Section 1 : Introduction
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Lecture 1 | Course Outline copy | 00:05:59 Duration |
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Lecture 2 | Join Our Online Classroom! | |
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Lecture 3 | Exercise Meet The Community | |
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Lecture 4 | Your First Day | 00:03:48 Duration |
Section 2 : Machine Learning 101
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Lecture 1 | What Is Machine Learning | 00:06:52 Duration |
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Lecture 2 | AIMachine LearningData Science | 00:04:51 Duration |
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Lecture 3 | Exercise Machine Learning Playground | 00:06:16 Duration |
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Lecture 4 | How Did We Get Here | 00:06:03 Duration |
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Lecture 5 | Exercise YouTube Recommendation Engine | 00:04:25 Duration |
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Lecture 6 | Types of Machine Learning | 00:04:41 Duration |
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Lecture 7 | Are You Getting It Yet | |
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Lecture 8 | What Is Machine Learning Round 2 | 00:04:45 Duration |
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Lecture 9 | Section Review | 00:01:48 Duration |
Section 3 : Machine Learning and Data Science Framework
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Lecture 1 | Section Overview | 00:03:09 Duration |
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Lecture 2 | Introducing Our Framework | 00:02:38 Duration |
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Lecture 3 | Step Machine Learning Framework | 00:04:59 Duration |
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Lecture 4 | Types of Machine Learning Problems | 00:10:32 Duration |
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Lecture 5 | Types of Data | 00:04:51 Duration |
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Lecture 6 | Types of Evaluation | 00:03:31 Duration |
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Lecture 7 | Features In Data | 00:05:22 Duration |
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Lecture 8 | Modelling - Splitting Data | |
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Lecture 9 | Modelling - Picking the Model | 00:04:35 Duration |
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Lecture 10 | Modelling - Tuning | 00:03:17 Duration |
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Lecture 11 | Modelling - Comparison | 00:09:32 Duration |
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Lecture 12 | Overfitting and Underfitting Definitions | |
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Lecture 13 | Experimentation | 00:03:35 Duration |
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Lecture 14 | Tools We Will Use | 00:04:00 Duration |
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Lecture 15 | Optional Elements of AI |
Section 4 : The 2 Paths
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Lecture 1 | The 2 Paths | 00:03:27 Duration |
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Lecture 2 | Python + Machine Learning Monthly | |
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Lecture 3 | Endorsements On LinkedIN |
Section 5 : Data Science Environment Setup
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Lecture 1 | Section Overview | 00:01:09 Duration |
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Lecture 2 | Introducing Our Tools | 00:03:29 Duration |
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Lecture 3 | What is Conda | 00:02:35 Duration |
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Lecture 4 | Conda Environments | 00:04:30 Duration |
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Lecture 5 | Mac Environment Setup | 00:17:27 Duration |
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Lecture 6 | Mac Environment Setup 2 | 00:14:11 Duration |
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Lecture 7 | Windows Environment Setup | 00:05:17 Duration |
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Lecture 8 | Windows Environment Setup 2 | 00:23:18 Duration |
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Lecture 9 | Linux Environment Setup | |
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Lecture 10 | Sharing your Conda Environment | |
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Lecture 11 | Jupyter Notebook Walkthrough | |
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Lecture 12 | Jupyter Notebook Walkthrough 2 | 00:16:18 Duration |
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Lecture 13 | Jupyter Notebook Walkthrough 3 | 00:08:10 Duration |
Section 6 : Pandas Data Analysis
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Lecture 1 | Section Overview | 00:02:27 Duration |
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Lecture 2 | Downloading Workbooks and Assignments | |
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Lecture 3 | Pandas Introduction | 00:04:29 Duration |
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Lecture 4 | Series, Data Frames and CSVs | 00:13:21 Duration |
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Lecture 5 | Data from URLs | |
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Lecture 6 | Describing Data with Pandas | 00:09:49 Duration |
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Lecture 7 | Selecting and Viewing Data with Pandas | 00:11:08 Duration |
