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Complete Machine Learning and Data Science Bootcamp 2021 ^

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English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 42h 56m | 19.2 GB

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!

This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, + other top tech companies. You will go from zero to mastery!

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).

This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.

The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don’t worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!

The topics covered in this course are:

  • Data Exploration and Visualizations
  • Neural Networks and Deep Learning
  • Model Evaluation and Analysis
  • Python 3
  • Tensorflow 2.0
  • Numpy
  • Scikit-Learn
  • Data Science and Machine Learning Projects and Workflows
  • Data Visualization in Python with MatPlotLib and Seaborn
  • Transfer Learning
  • Image recognition and classification
  • Train/Test and cross validation
  • Supervised Learning: Classification, Regression and Time Series
  • Decision Trees and Random Forests
  • Ensemble Learning
  • Hyperparameter Tuning
  • Using Pandas Data Frames to solve complex tasks
  • Use Pandas to handle CSV Files
  • Deep Learning / Neural Networks with TensorFlow 2.0 and Keras
  • Using Kaggle and entering Machine Learning competitions
  • How to present your findings and impress your boss
  • How to clean and prepare your data for analysis
  • K Nearest Neighbours
  • Support Vector Machines
  • Regression analysis (Linear Regression/Polynomial Regression)
  • How Hadoop, Apache Spark, Kafka, and Apache Flink are used
  • Setting up your environment with Conda, MiniConda, and Jupyter Notebooks
  • Using GPUs with Google Colab

By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.

Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don’t really explain things well enough for you to go off on your own and solve real life machine learning problems.

Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.

Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.

You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!

What you’ll learn

  • Become a Data Scientist and get hired
  • Master Machine Learning and use it on the job
  • Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
  • Use modern tools that big tech companies like Google, Apple, Amazon and Facebook use
  • Present Data Science projects to management and stakeholders
  • Learn which Machine Learning model to choose for each type of problem
  • Real life case studies and projects to understand how things are done in the real world
  • Learn best practices when it comes to Data Science Workflow
  • Implement Machine Learning algorithms
  • Learn how to program in Python using the latest Python 3
  • How to improve your Machine Learning Models
  • Learn to pre process data, clean data, and analyze large data.
  • Build a portfolio of work to have on your resume
  • Developer Environment setup for Data Science and Machine Learning
  • Supervised and Unsupervised Learning
  • Machine Learning on Time Series data
  • Explore large datasets using data visualization tools like Matplotlib and Seaborn
  • Explore large datasets and wrangle data using Pandas
  • Learn NumPy and how it is used in Machine Learning
  • A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
  • Learn to use the popular library Scikit-learn in your projects
  • Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
  • Learn to perform Classification and Regression modelling
  • Learn how to apply Transfer Learning

Table of Contents

Introduction
1 Course Outline
2 Join Our Online Classroom!
3 Exercise Meet The Community
4 Your First Day

Machine Learning 101
5 What Is Machine Learning
6 AIMachine LearningData Science
7 Exercise Machine Learning Playground
8 How Did We Get Here
9 Exercise YouTube Recommendation Engine
10 Types of Machine Learning
11 Are You Getting It Yet
12 What Is Machine Learning Round 2
13 Section Review

Machine Learning and Data Science Framework
14 Section Overview
15 Introducing Our Framework
16 Step Machine Learning Framework
17 Types of Machine Learning Problems
18 Types of Data
19 Types of Evaluation
20 Features In Data
21 Modelling – Splitting Data
22 Modelling – Picking the Model
23 Modelling – Tuning
24 Modelling – Comparison
25 Overfitting and Underfitting Definitions
26 Experimentation
27 Tools We Will Use
28 Optional Elements of AI

The 2 Paths
29 The 2 Paths
30 Python + Machine Learning Monthly
31 Endorsements On LinkedIN

