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Batch Date: June 13th & 14th @5:00AM

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

Duration: 15 - 17 Weekends Batch (Sat : 2 Hours, Sun : 2 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:

Industry-Ready Full Stack Data Science
& Generative AI with AI Agents, Agentic AI
& Hands-on Projects Development & Deployment

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:

• Machine Learning Project Life Cycle, Problem Definition, Data Collection, EDA, Data Cleaning, Data Transformation, Data Partitioning, Model Fitting, Cross Validation, Evaluation Metrics, Deployment of Project

Fundamentals of Statistics:

• Sample, Population, Continuous and Discrete Data Types, Measures of Central Tendency, Measures of Dispersion, Histogram, Skewness, Kurtosis.

Statistical Visualisation:

• Bar Graph, Pie Chart, Box Plot, IQR, Whiskers, Outliers, Scatter Plot, Correlation Analysis, Positive, Negative and Neutral Correlation.

Fundamentals of Python:

• Introduction to Python Language, Software Installation, Python, Anaconda, Jupyter Notebook, Visual Studio Code, Google Colab.

Python Introduction:

• What is Python, Features, Identifiers, Characteristics, Indentation, Quotations, Reserved Words, Variables, Data Types, Operators, String Indexing, Slicing, String Functions, Expressions.

Python Programming:

• Lists, Tuples, Dictionaries, Sets, Built-in Methods such as Append, Extend, Insert, Remove, Pop, Clear, Index, Count, Sort, Reverse.

Control Flow Structure:

• Conditional Statements, If, If-Else, If-Elif-Else, Nested If, For Loop, While Loop, Break, Continue, Functions, Lambda Functions, Real-Time Examples

Exceptional Handling:

• What is Exception Handling, Types of Exceptions, Try-Except Blocks, Use Cases and Practical Examples.

File Handling:

• Reading and Writing Files, File Operations, Text Files, Practical Scenarios.

Numpy:

• NumPy Installation, Arrays, Vectors, Matrices, Random Functions, Numerical Computing.

Exploring Pandas:

• DataFrames, Read CSV, Head, Tail, Describe, Info, Selecting Columns, Dropping Columns, GroupBy, Merge, Concat, Missing Value Handling, Data Preprocessing

Exploratory Data Analysis (EDA) :

• Histograms, Boxplots, Bar Charts, Scatter Plots, Heatmaps using Matplotlib and Seaborn, Case Studies.

Advance Statistics - Probability - Normal Distribution:

• Probability Concepts, Normal Distribution, Standardization, Z-Score, Z-Tables, Confidence Intervals, Applications.

Advance Statistics - Hypothesis Testing:

• Level of Significance, One Sample Z-Test, Two Sample Z-Test, T-Test, Case Studies.

Introduction to Machine learning:

• What is Machine Learning, Types of Machine Learning, Supervised, Unsupervised and Reinforcement Learning, Real-Time Applications.

Supervised Machine Learning Linear Regression:

• Simple Linear Regression, RMSE, R-Square, Case Studies and Practical Implementation.

Multiple Linear Regression:

• Assumptions of Linear Regression, Variable Selection, Multicollinearity, VIF, Real-Time Applications

Logistic Regression:

• Classification Problems, Model Fitting, Confusion Matrix, Accuracy Score, Practical Case Studies.

Metrics:

• Sensitivity, Specificity, Precision, Recall, F1 Score, ROC Curve, AUC Score.

Data Transformation:

• StandardScaler, MinMaxScaler, Label Encoding, One-Hot Encoding, Train-Test Split.

Modal Validation Techniques:

• K-Fold Cross Validation, Stratified K-Fold, Shuffle Split Cross Validation.

Under Fitting to Over fitting:

• Bias-Variance Tradeoff, Feature Engineering, Model Generalization, Practical Examples.

Regularization Techniques:

• Ridge Regression, Lasso Regression, ElasticNet.

