029 _args and __kwargs | cipskills

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Complete Machine Learning & Data Science Bootcamp

01 - Introduction

001 Course Outline (5:59)
004 Your First Day (3:48)

02 - Machine Learning 101

001 What Is Machine Learning_ (6:52)
002 AI_Machine Learning_Data Science (4:51)
003 Exercise_ Machine Learning Playground (6:16)
004 How Did We Get Here_ (6:03)
005 Exercise_ YouTube Recommendation Engine (4:25)
006 Types of Machine Learning (4:41)
008 What Is Machine Learning_ Round 2 (4:45)
009 Section Review (1:48)

03 - Machine Learning and Data Science Framework

001 Section Overview (3:08)
002 Introducing Our Framework (2:38)
003 6 Step Machine Learning Framework (4:59)
004 Types of Machine Learning Problems (10:32)
005 Types of Data (4:51)
006 Types of Evaluation (3:31)
007 Features In Data (5:22)
008 Modelling - Splitting Data (5:58)
009 Modelling - Picking the Model (4:35)
010 Modelling - Tuning (3:17)
011 Modelling - Comparison (9:32)
013 Experimentation (3:35)
014 Tools We Will Use (4:00)

04 - The 2 Paths

001 The 2 Paths (3:27)

05 - Data Science Environment Setup

001 Section Overview (1:09)
002 Introducing Our Tools (3:28)
003 What is Conda_ (2:35)
004 Conda Environments (4:30)
005 Mac Environment Setup (17:26)
006 Mac Environment Setup 2 (14:11)
007 Windows Environment Setup (5:17)
008 Windows Environment Setup 2 (23:17)
011 Jupyter Notebook Walkthrough (10:20)
012 Jupyter Notebook Walkthrough 2 (16:18)
013 Jupyter Notebook Walkthrough 3 (8:10)

06 - Pandas_ Data Analysis

001 Section Overview (2:27)
003 Pandas Introduction (4:29)
004 Series, Data Frames and CSVs (13:21)
006 Describing Data with Pandas (9:48)
007 Selecting and Viewing Data with Pandas (11:08)
008 Selecting and Viewing Data with Pandas Part 2 (13:07)
009 Manipulating Data (13:57)
010 Manipulating Data 2 (9:57)
011 Manipulating Data 3 (10:12)
013 How To Download The Course Assignments (7:43)

07 - NumPy

001 Section Overview (2:41)
002 NumPy Introduction (5:17)
004 NumPy DataTypes and Attributes (14:05)
005 Creating NumPy Arrays (9:22)
006 NumPy Random Seed (7:17)
007 Viewing Arrays and Matrices (9:35)
008 Manipulating Arrays (11:32)
009 Manipulating Arrays 2 (9:44)
010 Standard Deviation and Variance (7:10)
011 Reshape and Transpose (7:26)
012 Dot Product vs Element Wise (11:45)
013 Exercise_ Nut Butter Store Sales (13:04)
014 Comparison Operators (3:33)
015 Sorting Arrays (6:20)
016 Turn Images Into NumPy Arrays (7:37)

08 - Matplotlib_ Plotting and Data Visualization

001 Section Overview (1:50)
002 Matplotlib Introduction (5:16)
003 Importing And Using Matplotlib (11:36)
004 Anatomy Of A Matplotlib Figure (9:19)
005 Scatter Plot And Bar Plot (10:09)
006 Histograms And Subplots (8:40)
007 Subplots Option 2 (4:15)
008 Quick Tip_ Data Visualizations (1:48)
009 Plotting From Pandas DataFrames (5:58)
011 Plotting From Pandas DataFrames 2 (10:33)
012 Plotting from Pandas DataFrames 3 (8:32)
013 Plotting from Pandas DataFrames 4 (6:36)
014 Plotting from Pandas DataFrames 5 (8:29)
015 Plotting from Pandas DataFrames 6 (8:28)
016 Plotting from Pandas DataFrames 7 (11:20)
017 Customizing Your Plots (10:09)
018 Customizing Your Plots 2 (9:41)
019 Saving And Sharing Your Plots (4:14)

