• Overview
• Flow of course

• What is data science
• Importance of data science
• Demand for data science
• Prerequisite to learn data science
• Data science life cycle
• Why Organizations hiring Data Scientist
• Real Time Projects overview
• Data science vs Business Intelligence
• Terms to Remember in Data Science
• Artificial Intelligence Overview
• Overview of Real time Project on Artificial Intelligence
• What is Data?
• How Data is generated?
• Data Storage Overview
• Types of Data
• Bascis of Software Life Cycle

1. Installation of required softwares
2. How to install Packages/ Libraries

• Numpy
• Pandas
• Scipy
• Scikit-Learn
• Keras
• Matplotlib
• Seaborn
• Cufflinks

• Introduction to Python
• Python 2 vs Python 3
• Python Identifiers
• Operators
• Comments (Single and Multi lines)
• Data Types
• Advanced Data Structure
• Different type of Loops
• Conditions
• Functions

• Collecting data from different sources
• Analyzing data
• Data Preprocessing
• Data Munging
• Data Mining
• Data Manipulation
• Data Visualization
• Feature Selection
• Feature Scaling
• Dimensionality Reduction Techniques

• Basics of Statistics
• Descriptive Statistics
• Inferential Statistics
• Qualitative Analysis
• Quantitative Analysis
• Hypothesis Testing
• Data Distribution
• Outlier Detection
• Other Statistical fundamentals

1. Introduction to Machine Learning
2. Supervised learning

Regression Models
• Introduction to Linear Regression
• Multiple Linear Regression
• Ordinary Least Squares Method
• Non Linear Models
• Polynomial Regression
• Support Vector Regressor
• K-NN Regressor
• Decision Tree Regressor
• Random Forest Regressor

Evaluation Metrics for Regression Models
• R Square
• Adjusted R Square

Error Functions
• Root Mean Square Error (RMSE)
• Mean Absolute Error

Error Functions
• Root Mean Square Error (RMSE)
• Mean Absolute Error

Classification Models
• Logistic Regression
• Naive Bayes
• Support Vector Classifier
• K-NN Classifier
• Decision Tree Classifier
• Random Forest Classifier

Evaluation Metrics for Classification Models
• Accuracy
• Precision
• Recall
• F1 score
• ROC Curve

3.Unsupervised learning

Clustering models
• K-Mean Clustering
• Hierarchial Clustering

4.Reinforcement learning

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans i.e., learn by example. The main concept in deep leaning algorithms is automating the extraction of representations (abstractions) from the data.

1. Limitations of Machine Learning
2. What is Deep Learning?
3. Advantage of Deep Learning over Machine learning

1. How Deep Learning Works?
2. Activation Functions
3. Illustrate Perceptron
4. Training a Perceptron
5. Important Parameters of Perceptron
6. Constants, Placeholders, Variables

1. Understand limitations of a Single Perceptron
2. Understand Neural Networks in Detail
3. Illustrate Multi-Layer Perceptron
4. Backpropagation

1. Introduction to CNNs
2. CNNs Application
3. Architecture of a CNN
4. Convolution and Pooling layers in a CNN

1. Introduction to RNN Model
2. Modelling sequences
3. Training RNNs with Backpropagation
4. Long Short-Term memory (LSTM)
5. Recursive Neural Tensor Network Theory
6. Recurrent Neural Network Model