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Complete Machine Learning and Data Science: Zero to Mastery

43:22:23 Inglés Premium 22/11/2023 324 videos

Descripción del curso

This is a brand new Machine Learning and Data Science course just launched January 2020 and updated this month with the latest trends and skills! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 270,000+ 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.
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 This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore.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! Taught By: Andrei Neagoie is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life.  Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time.   Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities.  Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way.  Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible.   See you inside the course!
Requirements:
  • No prior experience is needed (not even Math and Statistics). We start from the very basics.
  • A computer (Linux/Windows/Mac) with internet connection.
  • Two paths for those that know programming and those that don't.
  • All tools used in this course are free for you to use.
Who this course is for:
  • Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science and Python
  • You are a programmer that wants to extend their skills into Data Science and Machine Learning to make yourself more valuable
  • Anyone who wants to learn these topics from industry experts that don’t only teach, but have actually worked in the field
  • You’re looking for one single course to teach you about Machine learning and Data Science and get you caught up to speed with the industry
  • You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it”
  • You want to learn to use Deep learning and Neural Networks with your projects
  • You want to add value to your own business or company you work for, by using powerful Machine Learning tools.
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

Curriculum

Section 1: Module 1

  • 02 - Course Outline 06:00
  • 03 - Join Our Online Classroom! 04:02
  • 04 - Your First Day 03:49
  • 05 - What Is Machine Learning? 06:53
  • 06 - AI/Machine Learning/Data Science 04:52
  • 07 - ZTM Resources 04:24
  • 08 - Exercise: Machine Learning Playground 06:17
  • 09 - How Did We Get Here? 06:04
  • 10 - Exercise: YouTube Recommendation Engine 04:25
  • 11 - Types of Machine Learning 04:42
  • 12 - What Is Machine Learning? Round 2 04:46
  • 13 - Section Review 01:49
  • 14 - Section Overview 03:09
  • 15 - Introducing Our Framework 02:39
  • 16 - 6 Step Machine Learning Framework 05:00
  • 17 - Types of Machine Learning Problems 10:33
  • 18 - Types of Data 04:52
  • 19 - Types of Evaluation 03:32
  • 20 - Features In Data 05:23
  • 21 - Modelling - Splitting Data 05:59
  • 22 - Modelling - Picking the Model 04:36
  • 23 - Modelling - Tuning 03:18
  • 24 - Modelling - Comparison 09:33
  • 25 - Experimentation 03:36
  • 26 - Tools We Will Use 04:01
  • 27 - The 2 Paths 03:28
  • 28 - Section Overview 01:10
  • 29 - Introducing Our Tools 03:29
  • 30 - What is Conda? 02:36
  • 31 - Conda Environments 04:31
  • 32 - Mac Environment Setup 17:27
  • 33 - Mac Environment Setup 2 14:12
  • 34 - Windows Environment Setup 05:18
  • 35 - Windows Environment Setup 2 23:18
  • 36 - Jupyter Notebook Walkthrough 10:21
  • 37 - Jupyter Notebook Walkthrough 2 16:19
  • 38 - Jupyter Notebook Walkthrough 3 08:11
  • 39 - Section Overview 02:28
  • 40 - Pandas Introduction 04:30
  • 41 - Series, Data Frames and CSVs 13:22
  • 42 - Describing Data with Pandas 09:49
  • 43 - Selecting and Viewing Data with Pandas 11:09
  • 44 - Selecting and Viewing Data with Pandas Part 2 13:08
  • 45 - Manipulating Data 13:57
  • 46 - Manipulating Data 2 09:58
