What you'll learn
- Master Python programming from scratch to advanced level
- Perform Data Analysis using Pandas, NumPy, and Matplotlib
- Build Machine Learning models (Regression, Classification, Clustering)
- Deep Learning with Neural Networks, TensorFlow and PyTorch
- Develop Real-world Capstone Projects and deploy them
- Job-ready portfolio building and interview preparation
Description
A comprehensive 3-month job-ready program designed to take you from a beginner to a professional Data Scientist with expertise in AI and Machine Learning. Training includes Python, Data Analytics, Deep Learning, and Full-Stack Deployment.
Course Content
Month 1 — Core Data Foundations + Python for AI
Week 1 — Intro to Data Science & Python Programming
Data Science Fundamentals
- What is Data Science
- Industry workflows
- Types of data
- ML vs AI vs Deep Learning
Python Setup
- Install Anaconda or VS Code
- Working with Jupyter Notebook
Python Basics
- Variables
- Loops
- Functions
Practice
- Basic Python exercises
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Week 2 — Data Handling & Cleaning
NumPy
- Arrays
- Broadcasting
- Vectorization
Pandas
- DataFrames
- Joins
- Grouping
Data Cleaning
- Handling missing values
- Outliers
- Normalization
- Scaling
EDA
- Exploratory Data Analysis using Pandas
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Week 3 — Data Visualization
Visualization Libraries
- Matplotlib
- Seaborn
Plot Types
- Bar charts
- Line charts
- Heatmaps
- Histograms
Business Visualization
- Statistical visualizations
- Dashboard thinking
Tools
- Introduction to Power BI
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Week 4 — Statistics & Probability for Machine Learning
Statistics
- Descriptive statistics
Probability
- Probability basics
- Random variables
Distributions
- Gaussian distribution
- Bernoulli distribution
- Poisson distribution
Analysis
- Hypothesis testing
- Correlation vs causation
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Month 2 — Machine Learning Algorithms
Week 5 — Machine Learning Foundations
ML Basics
- Supervised learning
- Unsupervised learning
Data Preparation
- Feature engineering basics
- Train test split
- Cross validation
Evaluation Metrics
- Accuracy
- Recall
- F1 Score
- ROC-AUC
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Week 6 — Regression Algorithms
Regression Models
- Linear Regression simple
- Linear Regression multiple
- Ridge regression
- Lasso regression
- Polynomial regression
Evaluation
- Error metrics
Case Study
- Predicting house prices
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Week 7 — Classification Algorithms
Classification Models
- Logistic Regression
- KNN
- Naive Bayes
- Decision Trees
- Random Forest
Industry Use Cases
- Churn prediction
- Fraud detection
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Week 8 — Unsupervised Learning & Feature Engineering
Clustering
- K-Means
- Hierarchical clustering
Dimensionality Reduction
- PCA
- Feature reduction techniques
Industry Application
- Customer segmentation
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Month 3 — AI, Deep Learning & Real Client Project
Week 9 — Neural Networks & Deep Learning
Neural Network Basics
- What are artificial neural networks
- Backpropagation
- Activation functions
Frameworks
- TensorFlow introduction
- PyTorch introduction
Practice
- Build first neural network
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Week 10 — Computer Vision or NLP
Computer Vision Track
- CNNs
- Image classification
- Data augmentation
- Transfer learning using ResNet or VGG
NLP Track
- Tokenization
- Embeddings
- Sentiment analysis
- Text classification
- Introduction to HuggingFace models
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Week 11 — AI Tools & LLM Integration
LLM Fundamentals
- How GPT and LLMs work
Development
- Using OpenAI APIs
- Prompt engineering for automation
AI Tools
- Text summarizer
- Image analyzer
- Chat based Q&A system
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Week 12 — Final Live Project
Industry Projects
- ML demand forecasting
- Customer segmentation model
- AI chatbot for customer support
- Lead scoring engine
- Fraud detection model
- Resume parser using NLP
Delivery
- GitHub project
- Deployment using Streamlit Flask or FastAPI
- Presentation to team
- Internship certificate
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Training Partner
Codencreative Tech Pvt Ltd
IT Solutions & Training Provider
We are a Jaipur-based IT company providing software solutions and high-quality training. Our "Train Inside a Company" model ensures students gain real-world experience, working on live projects under the mentorship of industry experts.
