100-Days-Of-ML-Code

100 Days of Machine Learning Coding as proposed by Siraj Raval

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Data PreProcessing | Day 1

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Simple Linear Regression | Day 2

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Multiple Linear Regression | Day 3

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Logistic Regression | Day 4

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Logistic Regression Implementation | Day 5/6

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Learning & Practicing Linear Algebra | Day 7-16

Learning Matplotlib & Seaborn for Data visualisation | Day 17

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Learning Plotly and Cufflinks | Day 18-20

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Learning Geographical plotting using choropleth | Day 21-23

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Linear Regression Project | Day 24

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Logistic Regression Project | Day 25

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Learning KNN | Day 26

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Learning Decision Tree & Random Forest | Day 27

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Learning SVM | Day 28

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Learning KMeans clustring | Day 29

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Recommender System Project | Day 30

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NLP Project | Day 31

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Tensorflow 1.0 basics | Day 32

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Tensorflow prediction with Estimator | Day 33

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Handwritten number recognizer (Using Tensorflow) | Day 34

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Handwritten number recognizer (Using Keras) | Day 35

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Tensorflow project exercise | Day 36

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Politics Exercise using Keras | Day 37

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Keras CNN Project | Day 38

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Keras RNN Project | Day 39

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KNN Exercise Project | Day 40

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PCA Project | Day 41

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KPCA Project | Day 42

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Q-learning Project | Day 43

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Transfer learning Project | Day 44

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ML Workflow Project Part 1 | Day 45

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ML Workflow Project Part 2 | Day 46

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ML Workflow Project Part 3 | Day 47

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PCA Example Project | Day 48

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K-Fold Cross Validation Example | Day 49

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Find Top Pages of a Website Example | Day 50

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Digging Deeper in Numpy library | Day 51-52

Check out the chapters:

Computation on NumPy Arrays: Universal Functions.

Aggregations: Min, Max, and Everything In Between.

Computation on Arrays: Broadcasting.

Comparisons, Masks, and Boolean Logic.

Fancy Indexing.

Structured Data: NumPy's Structured Arrays.

Digging Deeper in Pandas library | Day 53-54

Check out the chapters:

Data indexing and selection.

Operating on data in pandas.

Handling missing data.

Hierarchial indexing.

Combining datasets: Concat and Append.

Combining datasets: Merge and Join.

Aggregation and grouping in pandas.

Pivot tables in pandas.

Neural network with fashion mnist data and Keras | Day 55

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Pandas profiling library example | Day 56

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Deep dive in Matplotlib library | Day 57

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Deep dive in Seaborn library | Day 58

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Final project on mammographic masses dataset | Day 59-60

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Learning about slicing and selection in Numpy | Day 61

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Spark dataframe exercise project | Day 62

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Linear Regression Example in Spark | Day 63

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Linear Regression project in Spark | Day 64

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Linear Regression Consulting project in Spark | Day 65

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Logistic Regression example in Spark | Day 66

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Logistic Regression project in Spark | Day 67

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Logistic Regression consulting project in Spark | Day 68

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D Tree, Random Forest & Gradient Boosting Example in Spark | Day 69

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Decision Tree, Random Forest & Gradient Boosting Project in Spark | Day 70

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Tree models Consulting Project in Spark | Day 71

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Kmeans clustering example in Spark | Day 72

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Kmeans clustering project in Spark | Day 73

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Kmeans clustering consulting project in Spark | Day 74

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Telecom customer churn prediction project in Spark | Day 75

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Recommender system in Spark | Day 76

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Renewable energy prediction project in Spark | Day 77

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NLP project in Spark | Day 78

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Online Retail data clustering and analysis project | Day 79

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Superstore dataset analysis project | Day 80

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Spark streaming basic project | Day 81

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Spark streaming with Twitter API | Day 82

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Africa banking data analysis project | Day 83

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Nifty stock exchange data analysis project | Day 84

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MLflow example project | Day 85

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Extract text from image using Tesseract and Tessy | Day 86

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Extract text from image using Tesseract and Pytesseract | Day 87

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Reading docs to train Tesseract OCR | Day 88

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Text extraction from image using Deeplearning and Tesseract | Day 89

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Working on image Cropping, Resizing to get better result from OCR | Day 90

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Testing the Tesseract OCR on image Cropping, Resizing and Increased DPI | Day 91-92

Checking The official implementation for A2-RL: Aesthetics Aware Rinforcement Learning for Automatic Image Cropping | Day 93-94

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Working on data collection and preparation for training Custom Handwritten Burmese OCR | Day 95

starting with this dataset

Learning how to deploy jupyter notebook in openshift | Day 96

Installing minishift to run openshift locally | Day 97-98

Installed minishift successfully working on deployment | Day 99

Started to work on jupyter notebook deployment in openshift environment | Day 100

Made with ❤️ and Python

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