AICL is providing an intensive training camp for students to bag their knowledge and potential in their related domains. It’s going to be
15 days of intensive live training, and continuous engagement (WhatsApp support ) to students in Machine Learning and Deep learning Domains.
Each day the students will be engaged with one hour of live hands-on training (Theory + Practical implementation). There will be a mini project where the team will assist you in a brief manner. AICL offers you the best pricing in the market for SIST students at Rs 1200 per candidate and we require a minimum of 15 days of training schedule to enrich the knowledge to the students.
And that’s not all !!!
Days
Hours
Minutes
Seconds
What you need to know:
Limited seats – ‘first come first serve’ basis
Practical guidance from infosec practitioners
Hands-on project training with enriched content
What you will learn:
Chapter 1- Introduction
Getting a clear idea about Machine Learning,
Deep Learning and Artificial Intelligence.
What is Machine Learning and Application?
What is Deep Learning and Application?
What is Computer Vision and Application?
What is Natural Language Processing and Application?
What is Artificial Intelligence and Application?
Chapter 2 – Understanding Prerequisite
What is Programming Language and Why? Why Python?
What are the Virtual Environment and the Advantage of using it?
Anaconda for python and types of IDE.
Operating system: Windows and Ubuntu Installing Anaconda.
Setting up Virtual Environment for hands-on.
Installing Python Library.
Intuition followed by hands-on for each concept
Chapter 3 – Regression-Supervised Learning:
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Support Vector Machine
Regression Decision Tree Regression
Random Forest Regression
Chapter 4: Classification-Supervised Learning:
Logistic Classification
K-Nearest Neighbor(K-NN)
Support Vector Machine (SVM)
Kernel SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
Chapter 5: Clustering
Unsupervised Learning:
K-Means Clustering
Hierarchical Clustering
Chapter 6: Dimensionality Reduction
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Kernel PCA
Chapter 7: Deep Learning & Computer Vision
Artificial Neural Network-ANN
CNN-Convolutional Neural Network
Hands-on: (Keras and Tensorflow Library)
Hemorrhage prediction using Deep Learning
Note:
The Above Syllabus is solely structured for the Course Planned. The Course would nurture the Students to work on their PC Instead of Screen Sharing or PPT.
Who can apply:
Engineering / Arts students (or any domain – with a passion for learning)
We stand by your dreams to get placed in your dream Destination !!!