by a shortage of qualified candidates available to fill the open positions. The need for data scientists shows no sign of slowing down in the coming years. LinkedIn listed data scientist as one of the most promising jobs in 2017 and 2018, along with multiple data-science-related skills as the most in-demand by companies. Where Do You Fit in Data Science? Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data. Data Scientist Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization. Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning Data Analyst Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders. Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization Data Engineer Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying. Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop) Data Science Career Outlook and Salary Opportunities Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. With over 4,500 open positions listed on Glassdoor, data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world. So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning. Predictive causal analytics – If you want a model which can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not. Prescriptive analytics: If you want a model which has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes. The best example for this is Google’s self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up. Machine learning for making predictions — If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases. Machine learning for pattern discovery — If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering. Let’s say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength. || DEEP LEARNING || Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before. In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers. How does deep learning attain such impressive results? In a word, accuracy. Deep learning achieves recognition accuracy at higher levels than ever before. This helps consumer electronics meet user expectations, and it is crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images. While deep learning was first theorized in the 1980s, there are two main reasons it has only recently become useful: Deep learning requires large amounts of labeled data. For example, driverless car development requires millions of images and thousands of hours of video. Deep learning requires substantial computing power. High-performance GPUs have a parallel architecture that is efficient for deep learning. When combined with clusters or cloud computing, this enables development teams to reduce training time for a deep learning network from weeks to hours or less. Examples of Deep Learning at Work Deep learning applications are used in industries from automated driving to medical devices. Automated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents. Aerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops. Medical Research: Cancer researchers are using deep learning to automatically detect cancer cells. Teams at UCLA built an advanced microscope that yields a high-dimensional data set used to train a deep learning application to accurately identify cancer cells. Industrial Automation: Deep learning is helping to improve worker safety around heavy machinery by automatically detecting when people or objects are within an unsafe distance of machines. Electronics: Deep learning is being used in automated hearing and speech translation. For example, home assistance devices that respond to your voice and know your preferences are powered by deep learning applications. What’s the Difference Between Machine Learning and Deep Learning? Deep learning is a specialized form of machine learning. A machine learning workflow starts with relevant features being manually extracted from images. The features are then used to create a model that categorizes the objects in the image. With a deep learning workflow, relevant features are automatically extracted from images. In addition, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically. Another key difference is deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network. A key advantage of deep learning networks is that they often continue to improve as the size of your data increases. MACHINE LEARNING What is the definition of machine learning? Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm. Machine learning is the process that powers many of the services we use today—recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on. In all of these instances, each platform is collecting as much data about you as possible—what genres you like watching, what links you are clicking, which statuses you are reacting to—and using machine learning to make a highly educated guess about what you might want next. Or, in the case of a voice assistant, about which words match best with the funny sounds coming out of your mouth. WHY IS MACHINE LEARNING SO SUCCESSFUL? While machine learning is not a new technique, interest in the field has exploded in recent years. This resurgence comes on the back of a series of breakthroughs, with deep learning setting new records for accuracy in areas such as speech and language recognition, and computer vision. What’s made these successes possible are primarily two factors, one being the vast quantities of images, speech, video and text that is accessible to researchers looking to train machine-learning systems. But even more important is the availability of vast amounts of parallel-processing power, courtesy of modern graphics processing units (GPUs), which can be linked together into clusters to form machine-learning powerhouses. Today anyone with an internet connection can use these clusters to train machine-learning models, via cloud services provided by firms like Amazon, Google and Microsoft. As the use of machine-learning has taken off, so companies are now creating specialized hardware tailored to running and training machine-learning models. An example of one of these custom chips is Google’s Tensor Processing Unit (TPU), the latest version of which accelerates the rate at which machine-learning models built using Google’s TensorFlow software library can infer information from data, as well as the rate at which they can be trained. These chips are not just used to train models for Google DeepMind and Google Brain, but also the models