09 - Scikit-learn_ Creating Machine Learning Models

001 Section Overview (2:30)
002 Scikit-learn Introduction (6:41)
004 Refresher_ What Is Machine Learning_ (5:40)
006 Scikit-learn Cheatsheet (6:13)
007 Typical scikit-learn Workflow (23:14)
008 Optional_ Debugging Warnings In Jupyter (18:57)
009 Getting Your Data Ready_ Splitting Your Data (8:37)
010 Quick Tip_ Clean, Transform, Reduce (5:03)
011 Getting Your Data Ready_ Convert Data To Numbers (16:54)
013 Getting Your Data Ready_ Handling Missing Values With Pandas (12:22)
016 Getting Your Data Ready_ Handling Missing Values With Scikit-learn (17:29)
017 NEW_ Choosing The Right Model For Your Data (20:14)
018 NEW_ Choosing The Right Model For Your Data 2 (Regression) (11:21)
020 Quick Tip_ How ML Algorithms Work (1:25)
021 Choosing The Right Model For Your Data 3 (Classification) (12:45)
022 Fitting A Model To The Data (6:45)
023 Making Predictions With Our Model (8:25)
024 predict() vs predict_proba() (8:33)
025 NEW_ Making Predictions With Our Model (Regression) (8:48)
026 NEW_ Evaluating A Machine Learning Model (Score) Part 1 (9:41)
027 NEW_ Evaluating A Machine Learning Model (Score) Part 2 (6:47)
028 Evaluating A Machine Learning Model 2 (Cross Validation) (13:16)
029 Evaluating A Classification Model 1 (Accuracy) (4:46)
030 Evaluating A Classification Model 2 (ROC Curve) (9:04)
031 Evaluating A Classification Model 3 (ROC Curve) (7:44)
033 Evaluating A Classification Model 4 (Confusion Matrix) (11:01)
034 NEW_ Evaluating A Classification Model 5 (Confusion Matrix) (14:23)
035 Evaluating A Classification Model 6 (Classification Report) (10:16)
036 NEW_ Evaluating A Regression Model 1 (R2 Score) (9:59)
037 NEW_ Evaluating A Regression Model 2 (MAE) (7:22)
038 NEW_ Evaluating A Regression Model 3 (MSE) (9:49)
040 NEW_ Evaluating A Model With Cross Validation and Scoring Parameter (25:19)
041 NEW_ Evaluating A Model With Scikit-learn Functions (14:02)
042 Improving A Machine Learning Model (11:16)
043 Tuning Hyperparameters (23:15)
044 Tuning Hyperparameters 2 (14:23)
045 Tuning Hyperparameters 3 (14:59)
047 Quick Tip_ Correlation Analysis (2:28)
048 Saving And Loading A Model (7:29)
049 Saving And Loading A Model 2 (6:20)
050 Putting It All Together (20:19)
051 Putting It All Together 2 (11:34)

11 - Milestone Project 1_ Supervised Learning (Classification)

001 Section Overview (2:09)
002 Project Overview (6:09)
003 Project Environment Setup (10:59)
004 Optional_ Windows Project Environment Setup (4:52)
005 Step 1~4 Framework Setup (12:06)
006 Getting Our Tools Ready (9:04)
007 Exploring Our Data (8:33)
008 Finding Patterns (10:03)
009 Finding Patterns 2 (16:48)
010 Finding Patterns 3 (13:37)
011 Preparing Our Data For Machine Learning (8:51)
012 Choosing The Right Models (10:15)
013 Experimenting With Machine Learning Models (6:31)
014 Tuning_Improving Our Model (13:49)
015 Tuning Hyperparameters (11:27)
016 Tuning Hyperparameters 2 (11:49)
017 Tuning Hyperparameters 3 (7:06)
019 Evaluating Our Model (11:00)
020 Evaluating Our Model 2 (5:55)
021 Evaluating Our Model 3 (8:50)
022 Finding The Most Important Features (16:07)
023 Reviewing The Project (9:13)

