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ADVANCED FULL STACK DATASCIENCE Course Details
 

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Batch Date: Aug 15th & 16th @8:30PM

Faculty: Khan Corporate Trainer (10+ Yrs of Exp,.. & Real time Expert)

Duration: 10 Weekends Batch

Venue :
DURGA SOFTWARE SOLUTIONS,
Flat No : 202, 2nd Floor,
HUDA Maitrivanam,
Ameerpet, Hyderabad - 500038

Ph.No: +91 - 8885252627, 9246212143, 80 96 96 96 96


 Syllabus:

Advanced Full Stack Data Science with Deep Learning & Generative AI (GenAI) Program
Hands-on Training, Project Development & Deployment

Topics:

Introduction to Data Science:

  • What is Data Science
  • Importance of Data Science
  • Why Data Science is needed
  • Use Cases
  • Problems and Solutions
  • Different Roles

Project Life Cycle:

  • Macine Learning Project Life Cycle
  • Problem
  • Collecting the data
  • EDA
  • Cleaning
  • Transformation
  • Partition
  • Model fitting
  • Cross validation
  • Metrics
  • Deployment of Project

Fundamentals of Statistics:

  • Sample
  • Population
  • Data types continous
  • discrete
  • Central tendency
  • Spread
  • Shape of the data such histogram
  • Skewness
  • Kurtosis

Statistical Visualisation:

  • Bargraph
  • Pie Graph
  • Box plot IQR
  • Whisker lengths
  • outliers
  • Scatter plot Positive
  • Negative
  • Neutral
  • Correlation examples

Fundamentals of Python:

  • Introduction to Python language
  • Software Installation
  • Python
  • Anaconda Jupyter
  • Visual Studio Code
  • Google Colab

Python Introduction:

  • What is Python
  • Features
  • Identifiers
  • Characteristics
  • Indentation
  • Quotations
  • Reserved Words
  • Variables
  • Datatypes
  • Operators
  • Different Types of Operators

Python Programming:

  • Variables
  • Datatypes
  • String Indexing
  • Slicing
  • String Functions
  • Expressions

Data Structures:

  • Data Structures List
  • Tuples
  • Dictionary
  • Set
  • methods such as append
  • extend
  • insert
  • remove
  • pop
  • clear
  • index
  • count
  • sort
  • Reverse

Control Flow Structure:

  • Conditional Statement if
  • if-else
  • if-elif-else
  • nested if
  • Looping Statement For loop
  • While Loop
  • Break
  • Continue and Functions
  • Different Types of Functions
  • Lambda functio
  • use cases
  • scenario
  • examples

Exceptional Handling:

  • What is exception
  • Different Types of Exceptions
  • Use Cases
  • Scenario
  • use cases
  • scenario
  • examples

File Handling:

  • What is file
  • creation of files
  • different types of file handling
  • orders of files
  • different operations
  • examples
  • use cases
  • scenarios.

Numpy:

  • Numpy – Installation and usage
  • Importing numpy
  • scalar
  • array
  • vector
  • one dimension
  • two dimension
  • random int

Exploring Pandas:

  • Pandas Installation
  • usage
  • giving column names
  • Importing pandas
  • read_csv
  • head
  • tail
  • describe
  • Pandas info
  • selecting columns
  • dropping columns
  • groupby
  • concat
  • merge
  • removing duplicates
  • filling blanks with mean
  • preprocessing
  • use cases
  • scenarios
  • examples

Exploratory Data Analysis:

  • EDA showing graphs
  • as histogram
  • boxplot
  • bargraph
  • scatter plot
  • heat map using matplotlib
  • seaborn using Google collab
  • with examples

Advance Statistics - Probability - Normal Distribution:

  • Probability
  • Normal distribution theory
  • standardization
  • zscore z table
  • applications
  • confidence Interval Working on Case Study

Advance Statistics - Hypothesis Testing:

  • Level of significance
  • Hypothesis Testing One sample Z test
  • Two sample Z test
  • T-test
  • Working on case study

Introduction to Machine learning:

  • What is Machine Learning?
  • Different Types of Machine Learning
  • use cases
  • Scenarios
  • real time examples

Supervised Machine Learning Linear Regression:

  • Simple Linear Regression
  • metrics such RMSE and R square - Working on case study
  • examples
  • use cases
  • scenarios

Multiple Linear Regression:

  • Introduction to Regression models
  • Multiple LinearRegression - Assumptions of Linear Regression
  • Variable selection
  • Multicollinearity
  • VIF
  • use cases
  • scenarios with real time example

Logistic Regression:

  • What is meant by classification models ?
  • When do we choose Logistic regression
  • modelfitting
  • confusion matrix
  • accuracy score - Working on case study

Metrics:

  • Other metrics Sensitivity
  • Specificity
  • precision
  • F1 score
  • ROC curve
  • AUC score Working on case Study.
  • use case
  • examples.

