Subcribe and Access : 5200+ FREE Videos and 21+ Subjects Like CRT, SoftSkills, JAVA, Hadoop, Microsoft .NET, Testing Tools etc..

Batch Date: May 16th & 17th @10:00AM

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

Duration: 10 Weekends Batch (Sat : 3 Hours, Sun : 3 Hours)

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 DATASCIENCE
with GENERATIVE AI (GENAI)

Topics:

Demo - Data Science:

• Introduction to 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, etc.,

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, String Indexing, Slicing, String Functions, Expressions.

Python Programming:

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

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 function, 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 tables, 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, real 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 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.

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.