that underpin Google Translate and the image recognition in Google Photo, as well as services that allow the public to build machine learning models using Google’s TensorFlow Research Cloud. The second generation of these chips was unveiled at Google’s I/O conference in May last year, with an array of these new TPUs able to train a Google machine-learning model used for translation in half the time it would take an array of the top-end GPUs, and the recently announced third-generation TPUs able to accelerate training and inference even further. As hardware becomes increasingly specialized and machine-learning software frameworks are refined, it’s becoming increasingly common for ML tasks to be carried out on consumer-grade phones and computers, rather than in cloud datacenters. In the summer of 2018, Google took a step towards offering the same quality of automated translation on phones that are offline as is available online, by rolling out local neural machine translation for 59 languages to the Google Translate app for iOS and Android. Natural Language Processing Large volumes of textual data Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks. For example, NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important. Today’s machines can analyze more language-based data than humans, without fatigue and in a consistent, unbiased way. Considering the staggering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to fully analyze text and speech data efficiently. Structuring a highly unstructured data source Human language is astoundingly complex and diverse. We express ourselves in infinite ways, both verbally and in writing. Not only are there hundreds of languages and dialects, but within each language is a unique set of grammar and syntax rules, terms and slang. When we write, we often misspell or abbreviate words, or omit punctuation. When we speak, we have regional accents, and we mumble, stutter and borrow terms from other languages. While supervised and unsupervised learning, and specifically deep learning, are now widely used for modeling human language, there’s also a need for syntactic and semantic understanding and domain expertise that are not necessarily present in these machine learning approaches. NLP is important because it helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics. How does NLP work? Breaking down the elemental pieces of language Natural language processing includes many different techniques for interpreting human language, ranging from statistical and machine learning methods to rules-based and algorithmic approaches. We need a broad array of approaches because the text- and voice-based data varies widely, as do the practical applications. Basic NLP tasks include tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detection and identification of semantic relationships. If you ever diagramed sentences in grade school, you’ve done these tasks manually before. In general terms, NLP tasks break down language into shorter, elemental pieces, try to understand relationships between the pieces and explore how the pieces work together to create meaning. These underlying tasks are often used in higher-level NLP capabilities, such as: Content categorization. A linguistic-based document summary, including search and indexing, content alerts and duplication detection. Topic discovery and modeling. Accurately capture the meaning and themes in text collections, and apply advanced analytics to text, like optimization and forecasting. Contextual extraction. Automatically pull structured information from text-based sources. Sentiment analysis. Identifying the mood or subjective opinions within large amounts of text, including average sentiment and opinion mining. Speech-to-text and text-to-speech conversion. Transforming voice commands into written text, and vice versa. Document summarization. Automatically generating synopses of large bodies of text. Machine translation. Automatic translation of text or speech from one language to another. In all these cases, the overarching goal is to take raw language input and use linguistics and algorithms to transform or enrich the text in such a way that it delivers greater value. R Language What is R? R is a programming language developed by Ross Ihaka and Robert Gentleman in 1993. R possesses an extensive catalog of statistical and graphical methods. It includes machine learning algorithms, linear regression, time series, statistical inference to name a few. Most of the R libraries are written in R, but for heavy computational tasks, C, C++ and Fortran codes are preferred. R is not only entrusted by academic, but many large companies also use R programming language, including Uber, Google, Airbnb, Facebook and so on. Data analysis with R is done in a series of steps; programming, transforming, discovering, modeling and communicate the results Program: R is a clear and accessible programming tool Transform: R is made up of a collection of libraries designed specifically for data science Discover: Investigate the data, refine your hypothesis and analyze them Model: R provides a wide array of tools to capture the right model for your data Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world What is R used for? Statistical inference Data analysis Machine learning algorithm R package The primary uses of R is and will always be, statistic, visualization, and machine learning. The picture below shows which R package got the most questions in Stack Overflow. In the top 10, most of them are related to the workflow of a data scientist: data preparation and communicate the results. What you’ll learn- Mathematics and Statistics behind Machine Learning
- Mathematics and Statistics behind Deep Learning
- Mathematics and Statistics behind Artificial Intelligence
- Python Programming Language from Scratch
- Python with it’s Libraries
- Learn Numpy, Pandas, Matplotlib, Scikit-Learn
- Learn Natural Language Processing
- Learn R Language
- Learn Tokenization in Natural Language Processing
- Learn Implementation of R Packages and Libraries on Different Data Sets
- Learn Implementation of Python Libraries on Different Data Sets
- Algorithms and Models of Machine Learning
- Algorithms and Models of Deep Learning
- Learn Data Science
- k-Nearest Neighbors, Naive Bayes etc
- Supervised and Unsupervised Learning
- Clustering
- Different Theorems