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Lecture 8 | Selecting and Viewing Data with Pandas Part 2 | 00:13:07 Duration |
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Lecture 9 | Manipulating Data | 00:13:57 Duration |
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Lecture 10 | Manipulating Data 2 | 00:09:57 Duration |
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Lecture 11 | Manipulating Data 3 | 00:10:12 Duration |
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Lecture 12 | Assignment Pandas Practice | |
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Lecture 13 | How To Download The Course Assignments | 00:07:43 Duration |
Section 7 : NumPy
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Lecture 1 | Section Overview | 00:02:41 Duration |
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Lecture 2 | NumPy Introduction | 00:05:18 Duration |
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Lecture 3 | Quick Note Correction In Next Video | |
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Lecture 4 | NumPy DataTypes and Attributes | 00:14:06 Duration |
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Lecture 5 | Creating NumPy Arrays | 00:09:22 Duration |
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Lecture 6 | NumPy Random Seed | 00:07:17 Duration |
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Lecture 7 | Viewing Arrays and Matrices | 00:09:35 Duration |
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Lecture 8 | Manipulating Arrays | 00:11:32 Duration |
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Lecture 9 | Manipulating Arrays 2 | 00:09:44 Duration |
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Lecture 10 | Standard Deviation and Variance | 00:07:10 Duration |
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Lecture 11 | Reshape and Transpose | 00:07:27 Duration |
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Lecture 12 | Dot Product vs Element Wise | 00:11:45 Duration |
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Lecture 13 | Exercise Nut Butter Store Sales | 00:13:04 Duration |
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Lecture 14 | Comparison Operators | 00:03:34 Duration |
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Lecture 15 | Sorting Arrays | 00:06:20 Duration |
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Lecture 16 | Turn Images Into NumPy Arrays | 00:07:37 Duration |
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Lecture 17 | Assignment NumPy Practice | |
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Lecture 18 | Optional Extra NumPy resources |
Section 8 : Matplotlib Plotting and Data Visualization
Section 9 : Scikit-learn Creating Machine Learning Models
Section 10 : Supervised Learning Classification + Regression
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Lecture 1 | Milestone Projects! |
Section 11 : Milestone Project 1 Supervised Learning (Classification)
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Lecture 1 | Section Overview | 00:11:34 Duration |
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Lecture 2 | Project Overview | 00:06:10 Duration |
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Lecture 3 | Project Environment Setup | 00:10:59 Duration |
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Lecture 4 | Optional Windows Project Environment Setup | 00:04:52 Duration |
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Lecture 5 | Step 1~4 Framework Setup | 00:12:06 Duration |
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Lecture 6 | Getting Our Tools Ready | 00:09:04 Duration |
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Lecture 7 | Exploring Our Data | 00:08:34 Duration |
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Lecture 8 | Finding Patterns | 00:10:03 Duration |
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Lecture 9 | Finding Patterns 2 | 00:16:48 Duration |
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Lecture 10 | Finding Patterns 3 | 00:13:37 Duration |
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Lecture 11 | Preparing Our Data For Machine Learning | 00:08:52 Duration |
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Lecture 12 | Choosing The Right Models | 00:10:15 Duration |
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Lecture 13 | Experimenting With Machine Learning Models | 00:06:32 Duration |
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Lecture 14 | TuningImproving Our Model | 00:13:49 Duration |
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Lecture 15 | Tuning Hyperparameters | 00:11:28 Duration |
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Lecture 16 | Tuning Hyperparameters 2 | 00:11:50 Duration |
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Lecture 17 | Tuning Hyperparameters 3 | 00:07:07 Duration |
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Lecture 18 | Quick Note Confusion Matrix Labels | |
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Lecture 19 | Evaluating Our Model | 00:11:01 Duration |
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Lecture 20 | Evaluating Our Model 2 | 00:05:56 Duration |
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Lecture 21 | Evaluating Our Model 3 | 00:08:50 Duration |
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Lecture 22 | Finding The Most Important Features | 00:16:07 Duration |
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Lecture 23 | Reviewing The Project | 00:09:13 Duration |
Section 12 : Milestone Project 2 Supervised Learning (Time Series Data)