Data Science Environment Setup
32 Section Overview
33 Introducing Our Tools
34 What is Conda
35 Conda Environments
36 Mac Environment Setup
37 Mac Environment Setup 2
38 Windows Environment Setup
39 Windows Environment Setup 2
40 Linux Environment Setup
41 Sharing your Conda Environment
42 Jupyter Notebook Walkthrough
43 Jupyter Notebook Walkthrough 2
44 Jupyter Notebook Walkthrough 3

Pandas Data Analysis
45 Section Overview
46 Downloading Workbooks and Assignments
47 Pandas Introduction
48 Series, Data Frames and CSVs
49 Data from URLs
50 Describing Data with Pandas
51 Selecting and Viewing Data with Pandas
52 Selecting and Viewing Data with Pandas Part 2
53 Manipulating Data
54 Manipulating Data 2
55 Manipulating Data 3
56 Assignment Pandas Practice
57 How To Download The Course Assignments

NumPy
58 Section Overview
59 NumPy Introduction
60 Quick Note Correction In Next Video
61 NumPy DataTypes and Attributes
62 Creating NumPy Arrays
63 NumPy Random Seed
64 Viewing Arrays and Matrices
65 Manipulating Arrays
66 Manipulating Arrays 2
67 Standard Deviation and Variance
68 Reshape and Transpose
69 Dot Product vs Element Wise
70 Exercise Nut Butter Store Sales
71 Comparison Operators
72 Sorting Arrays
73 Turn Images Into NumPy Arrays
74 Assignment NumPy Practice
75 Optional Extra NumPy resources

Matplotlib Plotting and Data Visualization
76 Section Overview
77 Matplotlib Introduction
78 Importing And Using Matplotlib
79 Anatomy Of A Matplotlib Figure
80 Scatter Plot And Bar Plot
81 Histograms And Subplots
82 Subplots Option 2
83 Quick Tip Data Visualizations
84 Plotting From Pandas DataFrames
85 Quick Note Regular Expressions
86 Plotting From Pandas DataFrames 2
87 Plotting from Pandas DataFrames 3
88 Plotting from Pandas DataFrames 4
89 Plotting from Pandas DataFrames 5
90 Plotting from Pandas DataFrames 6
91 Plotting from Pandas DataFrames 7
92 Customizing Your Plots
93 Customizing Your Plots 2
94 Saving And Sharing Your Plots
95 Assignment Matplotlib Practice

Scikit-learn Creating Machine Learning Models
96 Section Overview
97 Scikit-learn Introduction
98 Quick Note Upcoming Video
99 Refresher What Is Machine Learning
100 Quick Note Upcoming Videos
101 Scikit-learn Cheatsheet
102 Typical scikit-learn Workflow
103 Optional Debugging Warnings In Jupyter
104 Getting Your Data Ready Splitting Your Data
105 Quick Tip Clean, Transform, Reduce
106 Getting Your Data Ready Convert Data To Numbers
107 Getting Your Data Ready Handling Missing Values With Pandas
108 Extension Feature Scaling
109 Note Correction in the upcoming video (splitting data)
110 Getting Your Data Ready Handling Missing Values With Scikit-learn
111 Choosing The Right Model For Your Data
112 Choosing The Right Model For Your Data 2 (Regression)
113 Quick Note Decision Trees
114 Quick Tip How ML Algorithms Work
115 Choosing The Right Model For Your Data 3 (Classification)
116 Fitting A Model To The Data
117 Making Predictions With Our Model
118 predict() vs predict proba()
119 Making Predictions With Our Model (Regression)
120 Evaluating A Machine Learning Model (Score)
121 Evaluating A Machine Learning Model 2 (Cross Validation)
122 Evaluating A Classification Model 1 (Accuracy)
123 Evaluating A Classification Model 2 (ROC Curve)
124 Evaluating A Classification Model 3 (ROC Curve)
125 Reading Extension ROC Curve + AUC
126 Evaluating A Classification Model 4 (Confusion Matrix)
127 Evaluating A Classification Model 5 (Confusion Matrix)
128 Evaluating A Classification Model 6 (Classification Report)
129 Evaluating A Regression Model 1 (R2 Score)
130 Evaluating A Regression Model 2 (MAE)
131 Evaluating A Regression Model 3 (MSE)
132 Machine Learning Model Evaluation
133 Evaluating A Model With Cross Validation and Scoring Parameter
134 Evaluating A Model With Scikit-learn Functions
135 Improving A Machine Learning Model
136 Tuning Hyperparameters
137 Tuning Hyperparameters 2
138 Tuning Hyperparameters 3
139 Note Metric Comparison Improvement
140 Quick Tip Correlation Analysis
141 Saving And Loading A Model
142 Saving And Loading A Model 2
143 Putting It All Together
144 Putting It All Together 2
145 Scikit-Learn Practice