**+ Table of Contents**

**Introduction**

1 Introduction

**Installing Software**

2 How to Download Anaconda

3 VERY IMPORTANT NOTE FOR STUDENTS!!

4 How to Download R Studio

**Data Science with Python**

5 Introduction to NumPy

6 Understanding Data Types in Python

7 Fixed Type Arrays in Python

8 Creating Arrays from Scratch

9 The Basics of NumPy Arrays

10 Array Slicing – Accessing Subarrays

11 Reshaping of Arrays

12 Splitting of Arrays

13 Splitting of Arrays – 2

14 Exploring NumPy’s UFuncs

15 Exponents and Logarithms

16 Advanced UFunc Functions

17 Aggregations

18 Example US Presidents

19 Computation on Arrays – Broadcasting

20 Broadcasting Example

21 Broadcasting in Practice

22 Comparisons, Masks and Boolean Logic

23 Boolean Operators

24 Boolean Arrays as Masks

25 Fancy Indexing

26 Combined Indexing

27 Modifying values with Fancy Indexing

28 Example Binning Data

29 Sorting Arrays

30 Fast Sorting in NumPy

31 Example k-Nearest Neighbors

32 Structured Data – NumPy’s Structured Arrays

33 More Advanced Component Types

**Data Manipulation with Pandas**

34 Introduction

35 Installing and Using Pandas

36 Introducing Pandas Objects

37 Constructing Series Objects

38 The Pandas DataFrame Object

39 DataFrame as a specialized Dictionary

40 The Pandas Index Object

41 Data Indexing and Selection

42 Data Selection in DataFrame

43 DataFrame as two-dimensional array

44 Operating on Data in Pandas

45 UFuncs Index Alignment

46 Index alignment in DataFrame

47 Ufuncs Operations Between DataFrame and Series

48 Handling Missing Data

49 Missing Data in Pandas

50 NaN and None in Pandas

51 Operating on Null Values

52 Hierarchical Indexing

53 The better way Pandas MultiIndex

54 Methods of MultiIndex Creation

55 MultiIndex for columns

56 Indexing and Slicing a MultiIndex

57 Rearranging Multi-Indices

58 Index setting and resetting

59 Data Aggregations on Multi-Indices

60 Combining Datasets Concat and Append

61 Duplicate indices

62 Catching the repeats as an error

63 Combining Datasets Merge and Join

64 Specification of the Merge Key

65 Specifying Set Arithmetic for Joins

66 Example US States Data

67 Example US States Data – 2

68 Aggregation and Grouping

69 GroupBy Split, Apply, Combine

70 Iteration over groups

71 Aggregate, filter, transform, apply

72 Transformation

73 Pivot Tables

74 Pivot Table Syntax

75 Example Birthrate Data

76 Vectorized String Operations

77 Methods using regular expressions

78 Working with Time Series

79 Dates and times in Pandas Best of both worlds

80 Pandas Time Series Data Structures

81 Example Visualizing Seattle Bicycle Counts

82 Example Visualizing Seattle Bicycle Counts – 1

83 High-Performance Pandas eval() and query()

84 pandas.eval() for Efficient Operations

85 DataFrame.eval() for Column-Wise Operations

**Data Visualization with Matplotlib**

86 Visualization with Matplotlib

87 Plotting from an IPython Shell

88 Two Interfaces for the Price of one

89 Simple Line Plots

90 Adjusting the Plot – Axes Limits

91 Simple Scatter Plots

92 Scatter Plots with plt.scatter

93 Basic Errorbars

94 Density and Contour Plots

95 Histograms, Binnings, and Density

96 plt.hexbin – Hexagonal binnings

97 Customizing Plot Legends

98 Legend for Size of Points

99 Multiple Legends

100 Customizing Color-bars

101 Color limits and extensions

102 Example Handwritten Digits

103 Multiple Subplots

104 plt.subplots The Whole Grid in One Go

105 Text and Annotation

106 Arrows and Annotation

107 Major and Minor Ticks

108 Reducing or Increasing the Number of Ticks

109 Customizing Matplotlib Configurations and Stylesheets

110 Changing the Defaults rcParams

111 Stylesheets

112 Three-Dimensional Plotting in Matplotlib

