WEEK 1:(Introduction To AI)

Day 1:

Introduction to Artificial intelligence

Different Sectors Using Artificial intelligence.

1.Installation of libraries used in AI.

Day 2:

Introduction about AI libraries like TensorFlow and Keras and their uses.

Day 3:

Introduction about data science libraries like pandas, NumPy and matplotlib.

2.implementation data science libraries.

Day 4:

Handling the csv file through pandas and NumPy.

Day 5:

Working on data science libraries for handling the files.

3. implement matplotlib for graphical data representation.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 2:( Data science)

Day 1:

Data Analytics Communication, Data Types for Plotting, Data Types and Plotting

1.Implementation of installation of Pandas.

Day 2:

Data Analysis process using panda library of python.

Day 3:

Functions and methods used in pandas for the data analysis process.

2.implementation of panda’s methods.

Day 4:

Understanding the concept of DataFrame and series of pandas.

Day 5:

Pandas implementation with csv files.

3. work on dataframe and series.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 3:(NumPy Library)

Day 1:

Data Analytics through Numpy.

1.Implementation of installation of NumPy.

Day 2:

Data Analysis process using NumPy library of python.

Day 3:

Functions and methods used in NumPy for the data analysis process.

2.implementation of Numpy function for data analysis process.

Day 4:

Understanding the concept of Numpy in comparison with Pandas.

Day 5:

NumPy implementation with csv files.

3. work on NumPy Methods.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 4:(Machine learning)

Day 1:

Introduction about Machine Learning.

1.Practical of linear regression.

Day 2:

Supervised learning algorithm and unsupervised algorithm-linear regression, Logistic Regression

Day 3:

Polynomial regression, decision tree

2.Practical of Logistic regression.

Day 4:

Random forest, Ridge regression.

Day 5:

Lasso Regression and back propagation.

3. practical of random forest and ridge regression.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 1:(Classification algorithms)

Day 1:

Logistic classification.

1.practical of logistic and decision tree algorithm.

Day 2:

Decision tree classification.

Day 3:

Random forest classification.

2.implementation of random forest.

Day 4:

K-nearest neighbour classification.

Naïve bays classification and Support vector machine classification.

Day 5:

Clustering algorithms

3. work on project using classification algorithms.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 2:(Deep Learning)

Day 1:

Introduction about neural network and their phases.

1.implementation of CNN algorithm.

Day 2:

Introduction and implementation of Convolution Neural Networks: Image classification and Text classification algorithm of AI.

Day 3:

Single neuron neural network and multi-layer perceptron neural network.

2.implementation of vgg with 16 layers and 19 layers.

Day 4:

VGG16 and Vgg19 algorithm and introduction about activation functions of neural network.

Day 5:

Introduction about basic terms used in AI algorithms like strides, padding, pooling, filters, epochs and batch size etc.

3. work on single layer and multi-layer perceptron network.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 3(Deep learning algorithms)

Day 1:

Introduction about resnet algorithm and implementation with TensorFlow library.

1.practical of resnet algorithm.

Day 2:

Alexnet algorithm and practical implementation.

Day 3:

Introduction about Augmentation

Concept.

2.implementation of Alexnet algorithm.

Day 4:

YOLO algorithm for object detection.

Day 5:

Important concepts used in yolo algorithm: bounding boxes.

3. implementation of yolo algorithm and project work.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 4:(Deep learning algorithms)

Day 1:

Introduction about LSTM (Long-short term memory) algorithm.

1.implementaion of LSTM and RCNN algorithms.

Day 2:

RCNN (region convolutional neural network) algorithm.

Day 3:

Introduction about OpenCV and its implementation.

2.project work and implementation of OpenCV.

Day 4:

Work on OpenCV for image processing.

Day 5:

Project work on deep learning.

3. work on project using AI algorithms.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

ARTIFICIAL INTELLIGENCE COURSE

AI course duration

2 months

AI Course Mode

Offline/Online

AI Course Fee

15000/-

AI projects

Work on Live Projects of AI.

Free with AI Course

Project Report, Certificate, Synopsis and PPT.

Also provide International Artificial Intelligence Certificate