Supervised Learning Classification + Regression
146 Milestone Projects!

Milestone Project 1 Supervised Learning (Classification)
147 Section Overview
148 Project Overview
149 Project Environment Setup
150 Optional Windows Project Environment Setup
151 Step 1~4 Framework Setup
152 Getting Our Tools Ready
153 Exploring Our Data
154 Finding Patterns
155 Finding Patterns 2
156 Finding Patterns 3
157 Preparing Our Data For Machine Learning
158 Choosing The Right Models
159 Experimenting With Machine Learning Models
160 TuningImproving Our Model
161 Tuning Hyperparameters
162 Tuning Hyperparameters 2
163 Tuning Hyperparameters 3
164 Quick Note Confusion Matrix Labels
165 Evaluating Our Model
166 Evaluating Our Model 2
167 Evaluating Our Model 3
168 Finding The Most Important Features
169 Reviewing The Project

Milestone Project 2 Supervised Learning (Time Series Data)
170 Section Overview
171 Project Overview
172 Project Environment Setup
173 Step 1~4 Framework Setup
174 Downloading the data for the next two projects
175 Exploring Our Data
176 Exploring Our Data 2
177 Feature Engineering
178 Turning Data Into Numbers
179 Filling Missing Numerical Values
180 Filling Missing Categorical Values
181 Fitting A Machine Learning Model
182 Splitting Data
183 Challenge What’s wrong with splitting data after filling it
184 Custom Evaluation Function
185 Reducing Data
186 RandomizedSearchCV
187 Improving Hyperparameters
188 Preproccessing Our Data
189 Making Predictions
190 Feature Importance

Data Engineering
191 Data Engineering Introduction
192 What Is Data
193 What Is A Data Engineer
194 What Is A Data Engineer 2
195 What Is A Data Engineer 3
196 What Is A Data Engineer 4
197 Types Of Databases
198 Quick Note Upcoming Video
199 Optional OLTP Databases
200 Optional Learn SQL
201 Hadoop, HDFS and MapReduce
202 Apache Spark and Apache Flink
203 Kafka and Stream Processing

Neural Networks Deep Learning, Transfer Learning and TensorFlow 2
204 Section Overview
205 Deep Learning and Unstructured Data
206 Setting Up With Google
207 Setting Up Google Colab
208 Google Colab Workspace
209 Uploading Project Data
210 Setting Up Our Data
211 Setting Up Our Data 2
212 Importing TensorFlow 2
213 Optional TensorFlow 2.0 Default Issue
214 Using A GPU
215 Optional GPU and Google Colab
216 Optional Reloading Colab Notebook
217 Loading Our Data Labels
218 Preparing The Images
219 Turning Data Labels Into Numbers
220 Creating Our Own Validation Set
221 Preprocess Images
222 Preprocess Images 2
223 Turning Data Into Batches
224 Turning Data Into Batches 2
225 Visualizing Our Data
226 Preparing Our Inputs and Outputs
227 Optional How machines learn and what’s going on behind the scenes
228 Building A Deep Learning Model
229 Building A Deep Learning Model 2
230 Building A Deep Learning Model 3
231 Building A Deep Learning Model 4
232 Summarizing Our Model
233 Evaluating Our Model
234 Preventing Overfitting
235 Training Your Deep Neural Network
236 Evaluating Performance With TensorBoard
237 Make And Transform Predictions
238 Transform Predictions To Text
239 Visualizing Model Predictions
240 Visualizing And Evaluate Model Predictions 2
241 Visualizing And Evaluate Model Predictions 3
242 Saving And Loading A Trained Model
243 Training Model On Full Dataset
244 Making Predictions On Test Images
245 Submitting Model to Kaggle
246 Making Predictions On Our Images
247 Finishing Dog Vision Where to next