113 Wireframes and Surface Plots

114 Example Visualizing a Möbius strip

115 Geographic Data with Basemap

116 Map Projections

117 Seaborn Versus Matplotlib

118 Pair plots

119 Bar Plots

**Machine Learning with Python**

120 Data Wrangling

121 Creating a DataFrame

122 Describing the Data

123 Selecting Rows based on Conditionals

124 Renaming Columns

125 Finding the Minimum, Maximum, Sum, Average and Count

126 Handling Missing Values

127 Deleting a Row & Dropping Duplicate Rows

128 Grouping Values by Variables

129 Looping over a column

130 Concatenating DataFrames

131 Merging DataFrames

**Handling Categorical Data**

132 Encoding Nominal Categorical Data

133 Encoding Ordinal Categorical Features

134 Encoding Dictionaries of Features

135 Imputing Missing Class Values

136 Handling Imbalanced Classes

**Handling Missing Values**

137 Rescaling a Feature

138 Normalizing Observations

**Handling Numerical Data**

139 Transforming Features

140 Detecting Outliers

141 Handling Outliers

142 Discretizating Features

143 Grouping Observations using Clustering

**Loading Data**

144 Loading Data

**Vectors, Matrices and Arrays**

145 Vectors, Matrices and Arrays

146 One or more elements in a matrix

147 Finding Minimum and Maximum Values in an Array and Matrix + Vector

148 Flattening a Matrix

149 Calculating the trace of a Matrix

150 Calculating the Dot Products

**Deep Learning**

151 Introduction

152 MNIST and Softmax Regression

153 Softmax Regression

154 Softmax Regression Code Explanation

155 Computation Graphs

156 Graphs, Sessions, Fetches

157 Constructing and Managing Graph & Fetches

158 Flowing Tensors

159 Data Types, Casting, Tensor Arrays and Shapes

160 Matrix Multiplication

161 Names

162 Variables, Placeholders, and Simple Optimization

163 Placeholders

164 Optimization

165 The Gradient Descent Optimizer

166 Gradient Descent in TensorFlow

167 Example Linear Regression

168 Convolutional Neural Networks

169 Convolution, Pooling, Dropout

170 The Model

171 MNIST Data Set

172 Working with TensorfBoard

173 Introduction to Recurrent Neural Networks

174 MNIST Images as Sequences

175 Input and Label Placeholders

176 The RNN Step

177 Applying the RNN step with tfscan

178 Sequential Outputs

179 TensorFlow Built-in RNN Functions

180 RNN for Text Sequences

181 Text Sequences

182 contrib.learn

**Natural Language Processing**

183 Computing with Language Texts and Words

184 Searching Texts

185 Counting Vocabulary

186 Lexical Diversity

187 Lexical diversity of various genres in the Brown Corpus

188 Lists

189 Indexing Lists

190 Variables

191 Strings

192 Computing with Language Simple Statistics

193 Frequency Distributions

194 Fine-Grained Selection of Words

195 Collocations and Bigrams

196 Counting other Things

197 Making Decisions and Taking Control

198 Operating on Every Element

199 Nested Code Blocks

200 Looping with Conditionals

201 Accessing Text Corposa

202 Web and Chat Texts

203 Brown Corpus

204 Brown Corpus – 2

205 Reuters Corpus

206 Inaugural Address Corpus

207 Conditional Frequency Distribution

208 Plotting and Tabulating Distributions

209 Generating Random Texts with Bigram

210 Function

**Machine Learning with Python Introductory**

211 Essential Libraries and Tools

212 A First Application Classifying Iris Species

213 Measuring Success Training and Testing Data

214 First Things Fast – Look at your Data

215 Building your first model k-Nearest Neighbors

216 Making Predictions

**Supervised Learning with Python**

217 Supervised Learning

218 load breast cancer function from scikit-learn

219 Dataset Analysis

220 Analyzing KNeighborsClassifier

221 Evaluation of Test Performance

222 k-Neighbors Regression

223 Analyzing KNeighborsRegressor

