Description

 Course Description:

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptions, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open-source libraries used by leading tech companies in the machine learning field.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After Machine Learning:

• Machine Learning Engineer
• Robotics Engineer
• Cybersecurity Analyst
• Deep Learning Engineer
• Data Analyst
• Business Intelligence Developer
• NLP Engineer
• Recommendation System Engineer

Essential Skills you will Develop Machine Learning:

• Mathematics & Statistics
• Programming Skills
• Data Handling
• Machine Learning
• Model Evaluation & Production

Tools Covered:

• Number – Numerical computing
• Pandas – Data manipulation and analysis
• learn – Classical machine learning algorithms

Syllabus:

Module 1: Introduction to Machine Learning What is Machine Learning? Types: Supervised, Unsupervised, Reinforcement Learning ML vs AI vs Deep Learning
Applications and Use Cases ML Lifecycle.

Module 2: Python for Machine Learning Python Basics & Libraries Data Structures and Functions Data Manipulation and Visualization Notebooks & IDEs.

Module 3: Data Preprocessing and Feature Engineering Data Cleaning (Handling Missing, Duplicate Data) Data Encoding, Normalization, Standardization Feature Selection & Extraction Outlier Detection.

Module 4: Supervised Learning Linear Regression, Logistic Regression Decision Trees, Random Forests Support Vector Machines (SVM) k-Nearest Neighbors (-NN).

Module 5: Unsupervised Learning Clustering: K-means, Hierarchical, DBSCAN
Dimensionality Reduction: PCA, t-SNE Association Rule Mining (Aprior, FP-Growth).

Module 6: Model Evaluation and Selection Cross Validation Techniques Confusion Matrix, Accuracy, Precision, Recall, F1-Score ROC-AUC Curve Bias-Variance Tradeoff.

Module 7: Advanced Machine Learning Algorithms Gradient Boosting Ensemble Learning (Bagging & Boosting) Stacking and Voting Classifiers.

Module 8: Deep Learning Basics Introduction to Neural Networks Perceptron and Activation Functions Tensor Flow and Kera Basics Building Simple Neural Nets.

Module 9: ML in Practice ML Pipelines and Workflow Automation Model Deployment Basics (Flask) Version Control with Git ML Project Structure & Documentation.

Module 10: Capstone Project and Certification End-to-End ML Project (Real-time Dataset) Model Building, Evaluation, and Deployment Presentation of Results Final Assessment & Certification.

Industry Projects:

• Healthcare
• Finance
• Retail & E-commerce
• Automotive
• Manufacturing

Who is this program for?

• Researchers and Academics
• Students and Graduates
• Working Professionals
• Entrepreneurs and Product Managers
• Anyone Curious About AI

How To Apply

Mobile: 9100348679

Email: coursedivine@gmail.com