Certified Artificial Intelligence

Python Programming

 An Introduction to Python

 Beginning Python Basics

 Python Program Flow

 Functions& Modules

 Exceptions Handling

 File Handling

 Classes in Python

Statistical Concepts

 Descriptive &

Inferential Statistics,

 Probability Concept: Marginal, Joint &

Conditional

Probability, Bayes

Theorem

 Probability Distributions,

 Hypothesis Test

 Entropy &Information Gain,

 Regression & Correlation,

 Confusion Matrix, Bias & Variance

Data Science and Analytics

 An Introduction to Data Science and Analytics

 Data Analysis Using NumPy

 Data Analysis Using Pandas

 Data Visualization – Pandas, Matplotlib, Seaborne, Plotly and

Cufflinks

Machine Learning

 Introduction to Machine Learning

 Linear Regression

 Logistic Regression

 K-Means Clustering

 Decision Tree

 Random Forest

 K-Nearest Neighbours

 Support Vector Machine

 Naive Bayes

 Principal Component Analysis(PCA)

 Artificial Neural Networks(ANN) Deep Learning

 Introduction to Deep Learning

 Artificial Neural Network -ANN

 Loss Function

 Bias & Gradient Descent

 Stochastic Gradient Descent

 Convolution Neural Networks -CNN

 Recurrent Neural Networks – RNNs

 Natural Language Processing-NLP

 Computer Vision using Opencv

 Deployment