224 Linear Models

225 Linear regression (aka ordinary least squares)

226 Ridge regression

227 Boston Housing dataset and evaluated LinearRegression

228 Lasso

229 Linear Models for Classification

230 Decision boundaries of a linear SVM

231 Coefficients Learned by the models with the three different settings

232 Linear Models for Multiclass Classification

233 Predictions for all regions of the 2D

234 Strengths, weaknesses, and parameter

235 Decision Trees

**R Language**

236 Using R as a Calculator

237 Assignments

238 Vector of Values

239 Indexing of Vectors

240 Vectorized Expressions

241 Comments

242 Functions

243 Writing your own Functions

244 Vectorized Expressions and Functions

245 Control Structures

246 Fucnctor

247 Factors

248 Factors – 2

249 Data Frames

250 Dealing with Missing Values

251 Installing Packages

252 Data Pipelines

253 Writing Pipelines of Functional Calls

254 Writing Functions that work with the Pipelines

255 The Magical . Argument

256 Defining Functions Using

257 Anonymous Functions

258 Data Manipulation

259 Quickly Reviewing the Data

260 Breast Cancer Dataset

261 Breast Cancer Dataset – 2

262 Breast Cancer Dataset – 3

263 Boston Housing Dataset

264 The readr Package

265 Manipulating Data with dplyr

266 Some Useful dplyr Functions

267 Select()

268 Mutate()

269 Transmute()

270 Group by()

271 Tidying Data with tidyr

272 Visualizing Data

273 Longley Data

274 Longley Data and Geom point

275 Grammer of Graphics and ggplo2

276 qplot

277 Using Geometries

278 Making Graphs through ggplot

279 Using ggplot

280 Facets

281 Facet grid

282 Scaling

283 Using Iris Data Set

284 Themes and Other Graphical Transformation

285 Iris Dataset

286 Figures with Multiple Plots

287 Working with Large Dataset

288 Running out of Memory

**Python X**

289 Integer Values

290 Variables and Assignment

291 Identifiers

292 Floating-Point Types

293 Control Codes within Strings

294 User Input

295 The eval Function

296 Controlling the Print Function

**Expressions and Arithmetic**

297 Expressions

298 Operator Precedence and Associativity

299 Comments

300 Errors

301 Syntax Errors

302 Run-Time Errors

303 Logic Errors

304 Arithmetic Examples

305 More Arithmetic Operators

306 Algorithms

**Conditional Execution**

307 Boolean Expressions

308 Boolean Expressions 2.0

309 The Simple if Statement

310 The if-else Statement

311 Compound Boolean Expressions

312 Nested Conditionals

313 Multiway Decision Statements

314 Conditional Expressions

315 Errors in Conditional Statements

**Iteration**

316 The while statement

317 Definite Loops vs Indefinite Loops

318 The for statement

319 Nested Loops

320 Abnormal Loop Termination

321 The break statement

322 The continue statement

323 Infinite Loops

324 Iteration Examples

325 Computing Square root

326 Drawing a Tree

327 Printing Prime Number

328 Insisting on the Proper Input

**Using Functions**

329 Introduction to Using Functions

330 Standard Mathematical Functions

331 time Functions

332 Random Numbers

333 Importing Issues

**Writing Functions**

334 Function Basics

335 Using Functions

336 Main Function

337 Parameter Passing

338 Function examples

339 Better Organized Prime Generator

340 Command Interpreter

341 Restricted Input

342 Better Die Rolling Simulator

343 Tree Drawing Functions

344 Floating Point Equality

345 Custom Functions vs Standard Functions

**More on Functions**

346 Global Variables

347 Default Parameters

348 Recursion

349 Making Functions Reusable

350 Documenting Functions and Modules

351 Functions as Data

**Lists**

352 Lists

353 Using Lists

354 List Assignment and Equivalence

355 List Bounds

356 Slicing

357 Lists and Functions

358 Prime Generation with a List

