AI/Machine Learning

Al makes today’s machines more intelligent, more accurate, and more efficient. It presents the knowledge of artificial intelligence principles and approaches. It helps in developing an understanding of the building block of artificial intelligence in terms of intelligent agents, knowledge, logic, and learning.

Getting the reason why Al is so famous in the market. Understand the implementation of artificial intelligence in different sectors like business, health care, vehicle, education, security, etc. To impart basic proficiency in representing difficult real-life problems in a state space representation to solve them using Al techniques like searching and game playing

Machine Learning:

Machine Learning is a branch of Al that is behind almost all applications we have nowadays starting from a basic computation algorithm to complex computation algorithms. It is playing a significant role in developing new algorithms that permit software applications to predict/ achieve accurate results without being explicitly programmed.

Machine learning using Python / using R:

Machine Learning studies on various algorithms convert someone in the understanding of the power of machine learning in almost all applications of the market, nowadays.

The Takeaway will be mastery of the concept of Deep Learning, Machine Learning algorithms, pre-processing, and evaluation of results, and statistics with real-time case studies.

Python programming platform helps provide all the solutions, most simply. The biggest achievement of machine learning is the advantage over explicit programming and making the system auto-updated.

Key Topics Covered:

• Artificial Intelligence
• Introduction to Al
• Statistic essentials
• Pre-program preparation
• Python Programming
• Algorithm design using Machine learning
• OPEN-CV APINLP(nitk) for language processing
• Deep Learning and Neural Network
• Training using backpropagation
• Convolution Neural Network
• Reinforcement learning
• BFS and DFS path searching
• Machine Learning
• Preliminaries of statistics
• R programming for machine learning
• Python programming for machine learning
• Statistic programming using Python and R
• Pre-processing using Python and R
• Supervised learning
• Classification
• Decision treeH GINI
• Entropy
• Support vector machine
• Bayesian network (BNB, GNB, MNB)
• KNN
• Random Forest
• Regression
• Logistic regression
• Linear regression
• Multinomial regression
• Poisson regression
• Negative binomial regression
• Machine Learning
• Ridge regression
• Lasso Regression
• Unsupervised learning
• Clustering
• Hierarchical
• K-means
• K-mediodsl
• CLARAH
• DBSCAN
• Dimension Reduction
• Text mining using NLP
• PCA
• Word Cloud
• Survival Analysis
• Deep learning(Neural Network)
• ANN for classification problem
• ANN for regression problem
• RNN for time and sequence
• related problems