BRIDGE COURSE ON MACHINE LEARNING & DEEP LEARNINGSeptember 20, 2021 2021-09-20 12:05
BRIDGE COURSE ON MACHINE LEARNING & DEEP LEARNING
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 !!!
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
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 !!!