**List Processing**

359 Sorting

360 Flexible Sorting

361 Search

362 Linear Search

363 Binary Search

364 List Permutations

365 Randomly Permuting a List

366 Reversing a List

**Objects**

367 Using Objects

368 String Objects

**Python DIY**

369 The Background of Software Development

370 Software

371 Development Tools

372 Learning Programming with Python

373 Writing a Python Program

374 A Longer Python program

**Values and Variables**

375 Values and Variables

376 Integer Values

377 Variables and Assignment

378 Identifiers

379 Floating-point Types

380 Control Codes within Strings

381 User Input

382 The Eval Function

383 Controlling the Print Function

**Expressions and Arithmetic**

384 Expressions and Arithmetic

385 Expressions

386 Operator Precedence and Associativity

387 Comments

388 Errors

389 Syntax Errors

390 Run-Time Errors

391 Logic Errors

392 Arithmetic Examples

393 More Arithmetic Operators

394 Algorithms

**Conditional Execution**

395 Conditional Execution

396 Boolean Expressions

397 Boolean Expressions 2.0

398 The Simple if Statement

399 The if-else Statement

400 Compound Boolean Expressions

401 Nested Conditionals

402 Multiway Decision Statements

403 Conditional Expressions

404 Errors in Conditional Statements

**Iteration**

405 Iteration

406 The while statement

407 Definite Loops vs Indefinite Loops

408 The for statement

409 Nested Loops

410 Abnormal Loop Termination

411 The break statement

412 The continue statement

413 Infinite Loops

414 Iteration Examples

415 Computing Square root

416 Drawing a Tree

417 Printing Prime Number

**Using Functions**

418 Using Functions

419 Introduction to Using Functions

420 Standard Mathematical Functions

421 time Functions

422 Random Numbers

423 Introduction to Using Functions

424 Importing Issues

**Writing Functions**

425 Writing Functions

426 Function Basics

427 Using Functions

428 Main Function

429 Parameter Passing

430 Function examples

431 Better Organized Prime Generator

432 Command Interpreter

433 Restricted Input

434 Better Die Rolling Simulator

435 Tree Drawing Functions

436 Floating Point Equality

437 Custom Functions vs Standard Functions

**More on Functions**

438 More on Functions

439 Global Variables

440 Default Parameters

441 Making Functions Reusable

442 Documenting Functions and Modules

443 Functions as Data

**Lists**

444 Lists

445 Using Lists

446 List Assignment and Equivalence

447 List Bounds

448 Slicing

449 Lists and Functions

450 Prime Generation with a List

**List Processing**

451 List Processing

452 Sorting

453 Flexible Sorting

454 Search

455 Linear Search

456 Binary Search

457 List Permutations

458 Randomly Permuting a List

459 Reversing a List

**Objects**

460 Objects

461 Using Objects

462 String Objects

463 List Objects

**Custom Types**

464 Custom Types

465 Geometric Points

466 Methods

467 Custom Type Examples

468 Stopwatch

469 Automated Testing

470 Class Inheritance

**Mathematics and Statistics for Machine Learning**

471 Introduction

472 What is Machine Learning

473 Examples of Machine Learning Applications

474 Learning Association

475 Classification

476 Regression

477 Unsupervised Learning

478 Reinforcement Learning

**Supervised Learning**

479 Supervised Learning

480 Learning a Class from Examples

481 Vapnik-Chervonenkis (VC) Dimension

482 Probably Approximately Correct (PAC) Learning

483 Noise

484 Learning Multiple Classes

485 Regression

486 Model Selection and Generalization

487 Dimensions of a Supervised Machine Learning Algorithm

**Bayesian Decision Theory**

488 Bayesian Decision Theory

489 Introduction

490 Classification

491 Losses and Risks

492 Discriminant Functions

