Module 1: Introduction to Data Science & ML
- Introduction to Data Science
- What does Data Science involve?
- Data Science history
- Life cycle of Data Science
- Overview of Data Science Tools
- Machine Learning usage in Data Science
Module 2: Python Basics for Data Science & ML
- Overview of Python
- Different Applications where Python is Used
- Python Scripts execution
- Basics of Python Programming
- Flow Control in Python
- Values, Types, Variables
- Operands and Expressions
- Conditional Statements
- Loops in Python
Module 3: Linear Algebra and Advanced Statistics
- Descriptive Statistics
- Probability
- Inferential Statistics
Module 4: Data Analysis in Data Science
- Data Handling with Numpy
- Data Manipulation Using Pandas
- Data Pre-processing
- Feature Engineering
- Data Visualization using Matplotlib , Seaborn
Module 5: Introduction to Machine Learning Models
- Introduction to Machine Learning
- Types of ML models
- Supervised & Unsupervised Learning
- Performance Metrics
Module 6: Machine Learning Supervised Models – Part 1
- Introduction to Machine Learning Supervised Models
- Regression Models
- Master Linear Regression Model
- Understand Multi Linear Regression Model
- Polynomial Regression Model
Module 7: Machine Learning Supervised Models – Part 2
- Classification Models and use cases?
- Tree Based Models
- Ensemble Methods
- Bagging and Boosting
- Over fitting and Under fitting
- Evaluation Metrics
Module 8: Machine Learning Supervised Models – Part 3
- Introduction to Naive Bayes?
- What is Naive Bayes?
- How Naive Bayes works?
- Implementing Naive Bayes Classifier
- What is a Support Vector Machine?
- Illustrate how Support Vector Machine works
- Hyperparameter Optimization
- Grid Search vs. Random Search
- Implementation of Support Vector Machine for Classification
Module 9: Machine Learning Unsupervised Learning
- What is Clustering & its Use Cases?
- What is K-means Clustering?
- How does the K-means algorithm work?
- How to do optimal clustering
- What is DBSCAN Clustering?
- What is Hierarchical Clustering?
- How does Hierarchical Clustering work?
Module 10: Deep Learning Using TensorFlow
- Artificial Intelligence Basics
- Introduction to Neural Networks
- Activation Functions
- Introduction to Tensor Flow
Module 11: Deep Learning – Part 2
- ANN
- Sequential Neural Networks(RNNs, LSTMs, GRUs)
- Convolution Neural Networks
- Hyper Parameter Tuning in Neural Networks
- Transfer Learning
Module 12: Natural Language Processing
- Text Mining , Cleaning, and Pre-processing using Regex.
- Text Normalization Techniques
- Entity Recognition, Next Word Prediction
- Static Word Embedding Techniques
- Dynamic Word Embedding Techniques
- Topic Modelling
- Text classification, NLTK, sentiment analysis, etc.
- Transformer Based Models
- BERT,BART,ALBERT,DISTILBERT
Module 13: Deploying Machine Learning Models
- Machine Learning Models deployment overview
- What is Flask API
- Deploying Machine Learning Models with Flask API
- Jupyter Notebook
Module 14: Data Science Capstone Project
Module 15: Business Case Studies
- Customer Review Classification Using LSTMs,GRUs
- Customer Churn Prediction (Telecom)
- Image Classification Using Deep Learning Models
- Multi Class Ticket Classification Used Transformer Based Models.
- Loan Defaulter Prediction using Ensembling Techniques
Module 16: Resume Preparation, Interview and Job Assistance
- Prepare Crisp Resume as Data Scientist
- Discuss common interview questions in Data Science
- Explain students what jobs they should target and how