Diploma in Data Science - Asquare Technologies

Diploma in Data Science | Complete Syllabus

Data Science Syllabus

1. Introduction to Machine Learning ▼

1.1 Algorithms and Models in Machine Learning
1.2 Feature engineering
- 1.2.1 Encoding
- 1.2.2 Scaling

2. Types of Machine Learning – Classification ▼

2.1.1 KNN
- KNN Algorithm Implementation
- Performance measure
- Streamlit
2.1.2 SVM
- SVM Algorithm
2.1.3 Naive Bayes
- Probability
- Conditional Probability
- Bayes Theorem
- Q&A of Naive Bayes
- Naive Bayes Algorithm
2.1.4 Decision Tree
- Examples of decision tree
- Implementation of DT
2.1.5 Random Forest
- Implementation of Random Forest

2.2 Regression ▼

2.2.1 Simple Linear Regression
- Simple linear regression Algorithm
- Performance of SLR
2.2.2 Polynomial Regression
- Polynomial Regression Algorithm
2.2.3 Multiple Linear Regression
- Multiple Linear Regression Algorithm

2.3 Handling Imbalanced Datasets ▼

2.3.1 Implementation of Imbalanced Dataset

2.4 Clustering ▼

2.4.1 K-Means Clustering
- K-Means Clustering Algorithm

2.5 Hyperparameter Tuning ▼

Grid Search, Random Search

3. Introduction to NLP ▼

3.1 Steps of NLP
3.2 Applications of NLP
3.3 Sentiment Analysis on Twitter Data

4. Deep Learning ▼

4.1 What is ANN
- Implementation of ANN
- Difference between Feed Forward Network and backpropagation network
4.2 What is CNN
- Layers of CNN
- Implementation of CNN
4.3 What is RNN
- What is LSTM
- Implementation of RNN

Program Details

Duration Flexible / Self-paced

Modules 8 Core Sections

Level Beginner → Advanced

Certificate Yes + Projects