continuous learning
- Supervised Learning Model - Regression
- Simple linear regression in python
- Multiple Linear Regression
- Multiple Linear Regression in Python
- Industry Relevance of Linear Regression
- Supervised Learning Model - Classification
- Univariate Logistic Regression
- Multivariate Logistic Regression - Model Building
- Multivariate Logistic Regression - Model Evaluation
- Logistic Regression - Industry Applications
- Unsupervised Learning: Clustering
- Introduction to Clustering
- K Means Clustering
- Executing K Means in Python
- Hierarchical Clustering
- Tree Models
- Introduction to Decision Trees
- Algorithms for Decision Tree Construction
- Hyperparameter Tuning in Decision Trees
- Ensembles and Random Forests
- Time Series Forecasting - I (BA)
- Introduction to Time Series
- Smoothing Techniques
- Practice Questions
- Common Interview Questions
- Time Series Forecasting - II (BA)
- Introduction to AR Models
- Building AR Models
- Practice Questions
- Model Selection
- Principles of Model Selection
- Model Building and Evaluation
- Introduction to NLP
- What is NLP?
- History and evolution of NLP
- Applications of NLP
- Challenges in NLP
- Overview of NLP pipeline
- Corpus and Corpus Linguistics
- NLTK Toolkit
- Introduction to the NLTK toolkit
- Preprocessing text data with NLTK
- Basic NLP tasks using NLTK (e.g., Part-of-Speech Tagging, Named Entity Recognition)
- Stemming and Lemmatization
- WordNet in NLTK
- Chunking and Chinking
- Sentiment Analysis with NLTK
- Tokenization and Topic Modeling
- Tokenization in NLP
- Bag-of-Words representation
- Topic Modeling with LDA
- Latent Semantic Analysis
- Word Embeddings
- Sentiment Analysis Project:
- Introduction to Sentiment Analysis
- Sentiment Analysis using supervised and unsupervised methods
- Building a Sentiment Analysis model with Python
- Evaluating Sentiment Analysis models
- Power AI vs Deep Learning vs ML BI
- Introduction to Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
- Applications of AI, ML, and DL
- Differences between AI, ML and DL
- The Concept of Neural Networks
- Introduction to Neural Networks
- Types of Neural Networks
- Layers in Neural Networks
- Activation Functions
- Neural Networks - Feed-forward, Convolutional, Recurrent
- Feed-forward Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Applications of Neural Networks
- Deep Learning Project
- Building a Deep Learning model with Python
- Image Classification with Convolutional Neural Networks
- Natural Language Processing with Recurrent Neural Networks
- Hyperparameter Tuning in Deep Learning
- Generative AI
- 1. Generative AI basics.
- 2. GANs: Generator vs. Discriminator.
- 3. VAEs: Encoding and Decoding.
- 4. Text Generation with RNNs and LSTMs.
- 5. Image Generation Techniques.
- 6. Music Generation with NNs.
- 7. Conditional GANs.
- 8. Style Transfer.
- 9. Evaluation Metrics.
- 10. Real-world Applications & Prompt Engineering.
Process of Placement Assistance
Enhance skills, Qualifications, Career aspirations as smooth transition into workspace
Demo Section
Students will be given a brief outlook of their respective courses. In this particular session students will get to know what kind of software's they are going to use, How training sessions are going to be conducted, How they are going to evaluate themselves going further.
- Students will be given a brief outlook of their respective course's.
- In this particular session students will get to know what kind of software's they are going to use.
- How training session's are going to be conducted.
- How they are going to evaluate themselves in future.
Onboarding
- Orienting new students to the organization. It encompasses all activities and procedures that help new students become more productive
- Here students will be given access to the necessary tools, software's, resources required for the training.
- Students will engage with their respective mentors.
Orienting new students to the organization. It encompasses all activities and procedures that help new students become more productive Here students will be given access to the necessary tools, software's, resources required for the training, Students will engage with their respective mentors.
Learning
- Mentors will observe the students during class activities, discussions, understanding and active participation in the tasks.
- Mentors will help in achieving academic success and also share continuous feedback.
- Mentors also provide guidance, support and advice to less experienced students.
Mentors will observe the students during class activities, discussions, understanding and active participation in the tasks. Mentors will help in achieving academic success also shares continuous feedback.
Test Assignment
- The test will be conducted weekly once on their respective courses.
- It can help students to identify their strengths and weaknesses so they can focus on areas that need improvement.
- Successfully completing tasks and achieving good grades will boost students' confidence.
The test will be conducted weekly once on their respective courses, it can help students to identify their strengths and weaknesses so they can focus on areas that need improvement.
Communication Club
- Communication Club will be taken daily for the students.
- Students will receive their respective scores and suggestions.
- It will help students for active participation in the real time interviews.
Communication Club will be taken daily for the students. Students will receive their respective score and suggestions.
Mock Interviews
- Mock Interviews will be taken every Friday.
- It helps you to develop interview strategies, enhance your communication skills, and ease the nerves that many experience before an actual job interview.
- Mock interviews may be conducted by professionals or recruiters in the field.
Mock Interviews will be taken on every Friday. It helps you to develop interview strategies, enhance your communication skills, and ease the nerves that many experiences before an actual job interview.
Profile Building
- Portfolio, Resume & LinkedIn profile building
After completing the 70% of course. Mentors will assist the students on profile building like, Portfolio, Resume and LinkedIn profile.
Job Portal
- After completion of profile building, the job portal access will be given to students.
- It simplifies the job search process,
- The job assistance will be up-to one year.
After completion of profile building, the job portal access will be give to students. It simplify the job search process, The job assistance will be up to one year.
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