Artificial Intelligence and Machine Learning using Python @ Koneru Lakshmaiah Education Foundation, Off campus center, Hyderabad

Participants Information

Participants: 57
Speakers:06
Female participants:25
Male participants:32
Participants from SC/ST category:04
Research Scholars:03

Lab Sessions

  • Data Acquisition, Data Filtering, Data Imputation
  • Dimensionality reduction, Data Visualization
  • Introduction to Unsupervised ML, K-Means & Hierarchical Clustering
  • Association Rule Mining
  • Recommendation Systems
  • Classification, Logistic Regression and Linear Regression
  • CNN, RNN and LSTM

Topics Covered

  • Introduction to AI, ML and DL
  • A wholistic view with Analytics based approach
  • Problem type identification in ML and Introduction to Python programming
  • End-to-end life cycle of Machine Learning
  • Intoduction to Deep Learning MLP (Multi-Layer Perception)
  • Fuzzy Logic and its Applications
  • Neuro Fuzzy Systems
  • Applications of ANN
  • Learning Algorithms in Network Applications
  • Learning Algorithms and IoT Systems
  • Application Modelling in ML & AI for Large Data Sets - National Perspectives

Highlights

List of Speakers

Dr. Rashmi Ranjan Rout, Associate Professor, NIT Warangal


Dr. V. C. V. Rao, CDAC, Pune


Neelima Vobugari, CEO, Tarah Technologies, Bangalore


Mr. Fazhluddin Shaik, Data Analyst, Tarah Technologies, Bangalore


Mr. Mahesh Kumar C. V, Senior Data Analyst, Tarah Technologies, Hyderabad


Dr. Manjubala Bisi, Assistant Professor, NIT Warangal

Feedback Summary

  • Course Organization, planning and administrative aspects were excellent.
  • Eminent Speakers from reputed organizations.
  • All the sessions were affective, Dr. VCV. Rao session gave more applications of AI & ML.
  • The methodology and learning resources used were the major strength of the program.
  • Practical sessions along with theory was excellent.
  • Very good Accommodation and hospitality.
  • Suggestions from Participants

  • Required more hands on sessions on hardware and software.
  • Required more explanation about research topics related to ML.