Data Transformation:

  • What is Data Transformation
  • Standardard scaler
  • minmax scaler
  • label encoding
  • one hot encoding and Data partition
  • Training and Testing

Modal Validation Techniques:

  • Cross validation Stratified K-Fold
  • K-Fold cross validation
  • Shuffle Split Cross-Validation real time examples

Under Fitting to Over fitting:

  • Variance Biased Trade-off under fitting-causes-Lack of training
  • best fit
  • over fitting - causes -Noise in training data
  • Too many training epochs or iterations
  • too many variables
  • Visualizations Underfitting
  • bestfit
  • Overfitting and Feature Engineering - Working on case study

Regularization Techniques:

  • Techniques such as Lasso
  • Ridge
  • ElasticNet - Working on case study

Classifiers – Support Vector Machine:

  • Support vector machine Hyperplane
  • Maximum margin classifier
  • Support Vectors
  • SVM for Linear Classification
  • SVM for Non-Linear Classification polynomial
  • RBF, Sigmoid Function

Decision Tree:

  • Decision Tree Structure
  • Root node
  • Internal nodes
  • terminal nodes
  • Gini Impurity
  • Entropy and Information Gain for classification
  • Overfitting and Underfitting in Decision Trees
  • Pruning
  • Hyperparameters - Working on case study

Ensembled Techniques:

  • Ensemble Methods: Bagging and Random forests
  • Working on hyper parameters to control overfitting
  • Rreal time examples.

Boosting Methods:

  • Sequential methods: Gradient Boosting
  • Ada Boost
  • using Grid search CV, XG Boost, LightGBM use cases and real time examples.

Deployment - Project Discussion:

  • Final Project Explaination with Deployment.

Unsupervised Machine Learning:

  • What are DImensional Reduction Techniques?
  • Purpose of PCA
  • Eigenvectors / Eigen values
  • Applications
  • Advantages
  • Working on case study

Clustering:

  • Introduction to Clustering
  • Distance Metrics
  • Clustering Algorithms (K mean, dbscan)
  • Choosing the Right Number of Clusters
  • Elbow Method
  • Silhouette Analysis working on case study

Recommendation System:

  • What is Recommendation and why it is important?
  • What is Collaborative Filtering (CF) And Content-Based Filtering ?

Time Series Analysis:

  • Time series Concepts
  • components
  • Visualization
  • Data partition
  • Lagplot
  • ARIMA models
  • Python code on ARIMA models

Deep Learning - Artifical Neural Network:

  • Perceptron
  • Single Layer Network
  • activation functions
  • Back propagation method
  • Simple ANN code
  • Multilayer Neural network
  • Gradient Descent method
  • optimizers
  • learning rate - complete code with tensorflow

Deep Learning - Recurrent Neural Networks:

  • RNN - use cases
  • vanishing and exploiding problem
  • Simple RNN code
  • LSTM Architecture
  • Working model
  • LSTM vs GRU
  • real time examples

Deep Learning – Convolution Neural Network:

  • Introduction to CNN
  • Image Processing Basics
  • Convolution Layer
  • Filters
  • Feature Maps
  • Padding
  • Stride
  • Pooling
  • Layer
  • Softmax
  • CNN Architecture
  • TensorFlow
  • Keras
  • PyTorch
  • Transfer Learning
  • Image Classification

Natural Language Processing:

  • What is Text Data
  • Various forms
  • Applications
  • Text pre processing
  • Tokenization
  • Normalization
  • Stopwords
  • Lemmatization
  • stemming
  • Visualization on preprocessed text data
  • Text Representation
  • Bag of Words (BoW)
  • Term Frequency-Inverse Document Frequency (TF-IDF)
  • Sentiment Analysis
  • Classification model using Machine Learning
  • Named Entity Recognition (NER)
  • What is Word Embedding?
  • What are pre-trained word Embeddings
  • Word2Vec
  • Skip gram
  • CBOW
  • real time applications.
  • Language Modeling
  • N-gram Models
  • Neural Language Models
  • applicaton of RNNs
  • LSTMs on Text data
  • Working on case study
  • use cases and real time examples.

Generative AI – Introduction to LLM:

  • Introduction to GenAI
  • what is Large Language Models?
  • Transfer Learnings in NLP
  • what are pre-trained models
  • real time examples.

Generative AI - Applications of LLM's:

  • What are tansformers?
  • Transformer Archiecture
  • Encoding
  • Decoding
  • Hugging Face transformers and its use cases
  • applying pre- trained models use cases
  • Scenarios
  • with examples and creation of applications

Generative AI – Project:

  • How to develop a project in Generative AI
  • Deployment of GenAI Project

GitHub:

  • Account creation
  • new repository
  • uploading developed project
  • uploading use cases with coding examples

Resume:

  • Resume building discussion and guidance