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- What is Python?
- Why does Data Science require Python?
- Installation of Anaconda
- Understanding Jupyter Notebook
- Basic commands in Jupyter Notebook
- Understanding Python Syntax
- Variables and Strings
- Lists, Sets, Tuples, and Dictionaries
- Conditional Operators, Arithmetic Operators, and Logical Operators
- If, Elif and Else Statements
- While Loops
- For Loops
- Nested Loops and List and Dictionary Comprehensions
- What is function and types of functions
- Code optimization and argument functions
- Scope
- Lambda Functions
- Map, Filter, and Reduce
- Create, Read, Write files and Operations in File Handling
- Errors and Exception Handling
- Create a class
- Create an object
- The __init__()
- Modifying Objects
- Object Methods
- Self
- Modify the Object Properties
- Delete Object
- Pass Statements

**Data Types and Data Structures**

**Control Flow and Conditional Statements**

**Functions**

**File Handling**

**Class and Objects**

**Numpy – NUMERICAL PYTHON**

**2. Data Manipulation with Pandas**

**Data Visualization using Matplotlib and Pandas**

**Case Study on Exploratory Data Analysis (EDA) and Visualizations**

**Regular Expressions**

**PROJECT ON WEB SCRAPING: DATA MINING and EXPLORATORY DATA ANALYSIS**

**Data Types and Data Structures**

**Data Gathering Techniques**

**Descriptive Statistics**

**Probability Distribution**

**Inferential Statistics**

**Introduction to Databases**

- DML, DDL, DCL, and Data Types
- Common SQL commands using SELECT, FROM, and WHERE
- Logical Operators in SQL

- INNER and OUTER joins to combine data from multiple tables
- RIGHT, LEFT joins to combine data from multiple tables

- Advanced filtering using IN, OR, and NOT
- Sorting with GROUP BY and ORDER BY

- Common Aggregations including COUNT, SUM, MIN, and MAX
- CASE and DATE functions as well as work with NULL values

- Subqueries to run multiple queries together
- Temp tables to access a table with more than one query

- Perform Data Cleaning using SQL

**Linear Regression**

**Multiple Linear Regression**

**Evaluating the Metrics of Regression Techniques**

**Polynomial Regression**

**Regularization Techniques**

**CAPSTONE PROJECT:** A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Regression Techniques.

**Logistic regression**

**Naive Bayes**

- Principle of Naive Bayes Classifier
- Bayes Theorem
- Terminology in Naive Bayes
- Posterior probability
- Prior probability of class
- Likelihood
- Types of Naive Bayes Classifier
- Multinomial Naive Bayes
- Bernoulli Naive Bayes and Gaussian Naive Bayes

**Decision Trees**

- Decision Trees (Rule-Based Learning):
- Basic Terminology in Decision Tree
- Root Node and Terminal Node
- Regression Trees and Classification Trees
- Trees Versus Linear Models
- Advantages and Disadvantages of Trees
- Gini Index
- Overfitting and Pruning
- Stopping Criteria
- Accuracy Estimation using Decision Trees

**Case Study: **A Case Study on Decision Tree using Python

- Resampling Methods:
- Cross-Validation
- The Validation Set Approach Leave-One-Out Cross-Validation
- K-Fold Cross-Validation
- Bias-Variance Trade-O for K-Fold Cross-Validation

**Ensemble Methods in Tree-Based Models**

- What is Ensemble Learning?
- What is Bootstrap Aggregation Classifiers and how does it work?

