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Премиум
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    Course Outline
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    Your First Day
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    What Is Machine Learning?
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    AI/Machine Learning/Data Science
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    Exercise: Machine Learning Playground
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    How Did We Get Here?
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    Exercise: YouTube Recommendation Engine
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    Types of Machine Learning
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    What Is Machine Learning? Round 2
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    Section Review
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    Section Overview
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    Introducing Our Framework
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    6 Step Machine Learning Framework
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    Types of Machine Learning Problems
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    Types of Data
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    Types of Evaluation
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    Features In Data
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    Modelling - Splitting Data
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    Modelling - Picking the Model
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    Modelling - Tuning
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    Modelling - Comparison
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    Experimentation
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    Tools We Will Use
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    The 2 Paths
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    Section Overview
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    Introducing Our Tools
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    What is Conda?
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    Conda Environments
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    Mac Environment Setup
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    Mac Environment Setup 2
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    Windows Environment Setup
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    Windows Environment Setup 2
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    Jupyter Notebook Walkthrough
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    Jupyter Notebook Walkthrough 2
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    Jupyter Notebook Walkthrough 3
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    Section Overview
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    Pandas Introduction
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    Series, Data Frames and CSVs
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    Describing Data with Pandas
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    Selecting and Viewing Data with Pandas
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    Selecting and Viewing Data with Pandas Part 2
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    Manipulating Data
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    Manipulating Data 2
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    Manipulating Data 3
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    How To Download The Course Assignments
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    Section Overview
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    NumPy Introduction
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    NumPy DataTypes and Attributes
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    Creating NumPy Arrays
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    NumPy Random Seed
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    Viewing Arrays and Matrices
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    Manipulating Arrays
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    Manipulating Arrays 2
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    Standard Deviation and Variance
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    Reshape and Transpose
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    Dot Product vs Element Wise
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    Exercise: Nut Butter Store Sales
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    Comparison Operators
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    Sorting Arrays
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    Turn Images Into NumPy Arrays
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    Section Overview
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    Matplotlib Introduction
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    Importing And Using Matplotlib
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    Anatomy Of A Matplotlib Figure
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    Scatter Plot And Bar Plot
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    Histograms And Subplots
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    Subplots Option 2
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    Quick Tip: Data Visualizations
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    Plotting From Pandas DataFrames
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    Plotting From Pandas DataFrames 2
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    Plotting from Pandas DataFrames 3
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    Plotting from Pandas DataFrames 4
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    Plotting from Pandas DataFrames 5
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    Plotting from Pandas DataFrames 6
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    Plotting from Pandas DataFrames 7
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    Customizing Your Plots
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    Customizing Your Plots 2
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    Saving And Sharing Your Plots
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    Section Overview
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    Scikit-learn Introduction
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    Refresher: What Is Machine Learning?
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    Scikit-learn Cheatsheet
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    Typical scikit-learn Workflow
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    Optional: Debugging Warnings In Jupyter
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    Getting Your Data Ready: Splitting Your Data
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    Quick Tip: Clean, Transform, Reduce
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    Getting Your Data Ready: Convert Data To Numbers
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    Getting Your Data Ready: Handling Missing Values With Pandas
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    Getting Your Data Ready: Handling Missing Values With Scikit-learn
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    Choosing The Right Model For Your Data
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    Choosing The Right Model For Your Data 2 (Regression)
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    Quick Tip: How ML Algorithms Work
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    Choosing The Right Model For Your Data 3 (Classification)
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    Fitting A Model To The Data
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    Making Predictions With Our Model
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    predict() vs predict_proba()
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    Making Predictions With Our Model (Regression)
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    Evaluating A Machine Learning Model (Score)
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    Evaluating A Machine Learning Model 2 (Cross Validation)
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    Evaluating A Classification Model 1 (Accuracy)
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    Evaluating A Classification Model 2 (ROC Curve)
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    Evaluating A Classification Model 3 (ROC Curve)
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    Evaluating A Classification Model 4 (Confusion Matrix)
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    Evaluating A Classification Model 5 (Confusion Matrix)
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    Evaluating A Classification Model 6 (Classification Report)
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    Evaluating A Regression Model 1 (R2 Score)
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    Evaluating A Regression Model 2 (MAE)
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    Evaluating A Regression Model 3 (MSE)
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    Evaluating A Model With Cross Validation and Scoring Parameter
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    Evaluating A Model With Scikit-learn Functions
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    Improving A Machine Learning Model
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    Tuning Hyperparameters
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    Tuning Hyperparameters 2
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    Tuning Hyperparameters 3
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    Quick Tip: Correlation Analysis
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    Saving And Loading A Model
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    Saving And Loading A Model 2
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    Putting It All Together
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    Putting It All Together 2
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    Section Overview
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    Project Overview
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    Project Environment Setup
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    Step 1~4 Framework Setup
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    Getting Our Tools Ready
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    Exploring Our Data
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    Finding Patterns
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    Finding Patterns 2
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    Finding Patterns 3
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    Preparing Our Data For Machine Learning
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    Choosing The Right Models
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    Experimenting With Machine Learning Models
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    Tuning/Improving Our Model
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    Tuning Hyperparameters
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    Tuning Hyperparameters 2
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    Tuning Hyperparameters 3
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    Evaluating Our Model
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    Evaluating Our Model 2
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    Evaluating Our Model 3
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    Finding The Most Important Features
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    Reviewing The Project
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    Section Overview
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    Project Overview
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    Project Environment Setup
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    Step 1~4 Framework Setup
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    Exploring Our Data
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    Exploring Our Data 2
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    Feature Engineering
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    Turning Data Into Numbers
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    Filling Missing Numerical Values
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    Filling Missing Categorical Values
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    Fitting A Machine Learning Model
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    Splitting Data
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    Custom Evaluation Function
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    Reducing Data
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    RandomizedSearchCV
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    Improving Hyperparameters
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    Preproccessing Our Data
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    Making Predictions
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    Feature Importance
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    Data Engineering Introduction
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    What Is Data?
