Awesome Open Source
Awesome Open Source


I will update this repository to learn Machine learning with python with statistics content and materials

Day - 1: 6-4-2019 We Learnt about Different types of Analytics Different types of Machine Learning Why Python? Features of Python

Day - 2: 7-4-2019 We Started practising the python Ways to implement python Why Jupyter notebook? What is keyword, variable? Conditions on creating a identifier Different datastructure List, Tuple, Set, Dictionary, String Typecast

Day - 3: 13-4-019 Control Sataement Condition Statement What is Indendation? Functions Paraments, Defaut Parameters, Arbitory Parameters

Day - 4: 14-4-2019 Recursive Function Lambda Function Map, Filter, Reduce List Comprehension Set Comprehension Try, Except, Finally

Day - 5: 27-4-2019 Class and Object OS Library Module in python import and from import Numpy: Why Numpy? Numpy Basics

Day - 6: 28-4-2019 Pandas Data Loading Data Manipulation Data Filtering Data Grouping

Day - 7: 04-05-2019 What is Data Preprocessing? Why Data Preprocessing? Diferent Technique of Data Preprocessing Data Preprocessing with pandas example

Day - 8: 05-05-2019 Part - 1: What is Statistics? What are the Data types? Different measures - Central Tendency and Dispersion Percentiles, Quartiles and Box - Plots

Day - 9: 11-05-2019 Part - 2: Examples understanding indetail Concepts of Descriptive Statistics of Part - 1, Correlation, Covariance and Visualization

Day - 10: 12-05-2019 Exercise Session: Explaining Sampling bias, Various Sampling techniques, Characteristics of Normal Distribution and empirical rule, Central Limit Theorem, Standard Error, Z - Score, Confiedence Intervals

Day - 11: 18-05-2019 Exercise Session: Finding Descriptive Statistics for a data set in Excel, Understanding Covariance and Correlation Matrices, Solving Exercises for various Descriptive statistics concepts.

Day - 12: 19-05-2019 Part - 1: Understanding Null and Alternate hypothesis, Left tailed, Right tailed and two tailed tests, Level of Significance and Confidence Interval, Traditional and P-value approahces of Hypothesis testings, Type 1 and type 2 errors

Day - 13: 25-05-2019 Part - 2: Understanding Degree of Freedom, Z - Test, t - Test and Chi - Square Test

Day - 14: 26-05-2019 Part - 3: Analysis of Variance and Understanding Various plots using Searborn

Day - 15: 02-06-2019 Probability: Introduction to probability, Trials, Sample space, Intersections - unions & Complements, Independent and dependent events and Conditional Probability

Day - 16 Hackathon Session Stats Revision

Day - 17: 16-06-2019 Linear Regression: Supervised Learning, What is Linear Regression?, find slope and intercept, Different ways to solve Linear Regression, Line of best fit method, Linear Algebra, Gradient Descent

Day - 18 17-06-2019 Linear Regression Practise: Model Validation, train-test, Cross-validation, Variance, Bias, variance-bias trade-off, Error Metrics, simple linear regression model practise in company salary dataset, cab price dataset and House price prediction.

practise in Kaggle :

Predict the insurance income :
Predict the count of bike taken as rent :
predict the valuecourse of lung cancer value :

Day - 19 22-06-2019 Big Mart Sales (Linear Regression Hackathon Practise): BigMart Sales Hackathon contest in AnalyticsVidhya

Did necessary preprocessing and predicted the result using the Linear regression and uploaded the result to Analytics Vidhya


Predict Restaurant food cost

Day - 20 23-06-2019 Logistic Regression theory and Practise: Converting the continuous to probablity, Cost Function - Log loss, Error Metrics - Confusion Matrix, Accuracy, recall, precision, F1-score, ROC curve, AUC.

Predicting gender of an employer, Predicting marketing subscription by a customer.

Day - 21 29-06-2019 KNN and Naive Bayes Algorithm: KNN working, Regression and classification, Why scaling mandatory, How to find optimal K value, Computational Complexity of O(N^2), Why KD Tree. Naive Bayes Working, Classification, Assumption of Naive Bayes, Bayes Theorem, Example

Assigment: Implement KNN algorithm in following kaggle datset

Predict the insurance income :
Predict the count of bike taken as rent :
predict the valuecourse of lung cancer value :
predict employee attrition :

Day - 22: 30-06-2019 Unsupervised Learning(K-means, Hierrachical Cluster): Un-Supervised Learning, K-means, K-means++, Within- Sum-of-Square, optimal K value, Elbow curve, Scaling Madatory, Heirarchical, Agglomerative, Dendogram

Assignment: Find the pattern of the credit card usage in the following kaggle dataset.

Day - 23: 06-07-2019 Decision Tree: Entropy, Information gain, Gini Index, Problems of Decision tree, Pruning, High Bias. Decision Tree Classification and Decision Tree Regressor.

Assigngment : Implement Decision Tree Classsification predict employee attrition :
Implement Decision Tree Regressor Predict the insurance income :
Predict the count of bike taken as rent :
predict the valuecourse of lung cancer value :

Day - 24: 07-07-2019 Ensemble: Bagging - Random Forest, Boosting - AdaBoost.

Assignment: Implement Bagging and Boosting

Day - 25: 13-07-2019 Stacking and SVM: Stacking - Example - Support vector machine - perceptron, kernels

Assignemnt : Implement SVM and Stacking

Day - 26: 14-07-2019 TimeSeries: ACF - PACF - Regression for Forecasting - Smoothing - SMA - WMA - EMA - AR - MA - ARMA - ARIMA

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Python (1,143,903
Jupyter Notebook (242,165
Python3 (33,410
Machine Learning (31,776
Data Science (9,208
Statistics (4,340
Numpy (3,502
Machine Learning Algorithms (2,401
Logistic Regression (1,240
Linear Regression (1,223
Random Forest (1,217
Decision Trees (852
Modelling (281
Machinelearning Python (216
Data Preprocessing (195
Bias (106
Bagging (59
Descriptive Statistics (48
Covariance (42
Practise (32
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