Hands-On Machine Learning AI, Gen AI & Agentic AI with Internship
11th April, 2025
Live Weekday Instructor Led Classes Starts from
Timing: 8.00 PM - 9:30 PM (IST)
- Duration - 3Months
- Mode of Learning - Live Instructor Led Weekday Classes
- Monday - Friday
Note: Minimum Python Knowledge learners, can join this program!
Job Expectations From Data Scientist/ML Engineer
Python or SQL May Not Be Sufficient? Why?
Beyond just writing machine learning models, clients expect Data Scientists to convert complex algorithms into measurable business outcomes, such as increased sales, optimized operations, or better risk management
Do You Have This Kind of Data Science Projects?
Every data science project should contribute to measurable business improvements, whether through automation, predictive analytics, or customer segmentation that enhances profitability and efficiency
Being Too Technical, May Not Help You!!
Clients look for data-driven solutions that are not just technically sound but also scalable, efficient, and seamlessly integrated into existing business workflows
Communicating Insights in a Business-Friendly Manner
Clients expect Data Scientists to bridge the gap between technical complexity and business strategy by presenting insights in clear, data-backed storytelling that influences decision-making
Corporate Clients
Live Class Agenda on ML, AI
Introduction to Machine Learning
- What is Machine Learning?
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Reinforcement Learning
Data Preprocessing and Feature Engineering
- Handling Missing Values
- Encoding Categorical Variables
- One-hot Encoding
- Label Encoding
- Target Encoding
- Feature Scaling
- Min-Max Scaling
- Standardization (Z-score normalization)
- Robust Scaling
- Normalization
- Feature Selection Techniques
- Filter Methods
- Correlation Coefficient
- Chi-square Test
- ANOVA Test
- Mutual Information
- Wrapper Methods
- Forward Selection
- Backward Elimination
- Recursive Feature Elimination (RFE)
- Embedded Methods
- Lasso Regularization
- Ridge Regularization
- ElasticNet
- Filter Methods
Dimensionality Reduction
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- t-distributed Stochastic Neighbor Embedding (t-SNE)
- Uniform Manifold Approximation and Projection (UMAP)
Cross-Validation and Evaluation Metrics
- Cross-Validation Techniques
- K-Fold Cross-Validation
- Stratified K-Fold
- Leave-One-Out Cross-Validation
- Performance Metrics
- Accuracy, Precision, Recall, F1-Score
- ROC and AUC
- Confusion Matrix
Machine Learning Algorithms
- Linear Regression
- Ordinary Least Squares
- Assumptions of Linear Regression
- Evaluation Metrics
- Logistic Regression
- Sigmoid Function
- Odds Ratio and Log-Odds
- Evaluation and Threshold tuning
- Decision Trees
- Splitting Criteria (Gini, Entropy)
- Pruning Techniques
- Ensemble Methods
- Bagging
- Random Forest
- Boosting
- AdaBoost
- Gradient Boosting
- XGBoost, LightGBM, CatBoost
- Bagging
- Support Vector Machines (SVM)
- Kernel Functions (Linear, Polynomial, RBF)
- Hyperparameter Tuning
- K-Nearest Neighbors (KNN)
- Distance Metrics
- Optimal Value of K
- Neural Networks (ANN)
- Perceptron
- Activation Functions (ReLU, Sigmoid, Tanh)
- Optimizers (SGD, Adam, RMSProp)
Introduction to NLP and NLU
- What is NLP and NLU?