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    What Is A Data Engineer?
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    What Is A Data Engineer 2?
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    What Is A Data Engineer 3?
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    What Is A Data Engineer 4?
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    Types Of Databases
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    Optional: OLTP Databases
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    Hadoop, HDFS and MapReduce
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    Apache Spark and Apache Flink
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    Kafka and Stream Processing
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    Section Overview
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    Deep Learning and Unstructured Data
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    Setting Up Google Colab
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    Google Colab Workspace
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    Uploading Project Data
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    Setting Up Our Data
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    Setting Up Our Data 2
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    Importing TensorFlow 2
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    Optional: TensorFlow 2.0 Default Issue
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    Using A GPU
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    Optional: GPU and Google Colab
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    Optional: Reloading Colab Notebook
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    Loading Our Data Labels
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    Preparing The Images
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    Turning Data Labels Into Numbers
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    Creating Our Own Validation Set
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    Preprocess Images
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    Preprocess Images 2
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    Turning Data Into Batches
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    Turning Data Into Batches 2
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    Visualizing Our Data
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    Preparing Our Inputs and Outputs
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    Building A Deep Learning Model
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    Building A Deep Learning Model 2
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    Building A Deep Learning Model 3
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    Building A Deep Learning Model 4
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    Summarizing Our Model
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    Evaluating Our Model
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    Preventing Overfitting
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    Training Your Deep Neural Network
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    Evaluating Performance With TensorBoard
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    Make And Transform Predictons
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    Transform Predictions To Text
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    Visualizing Model Predictions
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    Visualizing And Evaluate Model Predictions 2
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    Visualizing And Evaluate Model Predictions 3
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    Saving And Loading A Trained Model
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    Training Model On Full Dataset
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    Making Predictions On Test Images
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    Submitting Model to Kaggle
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    Making Predictions On Our Images
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    Section Overview
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    What If I Don't Have Enough Experience?
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    JTS: Learn to Learn
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    JTS: Start With Why
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    CWD: Git + Github
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    CWD: Git + Github 2
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    Contributing To Open Source
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    Contributing To Open Source 2
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    What Is A Programming Language
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    Python Interpreter
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    How To Run Python Code
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    Our First Python Program
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    Python 2 vs Python 3
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    Exercise: How Does Python Work?
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    Learning Python
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    Python Data Types
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    Numbers
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    Math Functions
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    DEVELOPER FUNDAMENTALS: I
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    Operator Precedence
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    Optional: bin() and complex
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    Variables
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    Expressions vs Statements
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    Augmented Assignment Operator
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    Strings
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    String Concatenation
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    Type Conversion
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    Escape Sequences
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    Formatted Strings
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    String Indexes
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    Immutability
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    Built-In Functions + Methods
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    Booleans
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    Exercise: Type Conversion
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    DEVELOPER FUNDAMENTALS: II
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    Exercise: Password Checker
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    Lists
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    List Slicing
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    Matrix
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    List Methods
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    List Methods 2
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    List Methods 3
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    Common List Patterns
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    List Unpacking
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    None
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    Dictionaries
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    DEVELOPER FUNDAMENTALS: III
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    Dictionary Keys
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    Dictionary Methods
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    Dictionary Methods 2
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    Tuples
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    Tuples 2
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    Sets
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    Sets 2
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    Breaking The Flow
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    Conditional Logic
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    Indentation In Python
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    Truthy vs Falsey
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    Ternary Operator
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    Short Circuiting
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    Logical Operators
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    Exercise: Logical Operators
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    is vs ==
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    For Loops
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    Iterables
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    Exercise: Tricky Counter
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    range()
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    enumerate()
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    While Loops
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    While Loops 2
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    break, continue, pass
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    Our First GUI
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    DEVELOPER FUNDAMENTALS: IV
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    Exercise: Find Duplicates
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    Functions
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    Parameters and Arguments
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    Default Parameters and Keyword Arguments
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    return
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    Methods vs Functions
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    Docstrings
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    Clean Code
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    *args and **kwargs
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    Exercise: Functions
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    Scope
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    Scope Rules
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    global Keyword
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    nonlocal Keyword
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    Why Do We Need Scope?
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    Pure Functions
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    map()
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    filter()
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    zip()
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    reduce()
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    List Comprehensions
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    Set Comprehensions
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    Exercise: Comprehensions
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    Modules in Python
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    Optional: PyCharm
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    Packages in Python
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    Different Ways To Import
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    Thank You