Storytelling + Communication How To Present Your Work
248 Section Overview
249 Communicating Your Work
250 Communicating With Managers
251 Communicating With Co-Workers
252 Weekend Project Principle
253 Communicating With Outside World
254 Storytelling
255 Communicating and sharing your work Further reading

Career Advice + Extra Bits
256 Endorsements On LinkedIn
257 Quick Note Upcoming Video
258 What If I Don’t Have Enough Experience
259 Learning Guideline
260 Quick Note Upcoming Videos
261 JTS Learn to Learn
262 JTS Start With Why
263 Quick Note Upcoming Videos
264 CWD Git + Github
265 CWD Git + Github 2
266 Contributing To Open Source
267 Contributing To Open Source 2
268 Coding Challenges
269 Exercise Contribute To Open Source

Learn Python
270 What Is A Programming Language
271 Python Interpreter
272 How To Run Python Code
273 Our First Python Program
274 Latest Version Of Python
275 Python 2 vs Python 3
276 Exercise How Does Python Work
277 Learning Python
278 Python Data Types
279 How To Succeed
280 Numbers
281 Math Functions
282 DEVELOPER FUNDAMENTALS I
283 Operator Precedence
284 Exercise Operator Precedence
285 Optional bin() and complex
286 Variables
287 Expressions vs Statements
288 Augmented Assignment Operator
289 Strings
290 String Concatenation
291 Type Conversion
292 Escape Sequences
293 Formatted Strings
294 String Indexes
295 Immutability
296 Built-In Functions + Methods
297 Booleans
298 Exercise Type Conversion
299 DEVELOPER FUNDAMENTALS II
300 Exercise Password Checker
301 Lists
302 List Slicing
303 Matrix
304 List Methods
305 List Methods 2
306 List Methods 3
307 Common List Patterns
308 List Unpacking
309 None
310 Dictionaries
311 DEVELOPER FUNDAMENTALS III
312 Dictionary Keys
313 Dictionary Methods
314 Dictionary Methods 2
315 Tuples
316 Tuples 2
317 Sets
318 Sets 2

Learn Python Part 2
319 Breaking The Flow
320 Conditional Logic
321 Indentation In Python
322 Truthy vs Falsey
323 Ternary Operator
324 Short Circuiting
325 Logical Operators
326 Exercise Logical Operators
327 is vs ==
328 For Loops
329 Iterables
330 Exercise Tricky Counter
331 range()
332 enumerate()
333 While Loops
334 While Loops 2
335 break, continue, pass
336 Our First GUI
337 DEVELOPER FUNDAMENTALS IV
338 Exercise Find Duplicates
339 Functions
340 Parameters and Arguments
341 Default Parameters and Keyword Arguments
342 return
343 Exercise Tesla
344 Methods vs Functions
345 Docstrings
346 Clean Code
347 args and kwargs
348 Exercise Functions
349 Scope
350 Scope Rules
351 global Keyword
352 nonlocal Keyword
353 Why Do We Need Scope
354 Pure Functions
355 map()
356 filter()
357 zip()
358 reduce()
359 List Comprehensions
360 Set Comprehensions
361 Exercise Comprehensions
362 Python Exam Testing Your Understanding
363 Modules in Python
364 Quick Note Upcoming Videos
365 Optional PyCharm
366 Packages in Python
367 Different Ways To Import
368 Next Steps
369 Bonus Resource Python Cheatsheet

Bonus Learn Advanced Statistics and Mathematics for FREE!
370 Statistics and Mathematics

Where To Go From Here
371 Become An Alumni
372 Thank You

BONUS SECTION
373 Bonus Lecture

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