Data Science/Artificial Intelligence CT Program-Holistic Learning Approach!!!
End-to-End Real-Time Project Demonstrations
End-to-End KPI Dashboard Creation with Power BI.mp4 Star Schema Design Using Power Query.mp4 End-to-End Data Analytics Project using Python, SQL & Power BI.mp4 End-to-End Data Analytics using AWS.mp4 Real-Time Project Demonstration on EDA.mp4 Real-Time Project Demonstration using Probability & Hypothesis.mp4 Data Warehousing End-To-End Using SnowFlake & Window Functions.mp4 Data Science End-To-End Project using SnowPark, Stored Procedures & Power BI!!!.mp4 Data Analytics & Data Science Roadmap
1. Data Analytics BEPEC Roadmap.mp4 2. Data Science BEPEC Roadmap.mp4 Mindmaps
Real-Time Project Procedure.pdf Machine Learning_Project Approach.pdf Machine Learning Math Deep Dive.pdf Data Science Interview Revision.pdf Data Analytics Project Procedure.pdf Python Programming_Mindmap.pdf SQL NOTES
SQL Interview Questions.pdf 1. Induction Classes
1. What is Data, type of data & Importance of data.mp4 2. What are math equations? Different math equations.mp4 3. Fourier Series & Fourier Transformation.mp4 4. History of Probability & Types.mp4 5. Why Linear Algebra for ML?(Easy way).mp4 6. Importance of Calculus.mp4 Applied Statistics with Excel
1-introduction-to-statistics.mp4 3-sample-vs-population.mp4 4-types-of-sampling-techniques.mp4 6-analysing-data-with-eda-using-excel.mp4 7-measures-of-dispersion.mp4 9-correlation-scatter-plot.mp4 Mastering Python(New Version)
introduction-to-python.mp4 1-introduction-to-python.mp4 3-python-arithmetic-operators.mp4 5-python-data-structures.mp4 6-python-control-flow.mp4 different-list-commands.mp4 different-tuple-commands.mp4 different-set-commands.mp4 different-dict-commands.mp4 shallow-copy-deep-copy.mp4 string-modulo-operator.mp4 conditional-statements.mp4 user-defined-functions.mp4 keyword-positional-default-arguments.mp4 variable-length-arguments.mp4 local-global-variables.mp4 Introduction to Pandas.mp4 2.8.+Skewness_lesson.xlsx Statistics using Python.ipynb Marketing_conversion_data.xlsx Assignment DataSet - Implement Stats on this Dataset Statistics using Python.mp4 Python Stats Recap with Project Approach.pdf Automobile price data _Raw_.csv Statistics with Python -2.mp4 Lending Data Assignment.pdf Data Cleaning_Complete-2-3.ipynb Pandas Deep Dive-2 (1).ipynb Data Visualization with Seaborn-3.ipynb Data Visualization with Seaborn and Plotly.mp4 Different Numpy Commands.mp4 Customer Analytics Roadmap_BEPEC (1).pdf Confidence Interval & Hypothesis Testing-3.ipynb Types of Probability Distribution.ipynb Statistics Mindmap (1).pdf Real-Time Project with Confidence Interval & Probability.mp4 Statistical Testing (1).pptx Hypothesis Testing-3.ipynb Moving Average Forecasting.ipynb Forecasting Theory + Coding.mp4 Basics of Python v3-6.ipynb Python Basics Day - 4-TUPLES.ipynb python Basics Day - 4 - SETS.ipynb Python Basics Day - 8 (Lambda function)-2.ipynb python-Basics Day - 5 - DICT-2.ipynb 2. Python
