GET INSPIRED BY OUR SOLUTION

Statistical data analysis are immensely useful in solving economic problems such as wages, price, time series analysis, demand analysis. We can use the skills in areas of work in Insurance, mortality, marketing, public health, biology, even sports. 

Employment of statisticians is projected to grow 31 percent from 2018 to 2028, much faster than the average for all occupations. Growth is expected to result from more widespread use of statistical analysis to make informed business, healthcare etc.

Data science profoundly influences everything from business decisions to national security to what consumer products we buy. It impacts retail markets, solves public health dilemmas,Improves our manufacturing process to produce a better product.

We can better predict future disease outbreaks using the real-time exchange of clinical health information. A recent study on hiring trends  in India indicates that 97,000 positions related to data science are currently vacant due to a dearth of skills. 

Machine learning brings together computer science and statistics to harness that predictive power. It's a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions. 

Machine Learning can be a rewarding career for students who are good in mathematics and statistics and have programming skills. The field of Machine Learning offers a promising career path with lucrative salaries. 

Course Covers In depth- R Programming, Python Programming, Advanced Applied Statistical Techniques.

Main Topics are Linear Regression, Logistic Regression, K nearest neighbors and Decision Trees, Support Vector machine, Random Forest, and Naive Bayes. 

Dimensionality Reduction, k-means Clustering,Hidden Markov Model,DBSCAN Clustering, Principal Component Analysis, Singular Value Decomposition, Association Rule 

Course Covers In depth- R Programming, Python Programming, Advanced Applied Statistical Techniques.

Main Topics are Linear Regression, Logistic Regression, K nearest neighbors and Decision Trees, Support Vector machine, Random Forest, and Naive Bayes. Dimensionality Reduction, k-means Clustering, Hidden Markov Model, DBSCAN Clustering, PCA

MLP,CNN,RNN,GAN,LLM,Transformers,

BERT,GPT,  Generative  AI, Creating Intelligent Agents with Reinforcement Learning,

Course Covers Neural Networks, DL, Autoencoders, RBM, MLP,CNN,RNN,

GAN,LLM,Transformers,BERT,GPT,NLPsTokenisation Stemming and Lemmatisation Transforming Text into Structured Form One-hot Vectorisation, Word Embeddings Word2Vec Model Reinforcement Learning Principles and Tools Exploitation vs Exploration Dilemma, Markov Decision Process, Q-Learning

Introduction to LLMs

Transformers and Attention Mechanism

Retrieval Augmented Generation

Generative AI 

Understanding GPT  Practical Application of GPT