GRAStech

Description

AI/ML Master Program: A Comprehensive Journey with GRAStech

GRAStech's AI/ML Master Program is designed to equip learners with the essential skills and knowledge to excel in the rapidly evolving field of artificial intelligence and machine learning. Through a carefully curated curriculum, participants will delve into foundational concepts, advanced algorithms, and real-world applications.

From understanding the basics of data science to mastering complex techniques like deep learning and natural language processing, the program offers a comprehensive learning experience. GRAStech's expert instructors provide personalized guidance and hands-on training, ensuring that learners gain practical skills that are highly sought after by industry leaders.

Key Industries Hiring AI & ML Professionals

Technology and Software
 Companies: Google, Microsoft, IBM, Infosys, TCS, Wipro, Accenture
 Roles: AI Research Scientist, Machine Learning Engineer, Data Scientist

E-commerce and Retail
 Companies: Amazon, Flipkart, Myntra
 Roles: Data Analyst, Machine Learning Engineer, AI Product Manager

Healthcare
 Companies: Practo, Philips, MedGenome
 Roles: Data Scientist, AI Research Scientist, Healthcare Analytics Specialist
Finance and Banking
 Companies: HDFC Bank, ICICI Bank, JPMorgan Chase

 Roles: Risk Analyst, Fraud Detection Specialist, Algorithmic Trading Analyst
Automotive
 Companies: Tata Motors, Mahindra & Mahindra, Mercedes-Benz
 Roles: Robotics Engineer, AI Research Scientist, Data Scientist
Telecom
 Companies: Reliance Jio, Bharti Airtel, Vodafone Idea
 Roles: Network Optimization Analyst, AI Engineer, Data Analyst
Start-ups and Innovation Hubs
 Companies: ZestMoney, Niramai, Locus
 Roles: Various roles including AI/ML Engineers, Data Scientists, Product Managers

Educational and Professional Qualifications
 Degrees: Bachelor's or Master's in Computer Science, Data Science, Statistics,
Mathematics, or related fields.
 Certifications: Specialized AI/ML certifications from institutions like Coursera, edX,
or Udacity.
 Skills: Proficiency in programming languages (Python, R), knowledge of machine
learning frameworks (TensorFlow, PyTorch), strong analytical and problem-solving
skills.
Locations with High Demand
 Bengaluru (Bangalore): Known as the Silicon Valley of India, with numerous tech
firms and startups.
 Hyderabad: A major IT and business hub with a growing AI/ML presence.
 Pune: Emerging as a key player in tech and innovation.
 Delhi NCR: Includes Gurgaon and Noida, with many tech and finance companies.
 Chennai: Growing in the tech sector with increasing job opportunities in AI/ML.

What You will Learn?

Math- linear algebra Statistics - inferential and descriptive and probability Programming language- Python Data preprocessing techniques Data exploration
visualization techniques Machine learning algorithms Deep learning

Topics for this course

Total learning: 62 Topics

Definition of AI and ML

Historical context and evolution

Key applications and industries

Narrow AI vs. General AI

Super intelligent AI

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Vectors, matrices, and tensors

Matrix operations

Derivatives and gradients

Optimization techniques (e.g., gradient descent)

Probability distributions

Statistical inference

Hypothesis testing

Data acquisition techniques

Web scraping and APIs

Data cleaning and transformation

Feature selection and engineering

Handling missing values and outliers

Techniques and tools (e.g., Matplotlib, Seaborn)

Exploratory Data Analysis (EDA)

Linear Regression

Polynomial Regression

Evaluation metrics (e.g., MSE, RMSE)

Logistic Regression

Decision Trees

k-Nearest Neighbors (k-NN)

Support Vector Machines (SVM)

Evaluation metrics (e.g., accuracy, precision, recall, F1 score)

k-Means Clustering

Hierarchical Clustering

DBSCAN

Principal Component Analysis (PCA)

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Neurons and activation functions

Feedforward Neural Networks

Backpropagation and optimization

Regularization techniques (e.g., dropout, L2 regularization)

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Generative Adversarial Networks (GANs)

Transformers and Attention Mechanisms

Agents, environments, and rewards

Markov Decision Processes (MDPs)

Q-Learning

Deep Q-Networks (DQN)

Policy Gradient Methods

Cross-validation techniques

Hyperparameter tuning

Tools and platforms (e.g., Docker, TensorFlow Serving)

APIs and microservices

Model drift and retraining

Performance monitoring

Ethical Considerations

Bias and fairness in AI

Privacy concerns and data protection

Emerging technologies

AI in society and industry

Programming Languages: Python, R

Libraries and Frameworks: Scikit-learn, TensorFlow, Keras, PyTorch, Pandas, NumPy, Matplot

Development Environments: Jupyter Notebook, Anaconda

Opportunities after this course completion

Artificial intelligence (AI) engineer, Machine learning engineer, Data engineer, Robotics engineer, Software engineer, Data scientist, AI Data Analyst, NLP engineer, Researcher, Data Mining

Price: INR 55000

This Course Includes

Skill level : Advanced
Duration : 4 Months
Chapters : 38
Topics : 62
Language : English
Certificate : Yes

AI ML Master Program

Description

What You will Learn?

Topics for this course

1. Introduction to AI and ML

1. Introduction to AI and ML

Overview of AI and ML

Overview of AI and ML

Types of AI

Types of AI

Types of Machine Learning

Types of Machine Learning

Mathematical Foundations

Mathematical Foundations

Linear Algebra

Linear Algebra

Calculus

Calculus

Probability and Statistics

Probability and Statistics

Data Handling

Data Handling

Data Collection and Sources

Data Collection and Sources

Data Pre-processing

Data Pre-processing

Data Visualization

Data Visualization

Supervised Learning

Supervised Learning

Regression

Regression

Classification

Classification

Unsupervised Learning

Unsupervised Learning

Clustering

Clustering

Dimensionality Reduction

Dimensionality Reduction

Neural Networks and Deep Learning

Neural Networks and Deep Learning

Basics of Neural Networks

Basics of Neural Networks

Neural Networks

Neural Networks

Advanced Neural Network Architectures

Advanced Neural Network Architectures

Reinforcement Learning

Reinforcement Learning

Basics of Reinforcement Learning

Basics of Reinforcement Learning

Key Algorithms

Key Algorithms

Model Deployment and Production

Model Deployment and Production

Model Evaluation and Selection

Model Evaluation and Selection

Model Deployment

Model Deployment

Monitoring and Maintenance

Monitoring and Maintenance

Ethics and Future Directions

Ethics and Future Directions

Ethical Considerations

Ethical Considerations

Future Trends in AI and ML

Future Trends in AI and ML

Recommended Tools and Libraries

Recommended Tools and Libraries

 Assignments - 3

 Assignments - 3

 Real Time Projects - 3

 Real Time Projects - 3

 Certifications –NSDC &amp; GRAStech

 Certifications –NSDC &amp; GRAStech

 Paid Certifications – IBM &amp; Microsoft

 Paid Certifications – IBM &amp; Microsoft

 Fully job supports

 Fully job supports

 Certifications –NSDC & GRAStech

 Paid Certifications – IBM & Microsoft