Python basics day-1 (Intro to Python) (1).ipynb Python basics day -2 .ipynb Python Basics Day - 4-TUPLES.ipynb Python Basics Day- 3 - LIST.ipynb python Basics Day - 4 - SETS.ipynb python-data-structures-05-STRINGS.ipynb python-Basics Day - 5 - DICT.ipynb Python Basics Day - 6 (operators).ipynb python-Basics Day -7-functions (part-1).ipynb Python Basics Day - 6 - (Conditional - loops).ipynb Python Basics Day- 7 functions (part 2) .ipynb Python Basics Day - 8 (Lambda function) - Copy.ipynb Python Basics Day - 9 (Escape sequence).ipynb Hackathon-1(Questions).ipynb Hackathon-1(Answers).ipynb Session-17.1[Lecture-1 OOPs].mp4 Session-17.2[OOPS Part-1] python Advance Day(11,12) Class & objects.ipynb (Encapsulation, Abstraction,Inheritance,Polymorphism).ipynb Overloading,Overriding.ipynb Basics of Pandas_(part-1).ipynb Hackathon-2(Questions).ipynb Hackathon-2(Answers).ipynb Data visualization table.png Data visualization with Matplotlib.ipynb Data Cleaning using Sklearn & Pandas.ipynb Data Cleaning_Complete.ipynb Automobile price data _Raw_.csv 3. Statistics with Real-Time Project Demonstration on EDA
Basics of Statistics, What is Data? What are Features Real-Time Project Demonstration on EDA with Real-World Project regulalr_expressions_basics.ipynb Customer Analytics Roadmap_BEPEC.pdf Real-Time Project on EDA.ipynb 1. Why Statistics and When.mp4 2. Descriptive Statistics.mp4 3. Descriptive Stats using Python.mp4 Continuous Probability Distribution.mp4 Law of Large Numbers and CLT.mp4 Hypothesis Testing Part-1.mp4 4. Project-1: Data Analytics Project to Improve Employee Efficiency
Project Details, What to do? What to Submit? INX_Future_Inc_Employee_Performance_CDS_Project2_Data_V1.8.xls 5. Probability Distributions, Hypothesis Testing with Real-Time Project Demonstration
Probability Distribution.mp4 Real-Time Project Demonstration on Probability Distribution & Hypothesis Testing How to calculate p-value?Basics of Hypothesis Coding Probability Distribution, Hypothesis.pdf Real-Time Project on Probability Distribution & Hypothesis Testing.ipynb How to solve PDF-CDF-PMF.mp4 Chebyshevs, Log, Power Law, Q-Q, CLT.mp4 6. Tableau
Dashboard Explanations,Do_s_Dont_s of a Dashboard -Connecting With Cloud Era.mp4 Day 1 Introduction to Tableau.mp4 Day 2 Principles Of Data Visualization.mp4 Day 3 Data Interpretation,Pivot,Split Tables.mp4 Day 4 Time Series,Dual Axis Charts,Usage of Markscard.mp4 Day 5 Bar Plot,Stacked Bar Plot,Color Encoded,Nested Bar Plots,Customized Sql,Traditional Context.mp4 Day 6 Bullet Graphs,Correlation Analysis,Scatter Diagram,Prediction Model.mp4 Day 7 Part -1 Histogram,Varies Bin Sizes.mp4 Day 8 Part -2 What is box plot..etc.mp4 Day 9 Text Tables,Tree Maps,Circle Charts,Word Cloud,Bubble Chart,Field Map,Symbol Map,Action in Maps.mp4 Day 10 Cal - Part -1 Pareto Charts,Quick Table Calculations,Table Calculations,Lookup Z-N Functions.mp4 Day 11 Cal - Part -2 Else If Functions,Left,Right Manipulation,Parameters,Sets.mp4 Day 12 LOD Expressions.mp4 Project Work: E-Commerce.docx Project Work: Products.xlsx Tableau Interview Questions.pdf 7. Power BI
