1.1 Introduction to Data Science
1.2 Introduction to Python
1.3 Python for Data Science
1.4 Data Structures
1.5 Numpy
1.6 Pandas
1.7 Matplotlib.

2.1 Linear Algebra
2.2 Probability & Statistics
2.3 All kinds of plots for data visualization
2.4 Dimensionality reduction techniques
2.5 PCA
2.6 T-SNE

3.1 NLP intrduction and importance
3.2 Text Preprocessing
3.3 Uni-grams, bi-grams and n-grams
3.4 Bag of Words (BOW)
3.5 TF-IDF
3.6 Word to Vector

4.1 Accuracy
4.2 Confusion Matrics
4.3 Precision, Recall & F1 Score
4.4 ROC and AUC Curves
4.5 Log loss
4.6 Mean Absolute Error and Mean Absolute Deviation

5.1 Introduction to Machine learning algorithms
5.2 Types of ML algorithms
5.3 K-Nearest Neighbors (KNN)
5.4 Naive Bayes
5.5 Logistic Regression
5.6 Support Vector Machine (SVM)
5.7 Decision Trees
5.8 Linear Regression
5.9 Optimization Techniques
5.0 Ensemble Models
5.11 Random Forest
5.12 GBDT and XGBoost

6.1 Concept and necessity
6.2 Ways to create feature engineering

7.1 Recommendation System Introduction
7.2 Types of Recommender System
7.3 Collaborative filtering
7.4 Content based recommender system
7.5 Case Study on Recommendation System

1.1 Introduction to Data Science
1.2 Introduction to Python
1.3 Python for Data Science
1.4 Data Structures
1.5 Numpy
1.6 Pandas
1.7 Matplotlib.

2.1 Linear Algebra
2.2 Probability & Statistics
2.3 All kinds of plots for data visualization
2.4 Dimensionality reduction techniques
2.5 PCA
2.6 T-SNE

3.1 NLP intrduction and importance
3.2 Text Preprocessing
3.3 Uni-grams, bi-grams and n-grams
3.4 Bag of Words (BOW)
3.5 TF-IDF
3.6 Word to Vector

4.1 Accuracy
4.2 Confusion Matrics
4.3 Precision, Recall & F1 Score
4.4 ROC and AUC Curves
4.5 Log loss
4.6 Mean Absolute Error and Mean Absolute Deviation

5.1 Introduction to Machine learning algorithms
5.2 Types of ML algorithms
5.3 K-Nearest Neighbors (KNN)
5.4 Naive Bayes
5.5 Logistic Regression
5.6 Support Vector Machine (SVM)
5.7 Decision Trees
5.8 Linear Regression
5.9 Optimization Techniques
5.0 Ensemble Models
5.11 Random Forest
5.12 GBDT and XGBoost

6.1 Concept and necessity
6.2 Ways to create feature engineering

7.1 Recommendation System Introduction
7.2 Types of Recommender System
7.3 Collaborative filtering
7.4 Content based recommender system
7.5 Case Study on Recommendation System

8.1 Introduction
8.2 Neural Netwrok
8.3 Multi-layer Perceptron
8.4 Tensorflow & Keras
8.5 CNN
8.6 LSTM
8.7 Case Study - Computer Vision
8.8 Case Study - LSTM

9.1 Two class classification - Case Study
9.2 Prediction - Case Study
9.3 Multiclass classification - Case Study
9.4 Clustering - Case Study

Machnie learning

Deep learning