12 - Milestone Project 2_ Supervised Learning (Time Series Data)

001 Section Overview (1:07)
002 Project Overview (4:24)
004 Project Environment Setup (10:52)
005 Step 1~4 Framework Setup (8:36)
006 Exploring Our Data (14:16)
007 Exploring Our Data 2 (6:16)
008 Feature Engineering (15:24)
009 Turning Data Into Numbers (15:38)
010 Filling Missing Numerical Values (12:49)
011 Filling Missing Categorical Values (8:27)
012 Fitting A Machine Learning Model (7:16)
013 Splitting Data (10:01)
015 Custom Evaluation Function (11:13)
016 Reducing Data (10:36)
017 RandomizedSearchCV (9:32)
018 Improving Hyperparameters (8:11)
019 Preproccessing Our Data (13:15)
020 Making Predictions (9:18)
021 Feature Importance (13:50)

13 - Data Engineering

001 Data Engineering Introduction (3:24)
002 What Is Data_ (6:42)
003 What Is A Data Engineer_ (4:21)
004 What Is A Data Engineer 2_ (5:36)
005 What Is A Data Engineer 3_ (5:04)
006 What Is A Data Engineer 4_ (3:22)
007 Types Of Databases (6:50)
009 Optional_ OLTP Databases (10:54)
011 Hadoop, HDFS and MapReduce (4:22)
012 Apache Spark and Apache Flink (2:07)
013 Kafka and Stream Processing (4:33)

14 - Neural Networks_ Deep Learning, Transfer Learning and TensorFlow 2

001 Section Overview (2:06)
002 Deep Learning and Unstructured Data (13:36)
004 Setting Up Google Colab (7:17)
005 Google Colab Workspace (4:23)
006 Uploading Project Data (6:52)
007 Setting Up Our Data (4:40)
008 Setting Up Our Data 2 (1:32)
009 Importing TensorFlow 2 (12:43)
010 Optional_ TensorFlow 2.0 Default Issue (3:39)
011 Using A GPU (8:59)
012 Optional_ GPU and Google Colab (4:27)
013 Optional_ Reloading Colab Notebook (6:49)
014 Loading Our Data Labels (12:04)
015 Preparing The Images (12:32)
016 Turning Data Labels Into Numbers (12:12)
017 Creating Our Own Validation Set (9:18)
018 Preprocess Images (10:25)
019 Preprocess Images 2 (11:00)
020 Turning Data Into Batches (9:37)
021 Turning Data Into Batches 2 (17:54)
022 Visualizing Our Data (12:41)
023 Preparing Our Inputs and Outputs (6:38)
025 Building A Deep Learning Model (11:42)
026 Building A Deep Learning Model 2 (10:53)
027 Building A Deep Learning Model 3 (9:06)
028 Building A Deep Learning Model 4 (9:12)
029 Summarizing Our Model (4:52)
030 Evaluating Our Model (9:27)
031 Preventing Overfitting (4:20)
032 Training Your Deep Neural Network (19:09)
033 Evaluating Performance With TensorBoard (7:30)
034 Make And Transform Predictions (15:05)
035 Transform Predictions To Text (15:20)
036 Visualizing Model Predictions (14:46)
037 Visualizing And Evaluate Model Predictions 2 (15:52)
038 Visualizing And Evaluate Model Predictions 3 (10:40)
039 Saving And Loading A Trained Model (13:34)
040 Training Model On Full Dataset (15:02)
041 Making Predictions On Test Images (16:55)
042 Submitting Model to Kaggle (14:14)
043 Making Predictions On Our Images (15:15)

15 - Storytelling + Communication_ How To Present Your Work

001 Section Overview (2:19)
002 Communicating Your Work (3:22)
003 Communicating With Managers (2:58)
004 Communicating With Co-Workers (3:42)
005 Weekend Project Principle (6:32)
006 Communicating With Outside World (3:29)
007 Storytelling (3:06)

