John Paul Mueller - Machine Learning For Dummies

54 53

55 54

56 55

57 56

58 57

59 58

60 59

61 60

62 61

63 62

64 63

65 64

66 65

67 66

68 67

69 68

70 69

71 70

72 71

73 72

74 73

75 74

76 75

77 76

78 77

79 78

80 79

81 80

82 81

83 82

84 83

85 84

86 85

87 86

88 87

89 88

90 89

91 90

92 91

93 92

94 93

95 94

96 95

97 96

98 97

99 98

100 99

101 100

102 101

103 102

104 103

105 104

106 105

107 106

108 107

109 108

110 109

111 110

112 111

113 112

114 113

115 115

116 116

117 117

118 118

119 119

120 120

121 121

122 122

123 123

124 124

125 125

126 126

127 127

128 128

129 129

130 130

131 131

132 132

133 133

134 134

135 135

136 136

137 137

138 138

139 139

140 140

141 141

142 142

143 143

144 144

145 145

146 146

147 147

148 148

149 149

150 150

151 151

152 153

153 154

154 155

155 156

156 157

157 158

158 159

159 160

160 161

161 162

162 163

163 164

164 165

165 166

166 167

167 168

168 169

169 170

170 171

171 172

172 173

173 174

174 175

175 176

176 177

177 178

178 179

179 180

180 181

181 182

182 183

183 184

184 185

185 186

186 187

187 188

188 189

189 190

190 191

191 192

192 193

193 194

194 195

195 196

196 197

197 198

198 199

199 200

200 201

201 202

202 203

203 204

204 205

205 206

206 207

207 208

208 209

209 210

210 211

211 212

212 213

213 214

214 215

215 216

216 217

217 218

218 219

219 221

220 222

221 223

222 224

223 225

224 226

225 227

226 228

227 229

228 230

229 231

230 232

231 233

232 234

233 235

234 236

235 237

236 238

237 239

238 240

239 241

240 242

241 243

242 244

243 245

244 246

245 247

246 248

247 249

248 250

249 251

250 252

251 253

252 254

253 255

254 256

255 257

256 258

257 259

258 260

259 261

260 262

261 263

262 264

263 265

264 266

265 267

266 268

267 269

268 271

269 272

270 273

271 274

272 275

273 276

274 277

275 278

276 279

277 280

278 281

279 282

280 283

281 284

282 285

283 286

284 287

285 288

286 289

287 290

288 291

289 292

290 293

291 294

292 295

293 296

294 297

295 298

296 299

297 300

298 301

299 302

300 303

301 304

302 305

303 306

304 307

305 308

306 309

307 310

308 311

309 312

310 313

311 314

312 315

313 316

314 317

315 318

316 319

317 320

318 321

319 322

320 323

321 324

322 325

323 326

324 327

325 328

326 329

327 330

328 331

329 332

330 333

331 334

332 335

333 336

334 337

335 338

336 339

337 340

338 341

339 342

340 343

341 344

342 345

343 346

344 347

345 348

346 349

347 350

348 351

349 352

350 353

351 354

352 355

353 356

354 357

355 358

356 359

357 360

358 361

359 362

360 363

361 364

362 365

363 366

364 367

365 368

366 369

367 370

368 371

369 372

370 373

371 374

372 375

373 376

374 377

375 378

376 379

377 380

378 381

379 383

380 384

381 385

382 386

383 387

384 388

385 389

386 390

387 391

388 392

389 393

390 394

391 395

392 396

393 397

394 398

395 399

396 400

397 401

398 402

399 403

400 405

401 406

402 407

403 408

404 409

405 410

406 411

407 412

408 413

409 414

410 415

411 416

412 417

413 418

414 419

415 420

416 421

417 422

418 423

419 424

420 425

421 426

422 427

423 428

424 429

425 430

426 431

427 432

428 433

429 434

430 435

431 436

432 437

433 438

434 439

435 440

436 441

437 442

438 443

439 444

440 445

441 446

442 447

443 448

The term machine learning has all sorts of meanings attached to it today, especially after Hollywood (and other movie studios) have gotten into the picture. Films such as Ex Machina have tantalized the imaginations of moviegoers the world over and made machine learning into all sorts of things that it really isn’t. Of course, most of us have to live in the real world, where machine learning actually does perform an incredible array of tasks that have nothing to do with androids that can pass the Turing Test (fooling their makers into believing they’re human). Machine Learning For Dummies, 2 ndEdition gives you a view of machine learning in the real world and exposes you to the amazing feats you really can perform using this technology.

Even though the tasks that you perform using machine learning may seem a bit mundane when compared to the movie version, by the time you finish this book, you realize that these mundane tasks have the power to impact the lives of everyone on the planet in nearly every aspect of their daily lives. In short, machine learning is an incredible technology — just not in the way that some people have imagined.

This second edition of the book contains a significant number of changes, not the least of which is that it’s using pure Python code for the examples now upon request from our readers. You can still download R versions of every example, which is actually better than before when only some of the examples were available in R. In addition, the book contains new topics, including an entire chapter that discusses machine learning ethics.

Machines and humans learn in entirely different ways, which is why the first part of this book is essential to your understanding of machine learning. Machines perform routine tasks at incredible speeds, but still require humans to do the actual thinking.

The second part of this book is about getting your system set up to use the various Python coding examples. The two setups work for desktop systems using Windows, Mac OS, or Linux, or mobile devices that have access to a Google Colab compatible browser.

If you’re using R, you’ll find a README file in the R download file that contains instructions for configuring your R Anaconda environment.

The third part of the book discusses math basics with regard to machine learning requirements. It prepares you to perform math tasks associated with algorithms used in machine learning to make either predictions or classifications from your data.