1-introduction-to-power-bi.mp4 2-creating-a-bar-plot.mp4 4-creating-ribbon-chart.mp4 5-creating-scatter-plot.mp4 6-creating-waterfall-charts.mp4 7-creating-funnel-chart.mp4 8-creating-line-plot-area-plot.mp4 9-creating-matrix-conditional-formatting.mp4 10-creating-decomposition-tree.mp4 12-creating-gauge-card.mp4 14-creating-animated-bar-plot.mp4 15-creating-sunburst-chart.mp4 16-different-filers-in-power-bi.mp4 17-include-exclude-operations-in-power-bi.mp4 18-introduction-to-power-query.mp4 19-groupby-replace-in-power-query.mp4 20-merge-and-append-operations.mp4 21-prefix-suffix-length.mp4 22-pivot-in-power-query.mp4 23-introduction-to-dax-expressions.mp4 24-creating-dax-measures.mp4 25-creating-dax-columns.mp4 26-more-dax-expressions.mp4 27-publishing-power-bi-visualizations.mp4 [Real-Time]End-to-End Data Analytics Project Pipeline power-query-data-cleaning.mp4 8. MySQL
1. Introduction to MySQL.mp4 2. Creating DataBases in MySQL.mp4 3. Deep Dive into MySQL Interface.mp4 5.1. Clauses in MYSQL.mp4 5.2. Clauses in MySQL -2.mp4 5.3. Clauses in MYSQL-3.mp4 6.1. MYSQL Data Handling -1.mp4 6.2. MYSQL Data Handling -2.mp4 7. MYSQL Aggregate Functions.mp4 MySQL End-to-End
Different Join with MySQL Advanced MySQL
1. Data Integrity & Referential Integrity.mp4 2. Data Normalization.mp4 3-first-second-normal-form.mp4 4-functional-dependency-transitive-dependency-3rd-normal-form.mp4 5-boyce-codd-normal-form.mp4 7-temporary-table-cte-r-cte.mp4 8-when-to-use-tt-cte-r-cte.mp4 15-different-functions.mp4 16-different-ddl-commands-indexes.mp4 9. Unsupervised Learning with Real-Time Demonstration
Fundamentals of ML Part-1.mp4 Fundamentals of Machine Learning Part-2.mp4 Insight's from Train & Test Accuracy.mp4 Problem Identification & Approach Designing for Data Science Projects Associate Rules Scripting Data Analytics Project Approach.mp4 Recommendation Engine.pdf Associate Rules Script.ipynb Why to do Dimensionality Reduction? Project Implementation on PCA, T-SNE PCA Calculation Step by Step.ipynb T-SNE Math & Coding.ipynb 10. Supervised Learning: Linear Regression Real-Time
Linear Regression Material - Applied ML(BEPEC).pdf Flowchart of Supervised Learning with Different ML Methodologies Simple Linear Regression & Multiple Scirpt.py Different ML Equations like Loss Functions, Optimisers, Activation, Model Architecture Linear Regression Evaluation.mp4 Linear Regression Script End-to-End.mp4 Linear Regression Model Accuracy Improving Techniques & Evaluation Metrics Automobile price data _Raw_.csv Regression using Tensorflow 2x0.ipynb Tensorflow 2x0 Classification Model.ipynb 11. Advanced ML & Deep Learning with Real-Time Project
Logistic Regression Math.mp4 Classification Script End-to-End.mp4 ML/DL Algorithm Flow Chart, Which makes your learning so easy(Ice Breaker).mp4 Logistic Regression_Script,KNN, PCA.py Logistic Regression Material - Applied ML(BEPEC) copy.pdf Regression Algorithms Blueprint.ipynb Difference b/w Regression vs Classification.mp4 ML Blueprint Detailed.ipynb Decision Tree Maths Deep Dive.mp4 Introduction to Gradient Boosting.mp4 SVM,Logistic, KNN & DT.ipynb Decision-Tree Learning . (1).pptx Hyper Parameter Tuning.pdf Support Vector Machines.pptx SVM,Logistic, KNN & DT (1).ipynb Data Science Process-V1.docx 120 finalized Questions of machine learning.docx 12. Deep Learning Foundation(Neural Networks)
1-introduction-to-neural-networks.mp4 2-neural-network-architecture.mp4 3. Different Optimizers.mp4 4-different-activation-functions.mp4 5. Different Loss Functions.mp4 Deep Learning Introduction .pdf 13. R Programming