16 - Career Advice + Extra Bits

003 What If I Don't Have Enough Experience_ (15:03)
006 JTS_ Learn to Learn (1:59)
007 JTS_ Start With Why (2:43)
009 CWD_ Git + Github (17:40)
010 CWD_ Git + Github 2 (16:52)
011 Contributing To Open Source (14:44)
012 Contributing To Open Source 2 (9:42)

17 - Learn Python

001 What Is A Programming Language (6:24)
002 Python Interpreter (7:04)
003 How To Run Python Code (4:53)
004 Our First Python Program (7:43)
005 Latest Version Of Python (1:58)
006 Python 2 vs Python 3 (6:41)
007 Exercise_ How Does Python Work_ (2:10)
008 Learning Python (2:05)
009 Python Data Types (4:46)
011 Numbers (11:09)
012 Math Functions (4:29)
013 DEVELOPER FUNDAMENTALS_ I (4:07)
014 Operator Precedence (3:10)
016 Optional_ bin() and complex (4:02)
017 Variables (13:12)
018 Expressions vs Statements (1:36)
019 Augmented Assignment Operator (2:49)
020 Strings (5:30)
021 String Concatenation (1:16)
022 Type Conversion (3:03)
023 Escape Sequences (4:23)
024 Formatted Strings (8:24)
025 String Indexes (8:57)
026 Immutability (3:13)
027 Built-In Functions + Methods (10:03)
028 Booleans (3:21)
029 Exercise_ Type Conversion (8:23)
030 DEVELOPER FUNDAMENTALS_ II (4:42)
031 Exercise_ Password Checker (7:21)
032 Lists (5:01)
033 List Slicing (7:48)
034 Matrix (4:11)
035 List Methods (10:28)
036 List Methods 2 (4:24)
037 List Methods 3 (4:52)
038 Common List Patterns (5:57)
039 List Unpacking (2:41)
040 None (1:51)
041 Dictionaries (6:21)
042 DEVELOPER FUNDAMENTALS_ III (2:40)
043 Dictionary Keys (3:37)
044 Dictionary Methods (4:37)
045 Dictionary Methods 2 (7:04)
046 Tuples (4:46)
047 Tuples 2 (3:14)
048 Sets (7:24)
049 Sets 2 (8:45)

18 - Learn Python Part 2

001 Breaking The Flow (2:35)
002 Conditional Logic (13:18)
003 Indentation In Python (4:38)
004 Truthy vs Falsey (5:18)
005 Ternary Operator (4:14)
006 Short Circuiting (4:02)
007 Logical Operators (6:56)
008 Exercise_ Logical Operators (7:47)
009 is vs == (7:36)
010 For Loops (7:01)
011 Iterables (6:44)
012 Exercise_ Tricky Counter (3:23)
013 range() (5:39)
014 enumerate() (4:37)
015 While Loops (6:28)
016 While Loops 2 (5:49)
017 break, continue, pass (4:15)
018 Our First GUI (8:49)
019 DEVELOPER FUNDAMENTALS_ IV (6:34)
020 Exercise_ Find Duplicates (3:55)
021 Functions (7:41)
022 Parameters and Arguments (4:25)
023 Default Parameters and Keyword Arguments (5:41)
024 return (13:11)
026 Methods vs Functions (4:33)
027 Docstrings (3:47)
028 Clean Code (4:38)
029 _args and __kwargs (7:57)
030 Exercise_ Functions (4:18)
031 Scope (3:38)
032 Scope Rules (6:55)
033 global Keyword (6:13)
034 nonlocal Keyword (3:21)
035 Why Do We Need Scope_ (3:38)
036 Pure Functions (9:23)
037 map() (6:30)
038 filter() (4:23)
039 zip() (3:28)
040 reduce() (7:32)
041 List Comprehensions (8:37)
042 Set Comprehensions (6:27)
043 Exercise_ Comprehensions (4:36)
045 Modules in Python (10:54)
047 Optional_ PyCharm (8:19)
048 Packages in Python (10:45)
049 Different Ways To Import (7:03)

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