  • 47 - Manipulating Data 3 10:13
  • 48 - How To Download The Course Assignments 07:44
  • 49 - Section Overview 02:41
  • 50 - NumPy Introduction 05:18
  • 51 - NumPy DataTypes and Attributes 14:06
  • 52 - Creating NumPy Arrays 09:23
  • 53 - NumPy Random Seed 07:18
  • 54 - Viewing Arrays and Matrices 09:36
  • 55 - Manipulating Arrays 11:33
  • 56 - Manipulating Arrays 2 09:45
  • 57 - Standard Deviation and Variance 07:11
  • 58 - Reshape and Transpose 07:27
  • 59 - Dot Product vs Element Wise 11:46
  • 60 - Exercise: Nut Butter Store Sales 13:05
  • 61 - Comparison Operators 03:34
  • 62 - Sorting Arrays 06:20
  • 63 - Turn Images Into NumPy Arrays 07:38
  • 64 - Exercise: Imposter Syndrome 02:57
  • 65 - Section Overview 01:51
  • 66 - Matplotlib Introduction 05:17
  • 67 - Importing And Using Matplotlib 11:37
  • 68 - Anatomy Of A Matplotlib Figure 09:20
  • 69 - Scatter Plot And Bar Plot 10:10
  • 70 - Histograms And Subplots 08:41
  • 71 - Subplots Option 2 04:16
  • 72 - Quick Tip: Data Visualizations 01:49
  • 73 - Plotting From Pandas DataFrames 05:59
  • 74 - Plotting From Pandas DataFrames 2 10:34
  • 75 - Plotting from Pandas DataFrames 3 08:33
  • 76 - Plotting from Pandas DataFrames 4 06:37
  • 77 - Plotting from Pandas DataFrames 5 08:30
  • 78 - Plotting from Pandas DataFrames 6 08:29
  • 79 - Plotting from Pandas DataFrames 7 11:21
  • 80 - Customizing Your Plots 10:10
  • 81 - Customizing Your Plots 2 09:42
  • 82 - Saving And Sharing Your Plots 04:15
  • 83 - Section Overview 02:30
  • 84 - Scikit-learn Introduction 06:42
  • 85 - Refresher: What Is Machine Learning? 05:41
  • 86 - Scikit-learn Cheatsheet 06:14
  • 87 - Typical scikit-learn Workflow 23:15
  • 88 - Optional: Debugging Warnings In Jupyter 18:58
  • 89 - Getting Your Data Ready: Splitting Your Data 08:38
  • 90 - Quick Tip: Clean, Transform, Reduce 05:04
  • 91 - Getting Your Data Ready: Convert Data To Numbers 16:55
  • 92 - Getting Your Data Ready: Handling Missing Values With Pandas 12:23
  • 93 - Getting Your Data Ready: Handling Missing Values With Scikit-learn 17:30
  • 94 - NEW: Choosing The Right Model For Your Data 20:15
  • 95 - NEW: Choosing The Right Model For Your Data 2 (Regression) 11:22
  • 96 - Quick Tip: How ML Algorithms Work 01:26
  • 97 - Choosing The Right Model For Your Data 3 (Classification) 12:46
  • 98 - Fitting A Model To The Data 06:46
  • 99 - Making Predictions With Our Model 08:25
  • 100 - predict() vs predict_proba() 08:34
  • 101 - NEW: Making Predictions With Our Model (Regression) 08:49
  • 102 - NEW: Evaluating A Machine Learning Model (Score) Part 1 09:42
  • 103 - NEW: Evaluating A Machine Learning Model (Score) Part 2 06:48
  • 104 - Evaluating A Machine Learning Model 2 (Cross Validation) 13:17
  • 105 - Evaluating A Classification Model 1 (Accuracy) 04:47
  • 106 - Evaluating A Classification Model 2 (ROC Curve) 09:05
  • 107 - Evaluating A Classification Model 3 (ROC Curve) 07:45
  • 108 - Evaluating A Classification Model 4 (Confusion Matrix) 11:02
  • 109 - NEW: Evaluating A Classification Model 5 (Confusion Matrix) 14:23
  • 110 - Evaluating A Classification Model 6 (Classification Report) 10:17
  • 111 - NEW: Evaluating A Regression Model 1 (R2 Score) 10:00
  • 112 - NEW: Evaluating A Regression Model 2 (MAE) 07:23
  • 113 - NEW: Evaluating A Regression Model 3 (MSE) 09:50
  • 114 - NEW: Evaluating A Model With Cross Validation and Scoring Parameter 25:20
  • 115 - NEW: Evaluating A Model With Scikit-learn Functions 14:03
  • 116 - Improving A Machine Learning Model 11:17
  • 117 - Tuning Hyperparameters 23:16
  • 118 - Tuning Hyperparameters 2 14:24
  • 119 - Tuning Hyperparameters 3 15:00
  • 120 - Quick Tip: Correlation Analysis 02:29
  • 121 - Saving And Loading A Model 07:30
  • 122 - Saving And Loading A Model 2 06:21
  • 123 - Putting It All Together 20:20
