Data Science & Machine Learning
Turn data into predictions with Python, statistics, machine learning and deep learning.
What your team will learn
Python & Data Wrangling
Master Python programming and powerful libraries like NumPy and Pandas to clean, manipulate, and analyze massive datasets.
Machine Learning Algorithms
Implement supervised and unsupervised algorithms for regression, classification, and clustering to solve complex business problems.
Deep Learning & Neural Networks
Build advanced neural networks using TensorFlow and Keras for complex pattern recognition tasks.
Computer Vision & NLP
Develop models capable of understanding images and human language using OpenCV and modern NLP techniques.
Data Visualization & BI
Transform raw data into actionable business intelligence using Matplotlib, Seaborn, and Power BI dashboards.
Model Deployment & MLOps
Take your models from the lab to production using Flask, FastAPI, and Docker containerization.
Program curriculum
20 modules. Modules and depth can be customised for your team.
01MODULE 1 — Introduction to Data Science
- What is Data Science
- AI vs ML vs Data Science
- Data Science Lifecycle
- Real-World Use Cases
- Healthcare Applications
- Banking Applications
- E-Commerce Applications
- AI Systems
- Marketing Analytics
- Data Collection
- Data Cleaning
- Analysis
- Modeling
- Deployment
02MODULE 2 — Python Programming for Data Science
- Variables
- Data Types
- Operators
- Loops
- Functions
- OOPS Concepts
- Exception Handling
- File Handling
- Modules & Packages
- Lists
- Tuples
- Dictionaries
- Sets
- APIs
- Web Scraping Basics
03MODULE 3 — Mathematics for Data Science
- Vectors
- Matrices
- Matrix Operations
- Derivatives
- Gradient Descent Basics
- Probability Theory
- Conditional Probability
- Bayes Theorem
- Mean
- Median
- Mode
- Standard Deviation
- Variance
04MODULE 4 — Data Analysis with Python
- Arrays
- Broadcasting
- Vectorized Operations
- DataFrames
- Data Cleaning
- Data Transformation
- GroupBy Operations
- Merge & Join
- Missing Values
- Duplicate Handling
- Data Transformation
05MODULE 5 — Data Visualization
- Line Charts
- Bar Charts
- Histograms
- Scatter Plots
- Heatmaps
- Pairplots
- Correlation Analysis
- Plotly Basics
- Dashboard Concepts
- Business Reporting
- Analytical Visualization
06MODULE 6 — Exploratory Data Analysis (EDA)
- Data Profiling
- Outlier Detection
- Feature Understanding
- Correlation
- Covariance
- Customer Analysis
- Revenue Trends
- Behavioral Analysis
07MODULE 7 — Feature Engineering
- Encoding
- Scaling
- Normalization
- Correlation Analysis
- Importance Ranking
- PCA Basics
08MODULE 8 — Machine Learning Fundamentals
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Training & Testing
- Cross Validation
- Hyperparameter Tuning
09MODULE 9 — Supervised Machine Learning
- Linear Regression
- Polynomial Regression
- Ridge Regression
- Lasso Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines
- KNN
- Naive Bayes
10MODULE 10 — Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- Apriori Algorithm
- PCA
- t-SNE Basics
11MODULE 11 — Model Evaluation & Optimization
- Accuracy
- Precision
- Recall
- F1 Score
- ROC Curve
- Overfitting & Underfitting
- Bias vs Variance
- Hyperparameter Tuning
12MODULE 12 — Deep Learning Fundamentals
- Perceptron
- ANN Architecture
- ReLU
- Sigmoid
- Softmax
- Backpropagation
- Optimizers
13MODULE 13 — TensorFlow & Keras
- Tensors
- Sequential Models
- Building Neural Networks
- Model Training
- Model Saving & Loading
14MODULE 14 — Computer Vision
- Pixels
- Image Transformation
- CNN Architecture
- Image Classification
- Face Detection
- Image Recognition
15MODULE 15 — Natural Language Processing (NLP)
- Text Processing
- Tokenization
- Stop Words
- Stemming & Lemmatization
- TF-IDF
- Word Embeddings
- Sentiment Analysis
- Text Classification
16MODULE 16 — Generative AI Fundamentals
- GPT Models
- Transformer Basics
- Prompt Design
- Prompt Optimization
- OpenAI Basics
- AI Application Integration
17MODULE 17 — MLOps & Model Deployment
- Flask APIs
- FastAPI Basics
- Containerization
- AWS AI Services
- Azure AI Services
- Logging
- Performance Monitoring
18MODULE 18 — SQL for Data Science
- Queries
- Joins
- Aggregations
- KPI Queries
- Reporting Queries
19MODULE 19 — Power BI for Data Science
- Data Visualization
- Business Dashboards
- Data Analysis Workflow
20MODULE 20 — Real-Time End-to-End Projects
- 1. Customer Churn Prediction (Customer Analysis, ML Prediction Model, Business Insights)
- 2. AI Resume Screening System (NLP Processing, Candidate Ranking, Resume Analysis)
- 3. Healthcare Disease Prediction (Patient Analysis, Prediction Models, Dashboard Reporting)
- 4. Stock Market Prediction System (Time Series Analysis, Forecasting, Data Visualization)
- 5. AI Chatbot Application (NLP-Based Chat System, AI Responses, Context Handling)
- 6. Face Recognition System (Computer Vision, Image Processing, Identity Detection)
- 7. Recommendation Engine (Product Recommendations, User Behavior Analysis)
Tools & technologies covered
Who should attend
Frequently asked questions
1. Is Programming Mandatory?
Yes. Basic programming knowledge is helpful, but fundamentals are covered. We start with Python programming from scratch, ensuring you build a strong foundation before moving into complex data science concepts.
2. Will Deep Learning Be Covered?
Yes. TensorFlow, Keras, CNN, and deep learning concepts are included. You will learn how to build, train, and optimize neural networks for both structured data and unstructured data like images.
4. Is Generative AI Included?
Yes. Prompt engineering and LLM fundamentals are included. You will learn how to interact with modern Generative AI models and integrate AI APIs into your applications.
5. Will Deployment Be Covered?
Yes. Flask, FastAPI, Docker, and cloud basics are included. We ensure you know not just how to build models, but how to deploy them into production environments as scalable APIs.
Can this program be customised for our team?
Yes. We tailor the modules, depth, examples and hands-on projects to your team's current skills, technology stack and business goals.
Where is the training delivered?
On-site at your office or campus, live online for distributed teams, or at our training centre in Nagole, Hyderabad, whichever suits your team best.
How long does the program take?
Duration depends on the modules you choose and your team's availability. Our training proposal includes a detailed schedule with hours, timeline and session plan.
Do participants receive a certificate?
Yes. Participants who complete the program receive a PRR Technologies training completion certificate.
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