Diploma in Artificial Intelligence & Machine Learning

Live

Fee:

₹60,000

Dr. Priti Maheshwary | Professor Future Skills Academy

₹60,000

Dr. Priti Maheshwary

12 Months

90

40 2 Hours

AISECT Learn

English

1 year Access

Yes

This course is designed for aspiring professionals looking for a career transition into the AI & ML space, who have basic programming skills, an interest in technology, and an analytical bent of mind. The learners will get acquainted with the fundamentals of Python, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, and Deployment of a cloud platform.

Objectives

How to discover patterns in the user data and then make predictions based on these intricate patterns for answering business questions and solving business problems.

How to make machines learn using different ML algorithms.

How to develop interaction between humans and computers using natural language.

How to enable computers and systems to derive meaningful information from digital images, videos, and other visual inputs.

How deploying an application through one or more hosting models—software as a service (SaaS), platform as a service (PaaS) and/or infrastructure as a service (IaaS) can be implemented.

What Will You Learn

With this course, you will learn: Python programming, Python libraries Numpy, Pandas, Matplotlib, Seaborn, Supervised Machine Learning, Unsupervised Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, and Deployment on AWS cloud.

Linear Algebra, Calculus, Statistics, Applied Math, and Algorithms

Machine Learning, Neural Network Architectures, Audio, and Video Processing

Computer Vision, Signal Processing Techniques, cloud deployment basics

SKILLS YOU WILL GAIN

Linear Algebra, Calculus, Statistics, Applied Math, and AlgorithmsMachine Learning, Neural Network Architectures, Audio, and Video ProcessingComputer Vision, Signal Processing Techniques, cloud deployment basics

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Curriculum

Chapter 1 : Introduction to Artificial Intelligence

Chapter 2 : Introduction to machine Learning

Chapter 3 : Introduction to Deep Learning

Chapter 4 : Real World Applications of AI/ML

Chapter 5 : Ethics in Machine Learning

Chapter 6 : Evaluation of Machine Learning codes

Chapter 1 : Python Statistics Libraries

Chapter 2 : Vectors

Chapter 3 : Matrix

Chapter 4 : Probability and Statistics

Chapter 5 : Descriptive Statistics

Chapter 6 : Random Numbers

Chapter 1 : Introduction to Python

Chapter 2 : Python Language Syntax & Data Types

Chapter 3 : Conditions/Loops

Chapter 4 : Libraries in Python

Chapter 5 : Numpy

Chapter 6 : Panda

Chapter 7 : Data Story Telling

Chapter 8 : Data Acquisition

Chapter 9 : Data Cleaning and Preprocessing

Chapter 10 : Data Manipulation Using Excel

Chapter 11 : Data Manipulation using MySQL

Chapter 12 : Advance Queries

Chapter 13 : Data Visualization Using Python

Chapter 14 : Data Visualization Using PowerBI

Chapter 1 : Introduction to Supervised Learning

Chapter 2 : Linear Regression

Chapter 3 : Multiple Linear Regression

Chapter 4 : Logistic Regression

Chapter 5 : SVM

Chapter 6 : KNN

Chapter 7 : Random Forest Algorithm

Chapter 8 : Naïve Bayes

Chapter 9 : Types of Learning

Chapter 10 : Clustering in Machine Learning

Chapter 11 : K means Clustering

Chapter 12 : Elbow Method for optimal value of k in Kmeans

Chapter 13 : Mean-shift Clustering

Chapter 14 : Fuzzy Clustering

Chapter 1 : Introduction Reinforcement Learning

Chapter 2 : Q-Learning Algorithms

Chapter 3 : Introduction to Thompson Sampling

Chapter 4 : Genetic Algorithm for Reinforcement Learning

Chapter 5 : SARSA Reinforcement

Chapter 6 : Q-Learning in Python Example

Chapter 7 : Story Telling

Chapter 1 : Tensor Flow Basic

Chapter 2 : Tensor Flow Perceptron

Chapter 3 : CNN in TensorFlow

Chapter 4 : RNN in TensorFlow

Chapter 5 : Introduction to Keras

Chapter 6 : Distributed Computing TensorFlow

Chapter 7 : Multi Layer Perceptron Learning

Chapter 8 : Image Recognition using TensorFlow

Chapter 9 :The Role of Cloud Computing in Artificial Intelligence

Chapter 10 : Cloud deployment ,its types and popular service providers

Chapter 11 : Building the model using Jupiter notebook

Chapter 12 : Export the model using Pickle

Chapter 13 : Build a Flask website to serve the model

Chapter 14 : Steps in Deploying model to aws

Chapter 15 : Introduction to GIT and GITHUB ,installation and creating repository

Chapter 1 : Introduction to Natural Language Processing

Chapter 2 : Steps to Train Dataset for NLP

Chapter 3 : Text Preprocessing in Python

Chapter 4 : Removing stop words with NLTK in Python

Chapter 5 : Tokenize text using NLTK in Python

Chapter 6 : Restaurant Reviews Project

Chapter 1 : Introduction to Artificial Neural Networks

Chapter 2 : Structure of Neural Network

Chapter 3 : Characteristics of ANN

Chapter 4 : Types Neural Network

Chapter 5 : Implementing ANN Training process in Python

Chapter 1 : Introduction to Computer Vision?

Chapter 2 : Computer vision and Image processing

Chapter 3 : Installing useful packages

Chapter 4 : Introduction to OpenCV

Chapter 5 : Reading , Writing and displaying an image

Chapter 6 : Color Space Conversion

Chapter 7 : Edge Detection

Chapter 8 : Face Detection

Chapter 9 : Eye detection

Instructor

Dr. Priti Maheshwary

Professor at Future Skills Academy

Presently working in Future Skill Academy. Involved in Computer Science and Engineering for over 20 years in higher education and training. Her career has included various software development projects, teaching, research and administrative roles. She enjoys teaching and looking into how to improve student learning experience. Published around 50 research papers in refereed journals and conferences, 7 book chapters, 6 Patents. 8 PhD thesis completed under her guidance in the field of Internet of Things, Smart Cities, Ubiquitous Computing, Wireless Sensor Network, VANET, Image Processing specialized in Satellite Images, AI/ML & Deep Learning, and Cyber Security. Also done more than 10 projects in the field of Research and Consultancy.

4.0 853

1038 Learners

Dr. Pooja Bijlani

Trainer (Subject Expert) Future Skill Academy

AI ML , Data science, Full Stack Web Development , Full Stack Java Development

Presently she is working as a IT Trainer (Subject Expert) at Future Skill Academy, AISECT, Bhopal. She has over 15 years of academic and research experience. Expert in Programming Languages C,C++,Python , Java ,React Spring Boot, Artificial Intelligence , Machine Learning and Data Science. Students accolades her programming language teaching skills . Having experience in number of End to End projects in Natural language Processing , Computer Vision and Python Desktop Application GUI. Web Designing.

4.0 968

1087 Learners

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