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Artificial Intelligence and Machine Learning Syllabus

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Our Machine Learning Training in OMR is perfect for anyone interested in learning about artificial intelligence. In this course, you will explore key machine learning concepts and tools. You’ll learn how to create algorithms that help computers learn from data and make predictions. With hands-on projects and real-world examples, you will gain practical experience in areas like supervised and unsupervised learning, data preparation, and testing models. We focus on popular programming languages like Python and useful libraries such as TensorFlow and Scikit-learn. By completing our Machine Learning Course in OMR with Certifications and Placements, you will acquire the skills needed for a successful career in data science and analytics. Join us to take your first step into the exciting field of machine learning!

At SLA Institute, we guarantee placement in a high-paying Developer job with the support of our experienced placement officers. Our Machine Learning Course Syllabus covers all essential topics, providing you with a comprehensive understanding of Machine Learning.

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Upcoming Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
January 2025
Week days
(Mon-Fri)
Online/Offline

2 Hours Real Time Interactive Technical Training 

1 Hour Aptitude 

1 Hour Communication & Soft Skills

(Suitable for Fresh Jobseekers / Non IT to IT transition)

Course Fee
January 2025
Week ends
(Sat-Sun)
Online/Offline

4 Hours Real Time Interactive Technical Training

(Suitable for working IT Professionals)

Course Fee

Save up to 20% in your Course Fee on our Job Seeker Course Series

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Placement

100% Assistance

Learning

Job-Centered Approach

Timings

Convenient Hrs

Mode

Online & Classroom

Certification

Industry-Accredited

This Course Includes

  • FREE Demo Class
  • 0% EMI Loan Facilities
  • FREE Softskill & Placement Training
  • Tie up with more than 500+ MNCs & Medium Level Companies
  • 100% FREE Placement Assistance
  • Course Completion Certificate
  • Training with Real Time Projects
  • Industry-Based Coaching By MNC IT Professionals
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Expected Criteria for Assured Placement

The following criteria help the placement team guide the candidates to get placed immediately after the course completion through SLA Institute.

  • 80% of coursework completion helps us arrange interviews in required companies.
  • 2 or 3 projects to be done for the selected course to ace the technical round effectively.
  • Ensure attending the placement training right from the first day of the selected course.
  • Practice well with resume building, soft skill, aptitude skill, and profile strengthening.
  • Utilize the internship training program at SLA for the complete technical skills.
  • Collect the course completion certificate and update the copy to the placement team.
  • Ensure your performance indicator meets the expectation of top companies.
  • Always be ready with the updated resume that includes project details done at SLA.
  • Enjoy unlimited interview arrangements along with internal mock interviews.
Have Queries? Ask our Experts

+91 89256 88858

SLA's Distinctive Placement Approach

1

Tech Courses

2

Expert Mentors

3

Assignments & Projects

4

Grooming sessions

5

Mock Interviews

6

Placements

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Artificial Intelligence and Machine Learning Syllabus Course Syllabus

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Join our Machine Learning Course in OMR at SLA Institute to enhance your career in data science. Our complete program covers everything from basic ideas to advanced algorithms in machine learning. You’ll learn how to analyze data, create predictive models, and use machine learning methods for real-world issues. With hands-on projects and personalized job placement help, you’ll gain useful experience to succeed in the field. Our skilled instructors will support you throughout your training, making sure you are ready for a rewarding career in machine learning. Enroll today to explore great job opportunities with SLA Institute!

Module 1: Introduction to AI and Machine Learning
  • Overview of Artificial Intelligence (AI) and Machine Learning (ML)
  • History, evolution, and applications of AI and ML
  • Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
  • Tools and technologies used in AI and ML
Module 2: Python for AI and ML
  • Introduction to Python programming
  • Data structures, libraries, and functions in Python
  • NumPy, Pandas, and Matplotlib for data manipulation and visualization
  • Scikit-learn for machine learning algorithms
Module 3: Data Preprocessing and Feature Engineering
  • Data cleaning techniques: handling missing data, outliers, and normalization
  • Feature selection and extraction methods
  • Data transformation techniques for model optimization
Module 4: Supervised Learning Algorithms
  • Linear Regression and Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Model evaluation techniques: Cross-validation, Confusion Matrix, ROC Curve
Module 5: Unsupervised Learning Algorithms
  • Clustering techniques: K-Means, DBSCAN, Hierarchical Clustering
  • Dimensionality Reduction: PCA (Principal Component Analysis)
  • Anomaly Detection techniques
Module 6: Neural Networks and Deep Learning
  • Introduction to Neural Networks and their architecture
  • Backpropagation and optimization techniques
  • Deep Learning concepts: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)
  • TensorFlow and Keras for deep learning model implementation
Module 7: Natural Language Processing (NLP)
  • Text preprocessing and tokenization
  • Sentiment analysis and text classification
  • Word embeddings: Word2Vec, GloVe
  • NLP libraries: NLTK, spaCy
Module 8: Reinforcement Learning
  • Introduction to Reinforcement Learning
  • Markov Decision Process (MDP)
  • Q-Learning and Deep Q-Networks (DQN)
  • Applications of Reinforcement Learning
Module 9: Model Deployment and Evaluation
  • Model evaluation and performance metrics
  • Model tuning and optimization techniques
  • Deploying AI/ML models using cloud platforms like AWS, Google Cloud, or Azure
  • Model monitoring and updating strategies
Module 10: Real-world Applications of AI and ML
  • AI in healthcare, finance, retail, and autonomous systems
  • Case studies and industry-specific use cases
  • Ethical considerations in AI and ML

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The Placement Process at SLA Institute

  • To Foster the employability skills among the students
  • Making the students future-ready
  • Career counseling as and when needed
  • Provide equal chances to all students
  • Providing placement help even after completing the course
On Average Students Rated The Artificial Intelligence and Machine Learning Syllabus Course 4.90/5.0
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