493 Utility Theory

494 Association Rules

**Parametric Methods**

495 Parametric Methods

496 Introduction

497 Maximum Likelihood Estimation

498 Bernoulli Density

499 Multinomial Density

500 Gaussian (Normal) Density

501 Evaluating an Estimator

502 The Bayes’ Estimator

503 Parametric Classification

504 Regression

505 Tuning Model Complexity

506 Model Selection Procedures

**Dimensionality Reduction**

507 Dimensionality Reduction

508 Introduction

509 Subset Selection

510 Principal Components Analysis

511 Factor Analysis

512 Multidimensional Scaling

513 Linear Discriminant Analysis

514 Locally Linear Embedding

**Clustering**

515 Clustering

516 Introduction

517 Mixture Densities

518 k-Means Clustering

519 Expectation-Maximization Algorithm

520 Mixtures of Latent Variable Models

521 Supervised Learning after Clustering

522 Hierarchical Clustering

523 Choosing the Number of Clusters

**Nonparametric Methods**

524 Nonparametric Methods

525 Introduction

526 Nonparametric Density Estimation

527 Histogram Estimator

528 Kernel Estimator

529 k-Nearest Neighbor Estimator

530 Generalization to Multivariate Data

531 Nonparametric Classification

532 Condensed Nearest Neighbor

533 Nonparametric Regression

534 Running Mean Smoother

535 Kernel Smoother

536 Running Line Smoother

537 How to Choose the Smoothing Parameter

**Decision Trees**

538 Decision Trees

539 Introduction

540 Univariate Trees

541 Classification Trees

542 Regression Trees

543 Pruning

544 Rule Extraction from Trees

545 Learning Rules from Data

546 Multivariate Trees

**Linear Discrimination**

547 Linear Discrimination

548 Introduction

549 Generalizing the Linear Model

550 Geometry of the Linear Discriminant

551 Two Classes

552 Multiple Classes

553 Pairwise Separation

554 Parametric Discrimination Revisited

555 Gradient Descent

556 Logistic Discrimination

557 Two Classes

558 Multiple Classes

559 Discrimination by Regression

**Multilayer Perceptrons**

560 Multilayer Perceptrons

561 Introduction

562 Understanding the Brain

563 Neural Networks as a Paradigm for Parallel Processing

564 The Perceptron

565 Training a Perceptron

566 Learning Boolean Functions

567 Multilayer Perceptrons

568 MLP as a Universal Approximator

569 Backpropagation Algorithm

570 Nonlinear Regression

571 Two-Class Discrimination

572 Multiclass Discrimination

573 Multiple Hidden Layers

574 Training Procedures

575 Improving Convergence

576 Over training

577 Structuring the Network

578 Hints

579 Tuning the Network Size

580 Bayesian View of Learning

581 Dimensionality Reduction

582 Learning Time

583 Time Delay Neural Networks

584 Recurrent Networks

**Local Models**

585 Local Models

586 Introduction

587 Competitive Learning

588 Online k-Means

589 Adaptive Resonance Theory

590 Self-Organizing Maps

591 Radial Basis Functions

592 Incorporating Rule-Based Knowledge

593 Normalized Basis Functions

594 Competitive Basis Functions

595 Learning Vector Quantization

596 Cooperative Experts

597 Competitive Experts

**Hidden Markov Models**

598 Hidden Markov Models

599 Introduction

600 Discrete Markov Processes

601 Hidden Markov Models

602 Three Basic Problems of HMMs

603 Evaluation Problem

604 Finding the State Sequence

605 Learning Model Parameters

606 Continuous Observations

607 The HMM with Input

608 Model Selection in HMM

**Combining Multiple Learners**

609 Combining Multiple Learners

610 Rationale

611 Generating Diverse Learners

612 Model Combination Schemes

613 Voting

614 Error-Correcting Output Codes

615 Bagging

616 Boosting

617 Mixture of Experts Revisited

618 Stacked Generalization

619 Fine-Tuning an Ensemble

620 Cascading

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