What you’ll learn

  • Understand Data Science & Machine Learning
  • Python Programming Language (beginner to master)
  • R Programming Language (beginner to master)
  • Big Data Overview
  • Hadoop, YARN – Architecture
  • Spark / PySpark – Over View and Architecture
  • RDD and Data Frame Hands on Practices using PySpark – Spark Core, Spark SQL
  • Statistics & Mathematics Basics using Python , R & Spark
  • Machine Learning Algorithms (Deeper Insights – Master Level ) – Linear Regression, Linear Regression Assumptions , Logistic Regression,AUC & ROC Curves , Decision_Trees, Clustering and More…
  • Implementing Machine Learning Algorithms using Python
  • Implementing Machine Learning Algorithms using R
  • Implementing Machine Learning Algorithms using Spark (Big Data) – Spark MLlib
  • Natural Language Processing(NLP), Text Mining
  • NLP using Python , R and Spark
  • Deep Learning
  • End to End Implementation and Maintenance of Data Science / Machine Learning Algorithms in Production
  • How to attend Data Science Interviews and build your CV

Introduction to Data Science & Machine Learning

Introduction to Data Science & Machine Learning

 

Course Over View

DS-ML-Technologies & Course_OverView

 

Data Scientist _Scope of Tools_Technologies & Big Data

Data Scientist _Scope of Tools_Technologies & Big Data

 

Data Sets Needed For All of this Course

Data Sets Needed For All of this Course

 

Code Base

Code Base

 

All Assignments for the Course

All Assignments for the Course

 

Learn Python for Beginners

Download & Install Python
Anaconda Python_R Distribution
Anaconda Navigator
Spyder (Python 3.7)
Spyder (Python 3.7)_01
ADvantagesOfPython_Spyder
Python_DataTypes_Dynamic_Typecasting
Python_String_Int_DataTypes
Python_StringTypes_INT_STRING
Python_DataTypes_Tuple
Python_DataTypes_List
Python_DataTypes_ListInsideTuple_TupleInsideList
Python_Dictonary
Python_Set
Python_Operators
Python_Conditions&Loops_LoopControl
Python_KeyBoard_Input
Python_FileOperations_1
Python_FileOperations_2
ALL_Python_ Basics & Variables and Data Types and basic programming
All_02_Python_ Basics & File Operations _Exception Handling_Debugging

 

Learn Python Level-2

Python_Functions
Python_OOPS_Introduction to_Class_Object_Method
Python_OOPS_Part-1
Python_Modules & Libraries in Python

 

Basics of Statistics for Data Science

Stats_Beginning_01
Stats_Beginning_02
22_Stats_Beginning_02_Updated
23_Stats_FrequencyDistributionsandHistogram_Updated
23_Stats_MeasuresofcentralTendency_Updated
24_Stats_Dispersion_Updated
Stats_Frequency Distributions and Measures of central Tendency
Stats_Frequency Distributions and Measures of central Tendency_01
Stats_Dispersion
Stats_Type Of Variables
Stats_Data Types And Scales
Stats_Sampling Techniques_01
Stats_Sampling Techniques_02
Stats_Sampling Techniques_WithReplacementandwithoutReplacement
Stats_Hypothesis_Testing
Stats_Hypothesis_Testing_02

 

Learn Python for Statistics

Python_Stats_Basics_01
Python_Basic_Stats_02
Python_Basic_Stats_03

 

Python Data Handling using Pandas library

Python_Pandas-01
Python_Pandas-02

 

Learn R for Beginners

Install R and R Studio
02_R_Working with R Multiple Versions
03_R_Start with R Programming
01_R_R Studio Installation & Upgrading Version for Dependencies_02_CRAN R

 

Learn R (programming language)

04_R_Data Types_Integer_Numeric_Logical_Character_Factor_Complex_Date
05_R_DataStructures_Vectors_matrix_array_lists_data frames
06_R_Functions_Argument Types_Apply
07_R_ApplyFamily
07_R_ApplyFamily_01
08_R_Work With Packages _ library in R
09_R_Library_Dependency with R version
10_Split_Data into Train and testing using R

 

Big Data & Big Data Technologies

01_BigData_Introduction

 

Hadoop

02_BigData_Hadoop_Introduction
03_BigData_Hadoop_Architecture
04_BigData_Hadoop_OverView
05_BigData_MapReduce_Architecture
06_BigData_YARN & YARN Architecture
07_BigData_hadoop_ClusterModes