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Lecture 1 | Section Overview | 00:01:07 Duration |
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Lecture 2 | Project Overview | 00:04:24 Duration |
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Lecture 3 | Project Environment Setup | 00:10:52 Duration |
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Lecture 4 | Step 1~4 Framework Setup | 00:08:36 Duration |
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Lecture 5 | Downloading the data for the next two projects | |
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Lecture 6 | Exploring Our Data | 00:14:16 Duration |
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Lecture 7 | Exploring Our Data 2 | 00:06:17 Duration |
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Lecture 8 | Feature Engineering | 00:15:24 Duration |
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Lecture 9 | Turning Data Into Numbers | 00:15:38 Duration |
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Lecture 10 | Filling Missing Numerical Values | 00:12:49 Duration |
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Lecture 11 | Filling Missing Categorical Values | 00:08:27 Duration |
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Lecture 12 | Fitting A Machine Learning Model | 00:07:16 Duration |
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Lecture 13 | Splitting Data | 00:10:01 Duration |
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Lecture 14 | Challenge What's wrong with splitting data after filling it | |
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Lecture 15 | Custom Evaluation Function | 00:11:13 Duration |
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Lecture 16 | Reducing Data | 00:10:36 Duration |
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Lecture 17 | RandomizedSearchCV | 00:09:32 Duration |
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Lecture 18 | Improving Hyperparameters | 00:08:11 Duration |
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Lecture 19 | Preproccessing Our Data | 00:13:16 Duration |
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Lecture 20 | Making Predictions | 00:09:18 Duration |
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Lecture 21 | Feature Importance | 00:13:50 Duration |
Section 13 : Data Engineering
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Lecture 1 | Data Engineering Introduction | 00:03:24 Duration |
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Lecture 2 | Optional OLTP Databases | 00:10:54 Duration |
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Lecture 3 | What Is Data | 00:06:42 Duration |
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Lecture 4 | What Is A Data Engineer | 00:04:21 Duration |
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Lecture 5 | What Is A Data Engineer 2 | 00:05:36 Duration |
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Lecture 6 | What Is A Data Engineer 3 | 00:05:04 Duration |
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Lecture 7 | What Is A Data Engineer 4 | 00:03:23 Duration |
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Lecture 8 | Types Of Databases | 00:06:50 Duration |
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Lecture 9 | Quick Note Upcoming Video | |
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Lecture 10 | Optional Learn SQL | |
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Lecture 11 | Hadoop, HDFS and MapReduce | 00:04:23 Duration |
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Lecture 12 | Apache Spark and Apache Flink | 00:02:08 Duration |
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Lecture 13 | Kafka and Stream Processing | 00:04:33 Duration |
Section 14 : Neural Networks Deep Learning, Transfer Learning and TensorFlow 2
Section 15 : Storytelling + Communication How To Present Your Work
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Lecture 1 | Section Overview | 00:02:19 Duration |
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Lecture 2 | Communicating Your Work | 00:03:22 Duration |
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Lecture 3 | Communicating With Managers | 00:02:58 Duration |
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Lecture 4 | Communicating With Co-Workers | 00:03:43 Duration |
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Lecture 5 | Weekend Project Principle | 00:06:32 Duration |
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Lecture 6 | Communicating With Outside World | 00:03:29 Duration |
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Lecture 7 | Storytelling | 00:03:06 Duration |
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Lecture 8 | Communicating and sharing your work Further reading |
Section 16 : Career Advice + Extra Bits
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Lecture 1 | Endorsements On LinkedIn | |
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Lecture 2 | Quick Note Upcoming Video | |
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Lecture 3 | What If I Don't Have Enough Experience | 00:15:03 Duration |
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Lecture 4 | Learning Guideline | |
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Lecture 5 | Quick Note Upcoming Videos | |
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Lecture 6 | JTS Learn to Learn | 00:02:00 Duration |
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Lecture 7 | JTS Start With Why | 00:02:44 Duration |
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Lecture 8 | Quick Note Upcoming Videos | |
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Lecture 9 | CWD Git + Github | 00:17:40 Duration |
