1. Basics (Data Bases, Tables, Data Base Management System(DBMS), SQL)

2. Working with Database Management System

• RDBMS (MySQL) ( SQL, Queries (DDL, DML, Joins, and
• Aggregations using Group by, Filters…) )
• NRDBMS (MongoDB) – Non-relational Databases (No-SQL,Queries (CRUD))

1. Modeling Basics

• Predictive Modeling & AI Introduction

2. Machine Learning (ML)

• Train-Test Split
• Supervised Learning & Its Algorithms
  • Regression
    • Linear Regression (Simple & Multiple, Polynomial Regression, Lasso(L1) & Ridge(L2))
    • Non-Linear Regression (Random forest,Decision Tree Regressor, Svm Regressor, etc. )

3. Classification

• Class Imbalance
• Logistic Regression
• SVM
• KNNNaïve Bayes
• Decision Tree
• Random Forest
• Boosting and Baggin

4. Model Evaluation Techniques

• Regression and Classification Metrics 
• Cross-validation
• Bias-Variance Trade off
• Hyper-Parameter Tuning
• Un- Supervised Learning & It’s algorithms
  • Dimensionality Reduction (PCA)
  • Clustering( K-means, H-Clustering, DBSCAN Clustering)

5. Deep Learning (DL)

• Introduction to Neural Networks
  • Neurons in Human Brain ( What are neural network, structure of Neural Network)
  • How Neural networks work (Feed Forward Neural Network)
  • Forward Propagation (Weights)
  • Backward Propagation (Optimization (Gradient Descent))

6. Types of Neural Networks

• Artificial Neural Network (ANN)
• Convolution Neural Network (CNN)
• Recurrent Neural Network (RNN)

7. ANN for Regression & Classification

8. Working with Images Data

9. Introduction to CNN

• CNN Architectures

10. Image Classification using CNN

11. Introduction to Object Detection

• Object Detection Vs Object Classification 
• OpenCv Object Detection

12. Object detection models

• Introduction to R-CNN, Faster R-CNN, YOLO etc….
• Introduction to transfer learning Custom Object
• Detection using YOLO recent version

13. Working with Text data (Natural Language Understanding (NLU))

• Collecting Text data according to Analysis
• Text validation & Cleaning (Tokenization, Stop words, Stemming/Lemmatization)
• Text Pre-Processing (Converting Text to Numeric vectors (N-grams, BOW , TF-IDF, Word2vec Word Embedding’s)

14. Language Modeling (Natural Language Generation (NLG))

Sentiment Analysis/Text Classification Predictive

Modeling using ANN/ML o Introduction to Sequence Models

• RNN Sequence data (Text/Time series)
• Using RNN for Sequence data- LSTM
• Model Types Based on RNN/LSTM
• Next word prediction/Translations/Q&A Predictive Modeling using RNN/LSTM

1. Introduction

2. Tableau

• Introduction
• Installation
• Data Sources
• Visualizations
• Filters
• Dashboard
• Story

3. PowerBi

• Introduction
• Installation
• Data Sources
• Power Query
• Visualizations, Interactions Measures
• DAX (Data Analysis Expressions)
• Filters
• Reports