 

Learn Apache Spark

08_BigData_Limitations_Of_MapReduce
09_BigData_Spark_Introduction&OverView
09_BigData_Spark_Introduction&OverView_02
10_BigData_Spark_FrameWork&ExecutionModes
11_BigData_ExecutionModes_YARN_Mode
13_BigData_SPARK_Typical_Archetecture of Big Data_Technologies_ and Industry Sta

 

Installing Hadoop & Spark in your machine

Installing Hadoop & Spark in your machine
14_BigData_INSTALL_Hadoop _ SPARK_Using Sandbox
14_BigData_INSTALL_Hadoop _ SPARK_Using Sandbox_01

 

Transformations using Spark Core- RDD and Spark SQL-Data Frames

12_BigData_SPARK_API’s_RDD_DataFrame_DataSet_Introduction
15_BigData_SPARK_Transformation_Actions_Practise_PySparkShell
16_BigData_SPARK_DataFrames_SparkSQL_Jupyter
17_BigData_SPARK_Transformation_Actions_using RDD_Jupyter
18_BigData_Spark_SQL_Hive Integration
19_BIgData_PySpark_RDD_Transformations and Actions_Operations_PySpark
20_BIgData_PySpark_DataFrame_Operations_PySpark

 

Machine learning_Linear Regression

ML_Linear Regression_Introduction
ML_Linear Regression_Introduction_02
ML_Linear Regression_03
ML_Linear Regression_04
ML_LinearRegression_05
ML_LinearRegression_with_Python_01
ML_LinearRegression_with_Python_02_Split_Test&Train Data
ML_LinearRegression_with_Python_02_using SMF Library _Assumptions
ML_LinearRegression_with_Python_02_using SMF Library _Assumptions_02
ML_LinearRegression_Summery_Metrics
01_BigData_ML_LinearRegression_DecessionTreeRegressor_PySpark
02_BigData_ML_LinearRegression_PySpark
01_ML_R_Linear Regression
02_ML_R_Linear Regression_02
03_ML_R_Linear Regression_Regression Assumptions
04_ML_R_Linear Regression_Regression Assumptions_02
04_ML_R_Linear Regression_Regression Assumptions_Address the Assumptions_03

 

Machine learning_Logistic Regression

ML_Logistic_Regression_01
ML_Logistic_Regression_02
ML_LogisticRegression_Python_ROC_AUC
03_BigData_ML_LogisticRegression_Mllib-Pipeline_PySpark
01_ML_R_LogisticRegression_AUC and ROC
02_ML_R_Logistic Regression_02
02_ML_R_LogisticRegression_Build ConfusionMATRIX and Accuracy_Pricision Recall

 

Machine Learning_Model_Evaluation Metrics & Techniques

Machine Learning_Model_Evaluation Metrics & Techniques
Some Terminologies Of Machine Learning

 

Machine Learning_Under Fit_Good Fit _ Over Fit_Cross Validation Methods

ML_UnderFit_Fit_OverFit_CrossValidationMethods
ML_UnderFit_OverFit_Cross validation_ParmTuning_Purning
01_ML_R_Cross Validation_Manual_k fold
02_ML_R_Cross Validation_LOOCV Approach_K-Fold Approach_R
02_ML_R_Tree_Pruning_OverFit_R

 

Machine Learning_K Nearest Neighbor_Classifier

K Nearest Neighbor_Classifier_KNN
K Nearest Neighbor_Classifier_KNN_Python
01_ML_R_K Nearest Neighbors _KNN

 

Machine Learning_ Decision _Trees

Machine Learning_ Decision _Trees
Machine Learning_ Decision _Trees_02
Machine Learning_ Decision _Trees_03
Bagging_Random Forest
Random Forest for Regressor and Classifier _Python
Random Forest for Regressor and Classifier _Python_02
4_BigData_ML_DecisionTreeClassifier_RandomForestClassifier_GBTClassifier_PySpark
01_ML_R_ Decision Tree_R Library
02_ML_R_ Decision Tree_Classification
03_ML_R_ Decision Tree_Regression
02_ML_R_Tree_Pruning_OverFit_R

 

