Roadmap for 30 Day Machine Learning Challange

Day One: 
  • Core concepts of Machine Learning
  • Machine Learning Proces

    Day Two:
      • Project: Classification Walkthrough: Titanic Dataset

        Day Three:
        • Project: Regression Walkthrough: California Housing Price Dataset

        Day Four:

        • Working with Missing Data
        • Examining Missing Data
        • Dropping Missing Data
        • Imputing Data
        • Adding Indicator Columns
        Day Five:

        • Working with Cleaning Data
        • Column Names
        • Replacing Missing Values

        Day Six:

        • Data Exploration
        • Data Size
        • Summary Stats
        • Histogram
        • Scatter Plot
        • Joint Plot
        • Pair Grid
        • Box and Violin Plots
        • Comparing Two Ordinal Values
        • Correlation
        • RadViz
        • Parallel Coordinates

        Day Seven:

        • Preprocessing Data
        • Standardize
        • Scale to Range
        • Dummy Variables
        • Label Encoder
        • Frequency Encoding
        • Pulling Categories from Strings
        • Other Categorical Encoding
        • Date Feature Engineering
        • Add col _na Feature
        • Manual Feature Engineering

        Day Eight:

        • Feature Selection
        • Collinear Columns
        • Lasso Regression
        • Recursive Feature Elimination
        • Mutual Information
        • Principal Component Analysis
        • Feature Importance
        Day Nine:

        • Dealing with Imbalance Classes
        • Use a Different Metric
        • Tree-based Algorithms and Ensembles
        • Penalize Models
        • Upsampling Minority
        • Generate Minority Data
        • Downsampling Majority
        • Upsampling Then Downsampling

        Day Ten:

        • Classification Algorithms

        Day Eleven:

        • Model Selection
        Day Twelve:

        • Metrics and Classification Evaluation

        • Confusion Matrix
        • Metrics
        • Accuracy
        • Recall
        • Precision
        • F1
        • Classification Report
        • RoC
        • Precision-Recall Curve
        • Cumulative Gains Plot
        • Lift Curve
        • Class Balance
        • Class Prediction Error
        • Discrimination Threshold

        Day Thirteen:

        • Explaining Classification Model

        Day Fourteen:

        • Regression Algorithms

        Day Fifteen:

        • Metrics and Regression Evaluation

        Day Sixteen:

        • Explaining Regression Model

        Day Seventeen:

        • Dimensionality Reduction

        Day Eighteen:

        • Clustering

        Day Nineteen:

        • Implementing Pipeline

        Day Twenty:

        • Neural networks
        • Artificial neural networks (ANN)

        Day Twenty-one:

        • Project: ANN walkthrough: Predicting Stock Prices

        Day Twenty-two:

        • Natural Language Processing (NLP)

        Day Twenty-three:

        • Project: NLP walkthrough: Mining Newsgroups Dataset

        Day Twenty-four:

        • Deep Learning Basics

        Day Twenty-five:

        • Problems and Solutions

        Day Twenty-six:

        • Machine Learning best practices

        Day Twenty-seven:

        • Project: Building a Movie Recommendation Engine

        Day Twenty-eight:

        • Project:Recognizing Faces

        Day Twenty-nine:

        • Project: Predicting Online Ad Click-Through: Tree-based Algorithm

        Day Thirty:

        • Project: NewsGroups Dataset with Clustering and Topic Modeling


        ## Reference : https://www.learnmldaily.com/






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