- Differences between NLP, NLU, NLG
- NLP Pipeline
- Tokenization
- Lemmatization and Stemming
- Stop Word Removal
- POS Tagging
- Named Entity Recognition (NER)
NLP Techniques and Applications
- Word Embeddings
- Word2Vec
- GloVe
- FastText
- Language Models
- N-grams
- Probabilistic Models
- Text Classification
- Sentiment Analysis
- Spam Detection
- Sequence Modeling
- Hidden Markov Models (HMM)
- Conditional Random Fields (CRF)
Advanced NLP and NLU
- Topic Modeling
- Latent Dirichlet Allocation (LDA)
- Text Similarity
- Cosine Similarity
- Jaccard Similarity
- Transformers and Attention
- Self-Attention Mechanism
- Transformer Architecture
Prompt Engineering
- Introduction to Prompt Engineering
- Crafting Effective Prompts
- Prompt Tuning and Optimization
- Few-shot and Zero-shot Learning
- Chain-of-Thought Prompting
Retrieval-Augmented Generation (RAG)
- Basics of RAG
- Implementing RAG Systems
- Dynamic RAG
- Real-time Document Retrieval
- Dynamic Context Injection
Agentic AI
- Introduction to Autonomous Agents
- Building Agentic Systems
- Agent Frameworks (AutoGPT, BabyAGI)
- Applications and Ethics
LangChain
- Introduction to LangChain
- Core Components
- Building Applications with LangChain
- LangChain Use Cases
Generative AI and Advanced Models
- Variational Autoencoders (VAE)
- Generative Adversarial Networks (GANs)
- Auto-Regressive Models
- GPT Family (GPT-2, GPT-3, GPT-4)
- Diffusion Models
- Fine-Tuning Generative Models
Cloud-Based AI Platforms
- OpenAI API
- Integration and Deployment
- API Usage and Best Practices
- Amazon Bedrock
- Model Hosting and Deployment
- Advanced Model Integration
Practical Applications and Use-Cases
- Chatbots and Virtual Assistants
- Content Generation (Text, Images, Audio)
- Recommendation Systems
Ethical AI, Explainability, and Model Deployment
- Model Interpretability Techniques
- SHAP Values
- LIME
- Ethics in AI
- Model Deployment Best Practices
Hands-on Projects
- End-to-end ML Pipeline Project
- NLP-based Text Classification Project
- Generative AI Project (Fine-tuning GPT model for specific domain)
- Prompt Engineering and RAG Project
Contact us
we’re here to all your questions
How Can I Get Job in AI?
Many jobs in AI require a bachelor’s degree or higher. A degree in computer science, data science, artificial intelligence, mathematics, or a related field can be beneficial. However, some entry-level roles may only require an associate degree, certifications, or equivalent practical experience.
Data Science vs Artificial Intelligence
Data Science and Artificial Intelligence (AI) are closely related but serve different purposes.
- Data Science focuses on extracting insights from data using statistical analysis, machine learning, and data visualization techniques. It involves data cleaning, processing, and interpreting trends to drive business decisions.
- AI enables machines to simulate human intelligence, including learning, reasoning, and problem-solving. It encompasses fields like machine learning, deep learning, and natural language processing to automate tasks and improve decision-making.
Which is Better Career Data Science vs AI?
Choosing between Data Science and AI depends on your career goals.
- Data Science is ideal for those interested in working with large datasets, statistical analysis, and business intelligence. It offers roles such as Data Analyst, Data Engineer, and Data Scientist.
- AI is a better fit for those who want to develop intelligent systems, work on automation, and build AI-powered applications. AI careers include Machine Learning Engineer, AI Researcher, and Deep Learning Engineer.
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3 Steps for Successful Career Transition
We Will Help You Every Step Of The Way
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Get Detailed understanding about Job-Ready "T" Skillset
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Develop Confidence on Job-Ready Skillset by Implementing them on Real-Time Projects with BEPEC Internship
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Trustpilot Reviews!
“I have enrolled for the Data Science Career Transition Program through Bepec Kanth explains how to apply all the concepts in real world. Along with developing domain knowledge he is also helping to enhance our professional skills to face real world challenges as data scientist... What ? Why? Where ?.....3 words most important”
SREE SAI KONDLE
“First of all, I clearly got to know the role of DA/DS ,previously joined to one institute but i didnt get enough knowledge + no real time projects So, I joined here in oct batch everything is so perfect the way Kanth sir teaches and it is more like one should be consistent in learning. When it comes it support team they will always help u at ur queries will update about placement once i got placed.”
Harshini
“Hi I am grateful to Kanth and his team who worked very hard for career transition program into data science and AI field, the program was very smooth starting from the scratch to pro level. As I belong to mechanical field even I grabbed the insights very easily I think one must go for their program without any hesitations. I hope I would become principal data scientist one day. Thank you Kanth and his team”
Mayur Thakare
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