  • 124 - Putting It All Together 2 11:35
  • 125 - Section Overview 02:10
  • 126 - Project Overview 06:10
  • 127 - Project Environment Setup 11:00
  • 128 - Optional: Windows Project Environment Setup 04:53
  • 129 - Step 1~4 Framework Setup 12:07
  • 130 - Getting Our Tools Ready 09:05
  • 131 - Exploring Our Data 08:34
  • 132 - Finding Patterns 10:03
  • 133 - Finding Patterns 2 16:48
  • 134 - Finding Patterns 3 13:38
  • 135 - Preparing Our Data For Machine Learning 08:52
  • 136 - Choosing The Right Models 10:16
  • 137 - Experimenting With Machine Learning Models 06:32
  • 138 - Tuning/Improving Our Model 13:50
  • 139 - Tuning Hyperparameters 11:28
  • 140 - Tuning Hyperparameters 2 11:50
  • 141 - Tuning Hyperparameters 3 07:07
  • 142 - Evaluating Our Model 11:00
  • 143 - Evaluating Our Model 2 05:56
  • 144 - Evaluating Our Model 3 08:50
  • 145 - Finding The Most Important Features 16:08
  • 146 - Reviewing The Project 09:14
  • 147 - Section Overview 01:08
  • 148 - Project Overview 04:25
  • 149 - Project Environment Setup 10:53
  • 150 - Step 1~4 Framework Setup 08:37
  • 151 - Exploring Our Data 14:17
  • 152 - Exploring Our Data 2 06:17
  • 153 - Feature Engineering 15:25
  • 154 - Turning Data Into Numbers 15:39
  • 155 - Filling Missing Numerical Values 12:50
  • 156 - Filling Missing Categorical Values 08:28
  • 157 - Fitting A Machine Learning Model 07:17
  • 158 - Splitting Data 10:01
  • 159 - Custom Evaluation Function 11:14
  • 160 - Reducing Data 10:37
  • 161 - RandomizedSearchCV 09:33
  • 162 - Improving Hyperparameters 08:12
  • 163 - Preproccessing Our Data 13:16
  • 164 - Making Predictions 09:18
  • 165 - Feature Importance 13:51
  • 166 - Data Engineering Introduction 03:25
  • 167 - What Is Data? 06:43
  • 168 - What Is A Data Engineer? 04:21
  • 169 - What Is A Data Engineer 2? 05:37
  • 170 - What Is A Data Engineer 3? 05:04
  • 171 - What Is A Data Engineer 4? 03:23
  • 172 - Types Of Databases 06:51
  • 173 - Optional: OLTP Databases 10:55
  • 174 - Hadoop, HDFS and MapReduce 04:23
  • 175 - Apache Spark and Apache Flink 02:08
  • 176 - Kafka and Stream Processing 04:34
  • 177 - Section Overview 02:07
  • 178 - Deep Learning and Unstructured Data 13:37
  • 179 - Setting Up Google Colab 07:18
  • 180 - Google Colab Workspace 04:24
  • 181 - Uploading Project Data 06:53
  • 182 - Setting Up Our Data 04:41
  • 183 - Setting Up Our Data 2 01:33
  • 184 - Importing TensorFlow 2 12:44
  • 185 - Optional: TensorFlow 2.0 Default Issue 03:40
  • 186 - Using A GPU 09:00
  • 187 - Optional: GPU and Google Colab 04:28
  • 188 - Optional: Reloading Colab Notebook 06:50
  • 189 - Loading Our Data Labels 12:05
  • 190 - Preparing The Images 12:33
  • 191 - Turning Data Labels Into Numbers 12:12
  • 192 - Creating Our Own Validation Set 09:19
  • 193 - Preprocess Images 10:26
  • 194 - Preprocess Images 2 11:01
  • 195 - Turning Data Into Batches 09:38
  • 196 - Turning Data Into Batches 2 17:55
  • 197 - Visualizing Our Data 12:42
  • 198 - Preparing Our Inputs and Outputs 06:39
  • 199 - Building A Deep Learning Model 11:43
  • 200 - Building A Deep Learning Model 2 10:54
  • 201 - Building A Deep Learning Model 3 09:06
  • 202 - Building A Deep Learning Model 4 09:13
  • 203 - Summarizing Our Model 04:53
  • 204 - Evaluating Our Model 09:27
  • 205 - Preventing Overfitting 04:21
  • 206 - Training Your Deep Neural Network 19:10
  • 207 - Evaluating Performance With TensorBoard 07:31
  • 208 - Make And Transform Predictions 15:05
  • 209 - Transform Predictions To Text 15:21
  • 210 - Visualizing Model Predictions 14:47
  • 211 - Visualizing And Evaluate Model Predictions 2 15:53
  • 212 - Visualizing And Evaluate Model Predictions 3 10:40
  • 213 - Saving And Loading A Trained Model 13:35
  • 214 - Training Model On Full Dataset 15:03
