Data Science And AI Development Program

1. Python Programming

• Introduction & Installation

• Python Basics (Syntax, Semantics, Comments)

• Variables, Input & Output

• Strings, Operators & Data Types

• Conditional Statements & Loops

• Data Structures

• Functions

• Modules & Packages

• File Handling

• Exception Handling

• OOPS Concepts

2. Python Libraries

• NumPy

• Pandas

• Matplotlib

• Seaborn

• Plotly

• SciPy

• StatsModels

3. Statistics & Probability

• Descriptive Statistics

• Inferential Statistics

• Probability Distributions

4. Data Analytics

• Exploratory Data Analysis (EDA)

• Data Preprocessing & Feature Engineering

• Data Cleaning

  • Handling Missing Values

  • Handling Imbalanced Datasets

  • Handling Outliers

  • Handling Duplicate Data

  • Handling Wrong Data

• Feature Encoding

  • One Hot Encoding

  • Label Encoding

  • Ordinal Encoding

  • Target Encoding

• Feature Transformation

  • Log Transformation

  • Square Root Transformation

  • Power Transformation

• Feature Scaling

  • Standardization

  • Normalization

5. Machine Learning

Supervised Learning – Regression

• Simple Linear Regression

• Multiple Linear Regression

• Polynomial Regression

• Ridge Regression

• Lasso Regression

• ElasticNet Regression

• Logistic Regression

• Cross Validation

• Regression Evaluation Metrics

Supervised Learning – Classification

• K-Nearest Neighbors (KNN)

• Support Vector Machine (SVM)

• Naive Bayes

• Decision Tree

• Random Forest

• AdaBoost

• Gradient Boosting

• XGBoost

• Classification Evaluation Metrics

Unsupervised Learning

• K-Means Clustering

• Hierarchical Clustering

• DBSCAN

• Silhouette Analysis

• Principal Component Analysis (PCA)

• Anomaly Detection

6. Model Selection & Evaluation

• Overfitting & Underfitting (Bias-Variance Tradeoff)

• Cross Validation

• Hyperparameter Tuning

7. Machine Learning Model Deployment

• Model Saving & Loading

• API-based Model Deployment

Artificial Intelligence

8. Deep Learning

• Perceptron

• Artificial Neural Networks (ANN)

• Forward & Backward Propagation

• Weight Updates

• Activation Functions

• Loss Functions

• Optimizers

• Vanishing & Exploding Gradient

• Convolutional Neural Networks (CNN)

9. NLP for Machine Learning

• Tokenization

• Text Preprocessing (Stemming & Lemmatization using NLTK)

• Named Entity Recognition

• One Hot Encoding

• Bag of Words

• TF-IDF

• Word Embeddings

• Word2Vec & AvgWord2Vec

• Skip-Gram

10. NLP with Deep Learning

• Recurrent Neural Networks (RNN)

• Long Short-Term Memory (LSTM)

• GRU

11. Computer Vision

• Image Processing

• Object Detection

• Video Processing

12. Database (SQL)

• Introduction & Installation

• Data Retrieval

• Aggregation & Grouping

• Joins & Subqueries

• Views & Indexes

• Case Statements & CTE

• Window Functions

• Stored Procedures

13. Data Visualization (Power BI)

• Installation & Setup

• Connecting Data Sources

• Data Preparation using Power Query

• Creating New Columns

• Data Modeling using Relationships

• Charts & Visuals

• Advanced Charts

• Data Analysis using DAX

• Creating Reports & Dashboards

14. Generative AI

• Encoders & Decoders

• Transformers

15. Large Language Models (LLMs)

• Building Chatbots using LangChain

Career Opportunities

After completing this program, you can apply for roles such as:

• Data Scientist

• Data Analyst

• Machine Learning Engineer

• AI Engineer

• Business Intelligence Analyst

• NLP Engineer

• Computer Vision Engineer

Who Can Join This Program?

• Fresh Graduates (Any Stream)

• Final Year Students

• Working Professionals

• Career Switchers (Non-IT to IT)

• Manual Testers / Support Engineers

• Entrepreneurs & Freelancers

No prior coding experience required.

Program Benefits

✔ Industry-oriented curriculum
✔ Hands-on training with real datasets
✔ Live project experience
✔ Resume preparation
✔ Interview guidance
✔ Placement support
✔ Flexible batch timings
✔ Expert trainers