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Lecture 10 | CWD Git + Github 2 | 00:16:53 Duration |
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Lecture 11 | Contributing To Open Source | 00:14:44 Duration |
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Lecture 12 | Contributing To Open Source 2 | 00:09:43 Duration |
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Lecture 13 | Coding Challenges | |
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Lecture 14 | Exercise Contribute To Open Source |
Section 17 : Learn Python
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Lecture 1 | What Is A Programming Language | 00:06:24 Duration |
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Lecture 2 | Python Interpreter | 00:07:04 Duration |
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Lecture 3 | How To Run Python Code | 00:04:53 Duration |
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Lecture 4 | Our First Python Program | 00:07:44 Duration |
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Lecture 5 | Latest Version Of Python | 00:01:58 Duration |
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Lecture 6 | Python 2 vs Python 3 | 00:06:41 Duration |
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Lecture 7 | Exercise How Does Python Work | 00:02:10 Duration |
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Lecture 8 | Learning Python | 00:02:05 Duration |
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Lecture 9 | Python Data Types | 00:04:46 Duration |
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Lecture 10 | How To Succeed | |
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Lecture 11 | Numbers | 00:11:09 Duration |
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Lecture 12 | Math Functions | 00:04:29 Duration |
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Lecture 13 | DEVELOPER FUNDAMENTALS I | 00:04:07 Duration |
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Lecture 14 | Operator Precedence | 00:03:10 Duration |
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Lecture 15 | Exercise Operator Precedence | |
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Lecture 16 | Optional bin() and complex | 00:04:02 Duration |
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Lecture 17 | Variables | 00:13:13 Duration |
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Lecture 18 | Expressions vs Statements | 00:01:37 Duration |
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Lecture 19 | Augmented Assignment Operator | 00:02:49 Duration |
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Lecture 20 | Strings | 00:05:30 Duration |
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Lecture 21 | String Concatenation | 00:01:16 Duration |
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Lecture 22 | Type Conversion | 00:03:03 Duration |
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Lecture 23 | Escape Sequences | 00:04:24 Duration |
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Lecture 24 | Formatted Strings | 00:08:24 Duration |
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Lecture 25 | String Indexes | 00:08:57 Duration |
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Lecture 26 | Immutability | 00:03:14 Duration |
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Lecture 27 | Built-In Functions + Methods | 00:10:04 Duration |
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Lecture 28 | Booleans | 00:03:22 Duration |
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Lecture 29 | Exercise Type Conversion | 00:08:23 Duration |
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Lecture 30 | DEVELOPER FUNDAMENTALS II | 00:04:42 Duration |
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Lecture 31 | Exercise Password Checker | 00:07:21 Duration |
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Lecture 32 | Lists | 00:05:01 Duration |
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Lecture 33 | List Slicing | 00:07:48 Duration |
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Lecture 34 | Matrix | 00:04:11 Duration |
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Lecture 35 | List Methods | 00:10:28 Duration |
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Lecture 36 | List Methods 2 | 00:04:24 Duration |
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Lecture 37 | List Methods 3 | 00:04:52 Duration |
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Lecture 38 | Common List Patterns | 00:05:57 Duration |
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Lecture 39 | List Unpacking | 00:02:41 Duration |
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Lecture 40 | None | 00:01:51 Duration |
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Lecture 41 | Dictionaries | 00:06:21 Duration |
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Lecture 42 | DEVELOPER FUNDAMENTALS III | 00:02:40 Duration |
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Lecture 43 | Dictionary Keys | 00:03:37 Duration |
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Lecture 44 | Dictionary Methods | 00:04:37 Duration |
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Lecture 45 | Dictionary Methods 2 | 00:07:04 Duration |
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Lecture 46 | Tuples | 00:04:47 Duration |
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Lecture 47 | Tuples 2 | 00:03:15 Duration |
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Lecture 48 | Sets | 00:07:24 Duration |
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Lecture 49 | Sets 2 | 00:08:45 Duration |