Classifiers – Support Vector Machine:

• Hyperplane, Maximum Margin Classifier, Support Vectors, Linear and Non-Linear SVM, Polynomial, RBF, Sigmoid Kernels.

Decision Tree:

• Tree Structure, Gini Index, Entropy, Information Gain, Pruning Techniques, Hyperparameters.

Ensembled Techniques:

• Bagging, Random Forest, Hyperparameter Tuning, Practical Examples.

Boosting Methods:

• AdaBoost, Gradient Boosting, XGBoost, LightGBM, Grid Search CV.

Deployment - Project Discussion:

• End-to-End Machine Learning Project Development and Deployment.

Unsupervised Machine Learning:

• PCA, Dimensionality Reduction, Eigenvalues, Eigenvectors, Applications and Case Studies.

Clustering:

• K-Means, DBSCAN, Distance Metrics, Elbow Method, Silhouette Analysis.

Recommendation System:

• Collaborative Filtering, Content-Based Filtering, Recommendation Engine Concepts

Time Series Analysis:

• Time Series Components, Visualization, Lag Plots, ARIMA Models, Forecasting.

Deep Learning - Artifical Neural Network:

• Perceptron, Single Layer Network, Activation Functions, Backpropagation, Gradient Descent, Optimizers, TensorFlow Implementation.

Deep Learning - Recurrent Neural Networks:

• RNN, Vanishing Gradient Problem, LSTM Architecture, GRU, Sequential Data Processing, Practical Examples.

Natural Language:

• Text Data, Text Preprocessing, Tokenization, Normalization, Stopwords.

Processing (NLP):

• Lemmatization, Stemming, Bag of Words, TF-IDF, Sentiment Analysis, NER, Word Embeddings, Word2Vec, CBOW, Skip-Gram, Language Models, RNN and LSTM Applications.

Generative AI – Introduction to LLM:

• Introduction to Generative AI, Large Language Models (LLMs), Transfer Learning, Pre-trained Models, Foundation Models, Embeddings, Real-Time Examples.

Generative AI – Prompt Engineering:

• Prompt Engineering Basics, Zero-Shot Prompting, Few-Shot Prompting, Chain-of-Thought Prompting, Structured Prompt Design, Output Control Techniques.

Generative AI - Applications of LLM's:

• Transformers, Transformer Architecture, Encoding, Decoding, Hugging Face Transformers, Text Generation, Summarization, Question Answering, Real-Time Applications.

Generative AI - RAG & Vector Databases:

• Retrieval Augmented Generation (RAG), Embeddings, Vector Databases, Semantic Search, Document Q&A Systems.

Generative AI – Project:

• Development of Text Summarizer, Content Generator, Question Answering System, AI Chatbot or Similar GenAI Application. Deployment of GenAI Project.

AI Agents:

• What is an AI Agent, AI Assistant vs AI Agent, Components of AI Agents, LLM, Memory, Tools, Planning, Reasoning, Actions, Agent Workflow, Agent Architecture, Real-Time Business Use Cases
  
• Demonstration of AI Agents using ChatGPT/OpenAI APIs, Building a Simple Question Answering Agent, Career Guidance Agent, Travel Assistant Agent, Understanding Tool Usage and Agent Workflows .

Agentic AI:

• What is Agentic AI, Generative AI vs Agentic AI, Autonomous AI Systems, Goal-Oriented AI, Planning and Decision Making, Multi-Step Reasoning, Self-Correction Concepts, Industry Applications and Future Scope.
• Design and Demonstration of Simple Agentic Workflows, Research Assistant Workflow, Resume Screening Workflow, Career Recommendation Workflow, Multi-Step Task Execution.

GitHub:

• Account Creation, Repository Management, Uploading Projects, Version Control, Portfolio Building.

Resume & Career Guidance:

• Resume Building, LinkedIn Profile Guidance, Project Presentation, Interview Preparation and Career Guidance.