Certification in Data Intelligence Batch 2

About This Course

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing industries worldwide, from healthcare and finance to retail and cybersecurity. As businesses increasingly rely on data-driven decision-making, the demand for AI and ML professionals has skyrocketed. The Data to Intelligence: AI & ML in Practice certification program is designed to equip learners with the necessary skills to transition from handling raw data to building intelligent AI-driven solutions. This 3-month intensive program provides a structured, hands-on approach to AI and ML, ensuring a solid foundation and practical expertise in real-world applications.

Course Objectives

By the end of this certification program, participants will be able to:

Understand AI & ML Fundamentals: Gain knowledge of core AI/ML concepts, techniques, and real-world applications.
Process and Analyze Data: Learn data collection, cleaning, transformation, and feature engineering to improve model performance.
Develop Machine Learning Models: Implement supervised and unsupervised learning techniques for predictive analytics and decision-making.
Master Deep Learning & Neural Networks: Build and train neural networks for applications like image recognition and NLP.
Optimize and Tune AI Models: Improve accuracy and efficiency using hyperparameter tuning and optimization strategies.
Deploy AI Models in Production: Learn model deployment techniques using Flask, FastAPI, and cloud platforms like AWS and Google Cloud.
Apply AI in Various Industries: Understand how AI is used in healthcare, finance, retail, and cybersecurity.
Ensure Ethical AI Practices: Learn about fairness, bias mitigation, and responsible AI development.
Complete a Capstone Project: Develop a real-world AI/ML solution from scratch, demonstrating end-to-end expertise.

Learning Objectives

1. Understand fundamental AI and ML frameworks, methodologies, and problem-solving strategies.

2. Develop expertise in supervised and unsupervised learning, feature engineering, and model selection for practical applications.

3. Gain proficiency in advanced machine learning techniques, including logistic regression, SVM, dimensionality reduction, neural networks, and decision trees.

4. Apply reinforcement learning, PCA, and graphical models to real-world AI problems, improvingpredictive accuracy and decision-making

5. Learn optimization strategies and parameter estimation for high-performance models.

6. Explore unsupervised learning, clustering techniques, and ensemble methods.

Material Includes

E Learning materials

Target Audience

Data Scientists amp; ML Engineers – Building and deploying AI/ML models.
Software Developers – Integrating AI into applications.
Business Analysts & Decision Makers – Leveraging AI for strategic insights.
Researchers & Academics – Exploring AI methodologies and applications.
Students & AI Enthusiasts – Gaining hands-on experience in AI/ML.
Product Managers & Tech Executives – Understanding AI-driven innovation.
Industry Professionals – Applying AI across sectors like finance, healthcare, and tech.

Curriculum

24 Lessons2160h

MODULE 1 – Foundations of Artificial Intelligence & Machine Learning

1.1 AI Frameworks and Methodologies16:20

1.2 Problem solving in AI and current trends00:16:20

1.3 Machine Learning Essentials00:16:20

1.4 Supervised, Unsupervised, Semi-supervised Learning, Featurization, Model Selection and Tuning00:9:25

1.5 Discriminative Models: Least Squares Regression00:9:25

1.6 Gradient Descent Algorithm, Univariate and Multivariate Linear Regression00:9:25

1.7 Prediction Modeling00:7:22

1.8 Probabilistic Interpretation and Regularization00:7:22

Quiz 1

SDP SESSION – 5TH APRIL

SDP Lecture 2

SDP Lecture 3

MODULE 2: Advanced Machine Learning Techniques and Operations

MODULE 3: Unsupervised Learning, Clustering, and Neural Networks

Your Instructors

Anjali Baisla

5.0/5

4 Courses

2 Reviews

225 Students

Certification in Data Intelligence Batch 2

About This Course

Course Objectives

By the end of this certification program, participants will be able to:

Understand AI & ML Fundamentals: Gain knowledge of core AI/ML concepts, techniques, and real-world applications.

Process and Analyze Data: Learn data collection, cleaning, transformation, and feature engineering to improve model performance.

Develop Machine Learning Models: Implement supervised and unsupervised learning techniques for predictive analytics and decision-making.

Master Deep Learning & Neural Networks: Build and train neural networks for applications like image recognition and NLP.

Optimize and Tune AI Models: Improve accuracy and efficiency using hyperparameter tuning and optimization strategies.

Deploy AI Models in Production: Learn model deployment techniques using Flask, FastAPI, and cloud platforms like AWS and Google Cloud.

Apply AI in Various Industries: Understand how AI is used in healthcare, finance, retail, and cybersecurity.

Ensure Ethical AI Practices: Learn about fairness, bias mitigation, and responsible AI development.

Complete a Capstone Project: Develop a real-world AI/ML solution from scratch, demonstrating end-to-end expertise.

Learning Objectives

Material Includes

Target Audience

Curriculum

MODULE 1 – Foundations of Artificial Intelligence & Machine Learning

1.1 AI Frameworks and Methodologies16:20

1.2 Problem solving in AI and current trends00:16:20

1.3 Machine Learning Essentials00:16:20

1.4 Supervised, Unsupervised, Semi-supervised Learning, Featurization, Model Selection and Tuning00:9:25

1.5 Discriminative Models: Least Squares Regression00:9:25

1.6 Gradient Descent Algorithm, Univariate and Multivariate Linear Regression00:9:25

1.7 Prediction Modeling00:7:22

1.8 Probabilistic Interpretation and Regularization00:7:22

Quiz 1

SDP SESSION – 5TH APRIL

SDP Lecture 2

SDP Lecture 3

MODULE 2: Advanced Machine Learning Techniques and Operations

SDP SESSION – 24TH MAY

SDP SESSION – 31ST MAY

2.1 Logistic Regression and Multiclass Classification00:7:22

2.2 Support Vector Machines (SVM), Large Margin Classifiers, and Nonlinear SVM00:13:24

2.3 Kernel Functions and SMO Algorithm00:13:24

2.4 Dimensionality Reduction, Subset Selection, and Shrinkage Methods00:8:01

2.5 Principal Components Regression, Linear Classification, and Logistic Regression00:8:01

2.6 Linear Discriminant Analysis Optimization and Separating Hyperplanes Classification00:8:01

2.7 Neural Networks: Early Models, Backpropagation, Training & Validation00:10:26

2.8 Parameter Estimation (Maximum Likelihood and Bayesian)00:10:26

2.9 Decision Trees, Hypothesis Testing, and Ensemble Methods00:10:26

MODULE 2 – QUIZ

MODULE 3: Unsupervised Learning, Clustering, and Neural Networks

SDP SESSION – 7TH JUNE

SDP SESSION – 14TH JUNE

3.1 Clustering, Gaussian Mixture Models, and Spectral Clustering00:13:30

3.2 Reinforcement Learning and Dimensionality Reduction00:13:30

3.3 Principal Component Analysis (PCA)00:13:30

3.4 Generative Models: Linear Discriminative Analysis and Naïve Bayes Classifier00:14:10

3.5 Decision Trees and Ensemble Models (Bagging and Boosting)00:14:10

3.6 Graphical Models and Evaluation Measures00:10:26

3.7 Applications of Ensemble Methods in Supervised and Unsupervised Learning00:6:13

MODULE 3 – QUIZ

Your Instructors

Anjali Baisla

Write a review

Material Includes

Strategic HR Management and Organization Development Batch 2

Certification in Advanced Marketing Management Batch 2

Data Science