Section 18 : Learn Python Part 2
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Lecture 1 | Breaking The Flow | 00:02:35 Duration |
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Lecture 2 | Conditional Logic | 00:13:18 Duration |
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Lecture 3 | Indentation In Python | 00:04:39 Duration |
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Lecture 4 | Truthy vs Falsey | 00:05:18 Duration |
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Lecture 5 | Ternary Operator | 00:04:14 Duration |
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Lecture 6 | Short Circuiting | 00:04:02 Duration |
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Lecture 7 | Logical Operators | 00:06:56 Duration |
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Lecture 8 | Exercise Logical Operators | 00:07:48 Duration |
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Lecture 9 | is vs == | 00:07:36 Duration |
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Lecture 10 | For Loops | 00:07:01 Duration |
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Lecture 11 | Iterables | 00:06:44 Duration |
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Lecture 12 | Exercise Tricky Counter | 00:03:23 Duration |
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Lecture 13 | range() | 00:04:37 Duration |
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Lecture 14 | enumerate() | 00:05:39 Duration |
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Lecture 15 | While Loops | 00:06:28 Duration |
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Lecture 16 | While Loops 2 | 00:05:50 Duration |
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Lecture 17 | break, continue, pass | 00:04:16 Duration |
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Lecture 18 | Our First GUI | 00:08:49 Duration |
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Lecture 19 | DEVELOPER FUNDAMENTALS IV | 00:06:34 Duration |
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Lecture 20 | Exercise Find Duplicates | 00:03:55 Duration |
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Lecture 21 | Functions | 00:07:41 Duration |
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Lecture 22 | Parameters and Arguments | 00:04:25 Duration |
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Lecture 23 | Default Parameters and Keyword Arguments | 00:05:41 Duration |
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Lecture 24 | return | 00:13:11 Duration |
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Lecture 25 | Exercise Tesla | |
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Lecture 26 | Methods vs Functions | 00:04:33 Duration |
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Lecture 27 | Docstrings | 00:03:47 Duration |
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Lecture 28 | Clean Code | 00:04:38 Duration |
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Lecture 29 | args and kwargs | 00:07:57 Duration |
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Lecture 30 | Exercise Functions | 00:04:18 Duration |
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Lecture 31 | Scope | 00:03:38 Duration |
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Lecture 32 | Scope Rules | 00:06:55 Duration |
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Lecture 33 | global Keyword | 00:06:13 Duration |
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Lecture 34 | nonlocal Keyword | |
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Lecture 35 | Why Do We Need Scope | 00:03:39 Duration |
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Lecture 36 | Pure Functions | 00:09:23 Duration |
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Lecture 37 | map() | 00:06:31 Duration |
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Lecture 38 | filter() | 00:04:23 Duration |
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Lecture 39 | zip() | 00:03:28 Duration |
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Lecture 40 | reduce() | 00:07:32 Duration |
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Lecture 41 | List Comprehensions | 00:08:37 Duration |
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Lecture 42 | Set Comprehensions | 00:06:27 Duration |
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Lecture 43 | Exercise Comprehensions | 00:04:37 Duration |
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Lecture 44 | Python Exam Testing Your Understanding | |
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Lecture 45 | Modules in Python | 00:10:55 Duration |
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Lecture 46 | Quick Note Upcoming Videos | |
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Lecture 47 | Optional PyCharm | 00:08:19 Duration |
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Lecture 48 | Packages in Python | 00:10:45 Duration |
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Lecture 49 | Different Ways To Import | 00:07:04 Duration |
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Lecture 50 | Next Steps |
Section 19 : Bonus Learn Advanced Statistics and Mathematics for FREE!
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Lecture 1 | Statistics and Mathematics |
Section 20 : Where To Go From Here
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Lecture 1 | Become An Alumni | |
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Lecture 2 | Thank You | 00:02:44 Duration |
Section 21 : BONUS SECTION
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Lecture 1 | Bonus Lecture |