  • 215 - Making Predictions On Test Images 16:55
  • 216 - Submitting Model to Kaggle 14:15
  • 217 - Making Predictions On Our Images 15:16
  • 218 - Section Overview 02:20
  • 219 - Communicating Your Work 03:23
  • 220 - Communicating With Managers 02:59
  • 221 - Communicating With Co-Workers 03:43
  • 222 - Weekend Project Principle 06:33
  • 223 - Communicating With Outside World 03:30
  • 224 - Storytelling 03:07
  • 225 - What If I Don't Have Enough Experience? 15:04
  • 226 - JTS: Learn to Learn 02:00
  • 227 - JTS: Start With Why 02:44
  • 228 - CWD: Git + Github 17:41
  • 229 - CWD: Git + Github 2 16:53
  • 230 - Contributing To Open Source 14:09
  • 231 - Contributing To Open Source 2 09:41
  • 232 - What Is A Programming Language 06:25
  • 233 - Python Interpreter 07:05
  • 234 - How To Run Python Code 04:54
  • 235 - Latest Version Of Python 01:29
  • 236 - Our First Python Program 07:44
  • 237 - Python 2 vs Python 3 06:41
  • 238 - Exercise: How Does Python Work? 02:10
  • 239 - Learning Python 02:06
  • 240 - Python Data Types 04:47
  • 241 - Numbers 11:10
  • 242 - Math Functions 04:30
  • 243 - DEVELOPER FUNDAMENTALS: I 04:08
  • 244 - Operator Precedence 03:11
  • 245 - Optional: bin() and complex 04:03
  • 246 - Variables 13:13
  • 247 - Expressions vs Statements 01:37
  • 248 - Augmented Assignment Operator 02:50
  • 249 - Strings 05:30
  • 250 - String Concatenation 01:17
  • 251 - Type Conversion 03:04
  • 252 - Escape Sequences 04:24
  • 253 - Formatted Strings 08:25
  • 254 - String Indexes 08:58
  • 255 - Immutability 03:14
  • 256 - Built-In Functions + Methods 10:04
  • 257 - Booleans 03:22
  • 258 - Exercise: Type Conversion 08:23
  • 259 - DEVELOPER FUNDAMENTALS: II 04:43
  • 260 - Exercise: Password Checker 07:22
  • 261 - Lists 05:02
  • 262 - List Slicing 07:49
  • 263 - Matrix 04:12
  • 264 - List Methods 10:29
  • 265 - List Methods 2 04:25
  • 266 - List Methods 3 04:53
  • 267 - Common List Patterns 05:58
  • 268 - List Unpacking 02:42
  • 269 - None 01:52
  • 270 - Dictionaries 06:22
  • 271 - DEVELOPER FUNDAMENTALS: III 02:41
  • 272 - Dictionary Keys 03:38
  • 273 - Dictionary Methods 04:38
  • 274 - Dictionary Methods 2 07:05
  • 275 - Tuples 04:47
  • 276 - Tuples 2 03:15
  • 277 - Sets 07:25
  • 278 - Sets 2 08:46
  • 279 - Breaking The Flow 02:36
  • 280 - Conditional Logic 13:19
  • 281 - Indentation In Python 04:39
  • 282 - Truthy vs Falsey 05:19
  • 283 - Ternary Operator 04:15
  • 284 - Short Circuiting 04:03
  • 285 - Logical Operators 06:57
  • 286 - Exercise: Logical Operators 07:48
  • 287 - is vs == 07:37
  • 288 - For Loops 07:02
  • 289 - Iterables 06:44
  • 290 - Exercise: Tricky Counter 03:24
  • 291 - range() 05:39
  • 292 - enumerate() 04:38
  • 293 - While Loops 06:29
  • 294 - While Loops 2 05:50
  • 295 - break, continue, pass 04:16
  • 296 - Our First GUI 08:49
  • 297 - DEVELOPER FUNDAMENTALS: IV 06:35
  • 298 - Exercise: Find Duplicates 03:55
  • 299 - Functions 07:42
  • 300 - Parameters and Arguments 04:26
  • 301 - Default Parameters and Keyword Arguments 05:41
  • 302 - return 13:12
  • 303 - Methods vs Functions 04:34
  • 304 - Docstrings 03:48
  • 305 - Clean Code 04:39
  • 306 - *args and **kwargs 07:57
  • 307 - Exercise: Functions 04:19
  • 308 - Scope 03:39
  • 309 - Scope Rules 06:56
  • 310 - global Keyword 06:14
  • 311 - nonlocal Keyword 03:22
  • 312 - Why Do We Need Scope? 03:39
  • 313 - Pure Functions 09:24
  • 314 - map() 06:31
  • 315 - filter() 04:24
  • 316 - zip() 03:29
  • 317 - reduce() 07:32
  • 318 - List Comprehensions 08:38
  • 319 - Set Comprehensions 06:27
  • 320 - Exercise: Comprehensions 04:37
  • 321 - Modules in Python 10:55
  • 322 - Optional: PyCharm 08:20
  • 323 - Packages in Python 10:46
  • 324 - Different Ways To Import 07:04
  • 325 - Thank You 02:45

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