Introduction to Data Science.mp4 Installation of R _ R Studio.mp4 Data Visualization with R Studio.mp4 Data Modelling with R Studio.mp4 Data Joining with R Studio.mp4 Data Visualization with ggplot2.mp4 Data Preparation with RStudio.mp4 Data Cleaning with RegExp.mp4 Company Casestudy_ Data Visualization.mp4 Data Visualization with Plotly.mp4 Data Cleaning with Regexp.R Data Joining with DPLYR.R Decision Tree_Item classification.R Diabetes Case Study_Logistic Regression.R Hiring challenges_Random Forest Script.R Linear Regression_Sales Prediction.R KNN_CaseStudy on Credit.R Linear Regression Simple _ Multiple.R KNN - Train Accuracy _ Test Accuracy.R KNN_Normalization Script.R Logistic Regression Script_Claims.R Decision Tree_Credit with Factor Conversion.R Cancer Casestudy_Logistic Regression.R Data Visualization with R Studio.R Linear Regression_Construction.R Data Visualization with ggplot2.R Data Preparation with R.R 15. POC on ML from Problem Identification to Deployment Demonstration
Deploying Webapps using Streamlit(Easy way for Non-Technical Learners).mp4 14. Advanced Time Series with Real-Time Project Demonstration
1.1. Introduction to Time Series Modelling.mp4 1.2. Time Series Modelling - 2.mp4 2.1. Characteristics of Time Series Modelling.mp4 3.1. Time Series Modelling Techniques.mp4 5. Coding Time Series Modelling using R.mp4 Simple Time Series Project End-to-End.mp4 Simple Time Series Forecasting .ipynb LSTM Example on Cashflow. ipynb End-to-end project on Time Series Modelling.mp4 Forecasting Script End-to-End.ipynb 5. Time Series Modelling using RNN.mp4 Time-Series Modelling.pdf 15.1 Pycaret, Docker, MLFlow, Gradio, FastAPI, SHAP
1. Setup the Environment.mp4 2. Docker Installation.mp4 3. How to activate virtual environment.mp4 5. Regression Models Using Pycaret.mp4 6. Regression Models Using Pycaret Part II.mp4 7. Interpretability Of Models Using SHAP_1.mp4 9. Application Development With Gradio.mp4 10. Creating API With Pycaret & FastAPI.mp4 11. Creating a Docker Image.mp4 12. Model Versioning Pycaret & MLflow.mp4 Notebook_API creation with Pycaret and FastAPI (1).ipynb Regression Models with Pycaret (1).ipynb Model registration and versioning with MLFlow (1).ipynb MLOps with Pycaret and MLflow (1).ipynb Create container for an API (1).ipynb Interpretability of models with SHAP (1).ipynb 1. Application development with Gradio (1).ipynb 15.2. Statistics, ML, NLP Theory Interview Questions
Data Science Interview Revision.pdf Machine Learning Math Deep Dive.pdf 1. How to Prepare Machine Learning Math for Interviews.mp4 3. What is Linear Regression.mp4 4. CaseStudy on Linear Regression.mp4 5. Math behind Linear Regression - 1.mp4 6. Math behind Linear Regression - 2.mp4 7. Math behind OLS Linear Regression.mp4 8. Assumptions of Linear Regression.mp4 9. Evaluation Metrics of Regression Models.mp4 10. Accuracy Improving Techniques.mp4 11. Regularization Techniques.mp4 End-to-End Linear Regression Coding Part-1 End-to-End Linear Regression Part-2 1. Why Logistic Regression.mp4 2. Math behind Logistic Regression.mp4 3 Evaluation Metrics behind Classification Algorithms.mp4 End-to-End Logistic Regression Coding.mp4 Cross Validation Coding.mp4 ROC & AUC Curve Coding.mp4 5. Introduction to Decision Tree.mp4 6. Intuition Behind Decision Tree.mp4 7. Math Behind Decision