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DataScience_Mentorship

Course Material - Data Science Program

Author: Rajesh More

MODULE 1: PYTHON BASICS

MODULE 2: SQL

MODULE 3: MATH AND STATISTICS

MODULE 4: MACHINE LEARNING

MODULE 5: DEEP LEARNING

DURATION: 8 MONTH

Queries: PLEASE CONTACT 9992907777

Name		Name	Last commit message	Last commit date
Latest commit History 96 Commits
10_Python_Basics_Functions.ipynb		10_Python_Basics_Functions.ipynb
11_Python_Basics_Regular_Expressions.ipynb		11_Python_Basics_Regular_Expressions.ipynb
12_Python_Basics_Pandas.ipynb		12_Python_Basics_Pandas.ipynb
13_Python_Basics_Pandas_iloc_loc.ipynb		13_Python_Basics_Pandas_iloc_loc.ipynb
14_Python_Basics_Pandas_Manupulating_Data.ipynb		14_Python_Basics_Pandas_Manupulating_Data.ipynb
15_Python_Basics_Pandas_Sort_Groupby.ipynb		15_Python_Basics_Pandas_Sort_Groupby.ipynb
16_Python_Basics_Pandas_Merging_Joining_Concatenating.ipynb		16_Python_Basics_Pandas_Merging_Joining_Concatenating.ipynb
17_Python_Basics_Pandas_DateTime.ipynb		17_Python_Basics_Pandas_DateTime.ipynb
18_Python_Basics_Numpy_1.ipynb		18_Python_Basics_Numpy_1.ipynb
19_Python_Basics_Numpy_2.ipynb		19_Python_Basics_Numpy_2.ipynb
1_Math_Linear_Algebra.ipynb		1_Math_Linear_Algebra.ipynb
1_Python_Basics.ipynb		1_Python_Basics.ipynb
20_Python_Basics_Seaborn_Matplotlib.ipynb		20_Python_Basics_Seaborn_Matplotlib.ipynb
21_Python_Basics_Seaborn_Matplotlib.ipynb		21_Python_Basics_Seaborn_Matplotlib.ipynb
22_Python_Basics_Exception_Handling.ipynb		22_Python_Basics_Exception_Handling.ipynb
23_1_SQL_Datatypes_and_Operators.ipynb		23_1_SQL_Datatypes_and_Operators.ipynb
24_5_SQL_SELECT_statement_Part1.ipynb		24_5_SQL_SELECT_statement_Part1.ipynb
25_6_SQL_SELECT_Statement_Part2.ipynb		25_6_SQL_SELECT_Statement_Part2.ipynb
26_7_SQL_order_by_and_joins.ipynb		26_7_SQL_order_by_and_joins.ipynb
2_Math_Linear_Algebra.ipynb		2_Math_Linear_Algebra.ipynb
2_Python_Basics.ipynb		2_Python_Basics.ipynb
3_Math_Linear_Algebra_Gradient_Descent.ipynb		3_Math_Linear_Algebra_Gradient_Descent.ipynb
3_Python_Basics_Operators_and_output_statement.ipynb		3_Python_Basics_Operators_and_output_statement.ipynb
4_Math_Probability_Theory.ipynb		4_Math_Probability_Theory.ipynb
4_Python_Basics_Flow_Control.ipynb		4_Python_Basics_Flow_Control.ipynb
5_Python_Basics_Strings.ipynb		5_Python_Basics_Strings.ipynb
6_Python_Basics_List_Data_Structure.ipynb		6_Python_Basics_List_Data_Structure.ipynb
7_Python_Basics_Tuple.ipynb		7_Python_Basics_Tuple.ipynb
8_Python_Basics_Set.ipynb		8_Python_Basics_Set.ipynb
9_Python_Basics_Dictionary.ipynb		9_Python_Basics_Dictionary.ipynb
Advanced_Python_OOPS_Concept_Part1.ipynb		Advanced_Python_OOPS_Concept_Part1.ipynb
Advanced_Python_OOPS_Concept_Part2.ipynb		Advanced_Python_OOPS_Concept_Part2.ipynb
CarPrice_project.csv		CarPrice_project.csv
Data_Visualization_with_Plotly.ipynb		Data_Visualization_with_Plotly.ipynb
ML_01_Intrduction_To_Machine_Learning.ipynb		ML_01_Intrduction_To_Machine_Learning.ipynb
ML_03_Linear_Regression_part1.ipynb		ML_03_Linear_Regression_part1.ipynb
ML_04_Linear_Regression_Part2_Assumption_Checks.ipynb		ML_04_Linear_Regression_Part2_Assumption_Checks.ipynb
ML_05_Linear_Regression_Implementation(Part3).ipynb		ML_05_Linear_Regression_Implementation(Part3).ipynb
ML_06_Evaluation__Metrics_For_regression.ipynb		ML_06_Evaluation__Metrics_For_regression.ipynb
ML_07_Overfitting_Vs_Underfitting(Bias_Variance_Tradeoff).ipynb		ML_07_Overfitting_Vs_Underfitting(Bias_Variance_Tradeoff).ipynb
ML_08_Ridge_Vs_Lasso_Vs_Elastic(Regularization).ipynb		ML_08_Ridge_Vs_Lasso_Vs_Elastic(Regularization).ipynb
