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In this course, you will learn :
- Python Programming
- Data Analysis and Visualization
- Supervised and Unsupervised Machine Learning
- Recommender System using Python
- Time Series Modelling
- Statistical Foundations
Syllabus :
1. Introduction to Data Science
- What is Data Science?
- What does Data Science involve?
- Era of Data Science
- Business Intelligence vs Data Science
- Life cycle of Data Science
- Tools of Data Science
- Introduction to Python
2. Data Extraction, Wrangling, & Visualization
- Data Analysis Pipeline
- What is Data Extraction?
- Types of Data
- Raw and Processed Data
- Data Wrangling
- Exploratory Data Analysis
- Visualization of Data
3. Introduction to Machine Learning with Python
- Python Revision (numpy, Pandas, scikit learn, matplotlib)
- What is Machine Learning?
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Linear regression
- Gradient descent
4. Supervised Learning - I
- What is Classification and its use cases?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree
- Confusion Matrix
- What is Random Forest?
5. Dimensionality Reduction
- Introduction to Dimensionality
- Why Dimensionality Reduction?
- PCA
- Factor Analysis
- Scaling dimensional model
- LDA
6. Supervised Learning - II
- What is Naïve Bayes?
- How Naïve Bayes works?
- Implementing Naïve Bayes Classifier
- What is Support Vector Machine?
- Illustrate how Support Vector Machine works?
- Hyperparameter optimization
- Grid Search vs Random Search
- Implementation of Support Vector Machine for Classification
7. Unsupervised Learning
- What is Clustering & its Use Cases?
- What is K-means Clustering?
- How K-means algorithm works?
- How to do optimal clustering?
- What is C-means Clustering?
- What is Hierarchical Clustering?
- How Hierarchical Clustering works?
8. Association Rules Mining and Recommendation Systems
- What are Association Rules?
- Association Rule Parameters
- Calculating Association Rule Parameters
- Recommendation Engines
- How Recommendation Engines work?
- Collaborative Filtering
- Content Based Filtering
9. Reinforcement Learning
- What is Reinforcement Learning?
- Why Reinforcement Learning?
- Elements of Reinforcement Learning
- Exploration vs Exploitation dilemma
- Epsilon Greedy Algorithm
- Markov Decision Process (MDP)
- Q values and V values
- Q values and V values
- α values
10. Time Series Analysis
- What is Time Series Analysis?
- Importance of TSA
- Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- Stationarity
- ACF & PACF
11. Model Selection and Boosting
- What is Model Selection?
- Need of Model Selection
- Cross – Validation
- What is Boosting?
- How Boosting Algorithms work?
- Types of Boosting Algorithms
- Adaptive Boosting