**Random Forest**

- What is it and how does it work?
- Variable selection using Random Forest

**Boosting: AdaBoost, Gradient Boosting**

- What is it and how does it work?
- Hyper parameter and Pro’s and Con’s

**Case Study:** Ensemble Methods – Random Forest Techniques using Python

**K Nearest Neighbors**

- K-Nearest Neighbor Algorithm
- Eager Vs Lazy learners
- How does the KNN algorithm work?
- How do you decide the number of neighbors in KNN?
- Curse of Dimensionality
- Pros and Cons of KNN
- How to improve KNN performance

**Case Study:** A Case Study on KNN using Python

**Support Vector Machines**

- The Maximal Margin Classifier
- HyperPlane
- Support Vector Classifiers and Support Vector Machines
- Hard and Soft Margin Classification
- Classification with Non-linear Decision Boundaries
- Kernel Trick
- Polynomial and Radial
- Tuning Hyper parameters for SVM
- Gamma, Cost, and Epsilon
- SVMs with More than Two Classes

**Case Study:** A Case Study on SVM using Python

**CAPSTONE PROJECT:** A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Classification Techniques.

**Principal Components Analysis**

**Case Study:** A Case Study on PCA using Python

**K-Means Clustering**

**Hierarchical Clustering**

**Case Study:** A Case Study on clusterings using Python

**Association Rules**

**Apriori**

**Case Study:** A Case Study on association rules using Python

**CAPSTONE PROJECT:** A project on a use case will challenge the Data Understanding, EDA, Data Processing, and Unsupervised algorithms.

**RECOMMENDATION SYSTEMS**

- a)Sigmoid b)Relu c)Softmax d)Leaky Relu e)Tanh

**TensorFlow 2.0**

**Artificial Neural Network with Tensorflow**

- Working with Images_Introduction
- Working with Images – Reshaping understanding, size of image understanding pixels Digitization,
- Sampling, and Quantization
- Working with images – Filtering
- Hands-on Python Demo: Working with images
- Introduction to Convolutions
- 2D convolutions for Images
- Convolution – Backward
- Transposed Convolution and Fully Connected Layer as a Convolution
- Pooling: Max Pooling and Other pooling options

**CNN Architectures and Transfer Learning**

- CNN Architectures and LeNet Case Study
**Case Study:**AlexNet**Case Study:**ZFNet and VGGNet**Case Study:**GoogleNet**Case Study:**ResNet- GPU vs CPU
- Transfer Learning Principles and Practice
- Hands-on Keras Demo: SVHN Transfer learning from MNIST dataset
- Transfer learning Visualization (run package, occlusion experiment)
- Hands-on demo T-SNE

**Object Detection**

- CNN’s at Work – Object Detection with region proposals
- CNN’s at Work – Object Detection with Yolo and SSD
- Hands-on demo- Bounding box regressor
- #Need to do a semantic segmentation project

**CNN’s at Work – Semantic Segmentation**

- CNNs at Work – Semantic Segmentation
- Semantic Segmentation process
- U-Net Architecture for Semantic Segmentation
- Hands-on demo – Semantic Segmentation using U-Net
- Other variants of Convolutions
- Inception and MobileNet models

**CNN’s at work- Siamese Network for Metric Learning**

- Metric Learning
- Siamese Network as metric learning
- How to train a Neural Network in Siamese way
- Hands-on demo – Siamese Network

**Introduction to Statistical NLP Techniques**

- Introduction to NLP
- Preprocessing, NLP Tokenization, stop words, normalization, Stemming and lemmatization
- Preprocessing in NLP Bag of words, TF-IDF as features
- Language model probabilistic models, n-gram model, and channel model
- Hands-on NLTK

**Word Embedding**

- Word2vec
- Golve
- POS Tagger
- Named Entity Recognition(NER)
- POS with NLTK
- TF-IDF with NLTK

**Sequential Models**

- Introduction to sequential models
- Introduction to RNN
- Introduction to LSTM
- LSTM forward pass
- LSTM backdrop through time
- Hands-on Keras LSTM

**Applications**

- Sentiment Analysis
- Sentence generation
- Machine translation
- Advanced LSTM structures
- Keras – machine translation
- ChatBot

- Install Tableau for Desktop 10
- Tableau to Analyze Data
- Connect Tableau to a variety of dataset
- Analyze, Blend, Join and Calculate Data
- Tableau to Visualize Data
- Visualize Data In the form of Various Charts, Plots, and Maps
- Data Hierarchies
- Work with Data Blending in Tableau
- Work with Parameters
- Create Calculated Fields
- Adding Filters and Quick Filters
- Create Interactive Dashboards
- Adding Actions to Dashboards