ML_09_Classification_in_Machine_Learning(Logistic_Regression).ipynb		ML_09_Classification_in_Machine_Learning(Logistic_Regression).ipynb
ML_10_Logistic_Regression_Implementation.ipynb		ML_10_Logistic_Regression_Implementation.ipynb
ML_11_Evaluation_Metrics_For_Classification.ipynb		ML_11_Evaluation_Metrics_For_Classification.ipynb
ML_12_Decision_Trees_Theory.ipynb		ML_12_Decision_Trees_Theory.ipynb
ML_13_Decision_Tree_Implementation(Regressor_and_Classifier).ipynb		ML_13_Decision_Tree_Implementation(Regressor_and_Classifier).ipynb
ML_14_Basic_Ensemble_Techniques.ipynb		ML_14_Basic_Ensemble_Techniques.ipynb
ML_15_Advanced_Ensemble_Techniques(Stacking).ipynb		ML_15_Advanced_Ensemble_Techniques(Stacking).ipynb
ML_16_Advanced_Ensemble_Techniques(Blending).ipynb		ML_16_Advanced_Ensemble_Techniques(Blending).ipynb
ML_17_Advanced_Ensemble_Techniques(Bagging)(Random_Forest).ipynb		ML_17_Advanced_Ensemble_Techniques(Bagging)(Random_Forest).ipynb
ML_18_Advanced_Ensemble_Techniques(Boosting)(XGBoost).ipynb		ML_18_Advanced_Ensemble_Techniques(Boosting)(XGBoost).ipynb
ML_19_K_Nearest_Neighbors.ipynb		ML_19_K_Nearest_Neighbors.ipynb
ML_20_Naive_Bayes_Algorithm.ipynb		ML_20_Naive_Bayes_Algorithm.ipynb
ML_21_Support_Vector_Machine(SVM_SVC).ipynb		ML_21_Support_Vector_Machine(SVM_SVC).ipynb
ML_22_Handling_Imbalanced_Data.ipynb		ML_22_Handling_Imbalanced_Data.ipynb
ML_23_Cross_Validation_and_Hyperparameter_Tuning.ipynb		ML_23_Cross_Validation_and_Hyperparameter_Tuning.ipynb
ML_24_Unsupervised_Learning_algorithms_(PCA).ipynb		ML_24_Unsupervised_Learning_algorithms_(PCA).ipynb
ML_25_Principal_Component_Analysis.ipynb		ML_25_Principal_Component_Analysis.ipynb
ML_26_Anomaly_Detection_Using_Isolation_Forest.ipynb		ML_26_Anomaly_Detection_Using_Isolation_Forest.ipynb
ML_27_K_Means_Clustering_Algorithm.ipynb		ML_27_K_Means_Clustering_Algorithm.ipynb
ML_28_K_Means_Clustering(Extra_Material)_.ipynb		ML_28_K_Means_Clustering(Extra_Material)_.ipynb
ML_29_Hierarchical_Clustering.ipynb		ML_29_Hierarchical_Clustering.ipynb
ML_2_Machine_Learning_Basics.ipynb		ML_2_Machine_Learning_Basics.ipynb
ML_30_Density_based_clustering_DBSCAN.ipynb		ML_30_Density_based_clustering_DBSCAN.ipynb
NLP_1_Introduction_To_NLP(Data_Cleaning).ipynb		NLP_1_Introduction_To_NLP(Data_Cleaning).ipynb
NLP_2_Natural_Language_Processing_Basics_(Feature_Extraction).ipynb		NLP_2_Natural_Language_Processing_Basics_(Feature_Extraction).ipynb
NLP_3_Topic_Modelling_Using_LDA.ipynb		NLP_3_Topic_Modelling_Using_LDA.ipynb
NLP_4_Topic_Modeling_with_LDA(Extra_Material).ipynb		NLP_4_Topic_Modeling_with_LDA(Extra_Material).ipynb
Pandas_Practice_Part1.ipynb		Pandas_Practice_Part1.ipynb
README.md		README.md
Statistics_1_Basics.ipynb		Statistics_1_Basics.ipynb
Statistics_2_Random_Variables.ipynb		Statistics_2_Random_Variables.ipynb
Statistics_3__Probability_Density_Functionipynb.ipynb		Statistics_3__Probability_Density_Functionipynb.ipynb
Statistics_4_PDF_and_Mass_Function.ipynb		Statistics_4_PDF_and_Mass_Function.ipynb
Statistics_5_Hypothesis_Testing_1.ipynb		Statistics_5_Hypothesis_Testing_1.ipynb
Statistics_6_Hypothesis_Testing2.ipynb		Statistics_6_Hypothesis_Testing2.ipynb
Statistics_7_T_Test.ipynb		Statistics_7_T_Test.ipynb
Statistics_8_Chi_Square_Test.ipynb		Statistics_8_Chi_Square_Test.ipynb
Statistics_9_ANOVA_Test.ipynb		Statistics_9_ANOVA_Test.ipynb
Superstore.xls		Superstore.xls
TS_1_Time_Series_Basics.ipynb		TS_1_Time_Series_Basics.ipynb
Unsupervised Learning Algorithms.pdf		Unsupervised Learning Algorithms.pdf

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