Tree.mp4 8. Math behind Decision Tree using GINI.mp4 9. Drawbacks of Decision Tree.mp4 10. Random Forest & Gradient Boosting.mp4 Plotting Decision Tree Coding.mp4 Hyperparameter Tuning Techniques Coding.mp4 11. Handling Imbalanced Dataset.mp4 12. Feature Selection Techniques.mp4 Feature Selection Techniques Coding.mp4 Wrapper Method Coding.mp4 OverSampling & UnderSampling Coding.mp4 What Matters in Interviews.mp4 1. Introduction to PCA.mp4 4. Math behind SVM Part-1.mp4 5. Math behind SVM Part-2.mp4 7. Classification using AutoML.mp4 1_Introduction to NLP & NLP Applications.mp4 3_NLP Pre-Processing Steps.mp4 4_NLP Feature Extraction.mp4 5_NLP Practical Example.mp4 End-to-End ML Deployment using AWS.mp4 16. AWS Sagemaker Studio with MLOps Tutorial with Investment Domain Real-Time Project Demonstration
Setting up AWS Sagemaker & Deploying Simple Model on Sagemaker.mp4 AIOps with AWS Sagemaker Studio.mp4 ML End-to-End Project on Sagemaker Studio.ipynb Deployment using Sagemaker 17. CMLA [Certified Machine Learning Architect]
Interview Questions from Data Scientist Corporate Lifecycle Building Project Charter, Network Diagrams for Data Science Project Model Evaluation & Model Deployment on Web, Mobile on Premise.mp4.mp4 Interview Questions from Model Deployment using Heroku, Mobile Deployment and Project Closing .mp4 multiclass_svm_tensorflow.py Pre-Processing_Sklearn.py Serialisation and deserialisation.py time_series_data_preprocessing.ipynb 1.01_PCoE_Project_Charter_Guide.doc 2.05_PCoE_Assumption_and_Constraint_Log_Guide.doc 2.06_PCoE_WBS_Diagram.doc Decision Tree_Pickle Model Saving.py 18. NLP, NLU with Real time Project
1. Introduction to Text Mining.mp4 2. NLP Processing Deep Dive.mp4 3. NLP Classification Coding.mp4 4. Language Identification using R.mp4 5. Phrase Extraction .mp4 NLU using RNN(NLP Part-2).ipynb Word2Vec - Spam & Non-Spam.ipynb Spam & Non_spam Classification-2.ipynb BEPEC - DT_Project CRM.pdf 19. SoftSkills for Interviews
1. Why Interview skills are important.mp4 2. How to choose right course?.mp4 3. Do's & Don't while marketing your profile.mp4 4. Constructing Great Resume.mp4 5. Do's & Don'ts in Linkedin/Naukri/Job Portals.mp4 6. 3 Elements to crack any interviews.mp4 20. Interview Preparation for Data Science
1. What is Data, type of data & Importance of data.mp4 2. What are math equations? Different math equations.mp4 3. Fourier Series & Fourier Transformation.mp4 4. History of Probability & Types.mp4 5. Why Linear Algebra for ML?(Easy way).mp4 6. Importance of Calculus.mp4 How to solve PDF,CDF, PMF.mp4 Chebyshev's Inequality, Log Transformations, Power Law Distribution, Central Limit theorem.mp4 How to choose right ML Algorithm? What is Reinforcement Learning? And Why?.mp4 How to grow model accuracy?.mp4 Tips & Tricks to prepare ML Algorithms.mp4 What is Feature Engineering.mp4 Setting up AWS Sagemaker & Deploying Simple Model on Sagemaker.mp4 deploy_to_sagemaker.ipynb How to speak in Data Science Interview and Interview Process..m4a How to speak in Data Science Interview.m4a How to build Linkedin Portfolio & Real-Time Project Experience.mp4 Resume Preparation & Data Science Interview Preparation.mp4 How to start with data scientist the math company data scientist.mp3