Machine Learning_Ensemble learning

Ensemble learning_Bagging
Ensemble learning_Boosting
ML_UnderFit_Fit_OverFit_CrossValidationMethos
TreePruning_HyperParmeter Tuning
RandomForestRegressor_Graphviz_Python
04_BigData_ML_DecisionTreeClassifier_RandomForestClassifier_GBTClassifier_PySpar
01_ML_R_RandomForest_AdaBoost_GradientBoost_DecissionTree_Ensembler_Models
02_ML_R_TreeModels_Classification_Evtree_ObliqueRF

 

Machine Learning_Un-SuperVised Learning Clustering_Kmeans

Un-SuperVised Learning Clustering_Kmeans
Un-SuperVised Learning Clustering_Kmeans_Python
07_BigData_ML_UnSupervised Learning_Clustering_KMeans
01_ML_R_K-means_Clustering
02_ML_R_K-means_centroid and Clustering_Clustering
03_ML_R_Hierarchical Clustering_Clustering

 

Machine Learning_Support Vector Machine(SVM) & Naive Bayes Classifier

Support Vector Machine(SVM) Classifier
Support Vector Machine(SVM) Classifier_01
Support Vector Machine(SVM) Classifier_02
Support Vector Machine(SVM) Classifier_03
Naive Bayes & Support Vector Machine(SVM) Classifier_04
ML_SVM classifier_VS_LogisticRegression_VS_DecisionTreeClassifier_VS_KNeighborsC
01_ML_R_Support Vector Machines_SVM
01_ML_R_NaiveBayes_FlightDelayes
02_ML_R_Naive Bayes car-theft
03_ML_R_Bayesian network

 

Machine Learning_Un-Supervised Learning_Dimensionality Reduction

Principal Component Analysis(PCA)_Python
01_ML_R_principal component analysis

 

Machine Learning_Un-Supervised Learning_Association Rule Mining

Un-Supervised Learning_Association Rule Mining_intro
Un-Supervised Learning_Association Rule Mining
Un-Supervised Learning_Association Rule Mining_01
Un-Supervised Learning_Association Rule Mining_02
02_ML_R_Association Rules_Apriory
03_ML_R_AssociationRules_Apriory_02
01_ML_R_Recommenderlab_R
04_ML_R_Recommenderlab_WineData
05_ML_R_Recommenderlab_MovieRating Data

 

Machine Learning_DeepLearning_NeuralNetowrks

DeepLearning_NeuralNetowrks
Machine Learning_DeepLearning_NeuralNetowrks_Python
01_ML_R_neuralnetwork_DeepLearning

 

Advanced Machine Learning Concepts

ML_GridSearchCV_RandomizedSearchCV_Python
5_BigData_ML_Classification CrossValidation and Hyperparameter Tuning_GridSearch
06_BIgData_PySpark and MLlib_End to End Implimenation

 

Machine Learning_Time Series

01_ML_TimeSeries_Intro
01_ML_TimeSeries vs Regression_01
02_ML_TimeSeries_Components in Time Series Data
03_ML_TimeSeries_Components & Algorithms in Time Series Data
04_ML_TimeSeries_Components Visualize the Time Series Data
05_ML_TimeSeries_AR_MA_ARIMA
06_ML_TimeSeries_ACF and PACF
Time Series with Python
01_ML_R_TimeSeries_ARIMA
02_ML_R_TimeSeries_ETS_HoltWinters
03_ML_R_TimeSeries_Multiple_DataSets_ARIMA

 

Machine Learning_EndToEnd_Implementation_Of_Model_Building

Machine Learning_EndToEnd_Implementation_Of_Model_Building
Machine Learning_Save and Load the model in PRODUCTION Environment _Python

 

Text Mining ( Natural Language Processing)

01_ML_NLP_NLP_Into
02_ML_NLP_Flow
03_ML_NLP_NLP Related Topics and Steps
04_ML_NLP_Algorithms
05_ML_Machine Learning – Algorithms for Classification of Text Data
06_ML_NLP_Positive and Negative Words_Dictionary

 

NLP With Python

NLP With Python

 

NLP with R

01_ML_NLP_R_Twitter-Sentiment Analysis

 

NLP with Spark

08_BigData_NLP_Spam Detector_NaiveBayes and Text Pre Processing for NLP _Pyspark

 

How to Build your CV and Attend Interviews for Data Science

Data Science_how to _Build CV_Attend Interviews

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