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Data Science AI & ML Training

👉 Earn 5LPA to 60LPA Salary

Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of technological innovation, transforming various industries by enabling systems to learn from data and make intelligent decisions. This AI & ML Training course provides a comprehensive understanding of AI and ML concepts, algorithms, and applications, preparing professionals to harness the power of these technologies effectively. Starting packages from 6-10 LPA.
4.7 (250 user ratings)
★★★★★ 4.7/5
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AI & ML Training Syllabus

The AI & ML Training course is designed to provide participants with a solid foundation in artificial intelligence and machine learning. As these technologies continue to transform industries and drive innovation, this course equips learners with the skills needed to develop intelligent systems and apply machine learning algorithms to solve real-world problems. Through a combination of theory, practical exercises, and projects, students will gain hands-on experience in building and deploying AI and ML models.

Course content

  1. Overview of AI: History, Definitions, and Applications
  2. AI vs. Machine Learning vs. Deep Learning
  3. Types of AI: Narrow AI, General AI, and Superintelligent AI
  4. Ethics and Societal Impact of AI
  5. Real-world Applications of AI
  1. Introduction to Python: Basics, Syntax, and Environment Setup
  2. Data Structures in Python: Lists, Tuples, Dictionaries, and Sets
  3. Control Flow in Python: Conditional Statements and Loops
  4. Functions and Modules in Python    
  5. Working with Libraries: NumPy and Pandas
  1. Data Manipulation with Pandas
  2. Data Visualization with Matplotlib and Seaborn
  3. Introduction to Scikit-Learn: Basic Functions and Datasets
  4. Hands-on Session: Implementing Simple Machine Learning Models in Python
  5. Review and Q&A
  1. What is Machine Learning?
  2. Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
  3. Key Concepts in Machine Learning: Data, Features, Labels, Training, and Testing
  4. Overview of Machine Learning Algorithms
  5. Linear Regression: Concepts and Applications
  1. Polynomial Regression
  2. Evaluation Metrics for Regression: MAE, MSE, R-Squared
  3. Hands-on Session: Implementing Linear and Polynomial Regression in Python
  4. Introduction to Classification
  5. Logistic Regression
  1. Support Vector Machines (SVM)
  2. Decision Trees
  3. Random Forests
  4. Evaluation Metrics for Classification: Accuracy, Precision, Recall, F1-Score, Confusion Matrix
  5. Hands-on Session: Implementing Classification Algorithms in Python
  1. Introduction to Clustering
  2. K-Means Clustering
  3. Hierarchical Clustering
  4. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
  5. Hands-on Session: Implementing Clustering Algorithms in Python
  1. Principal Component Analysis (PCA) t-Distributed Stochastic Neighbor Embedding (t-SNE)
  2. Model Evaluation Techniques: Cross-Validation, ROC Curve
  3. Hands-on Session: Implementing PCA and t-SNE in Python
  4. Hands-on Session: Model Evaluation in Python
  1. Introduction to Deep Learning
  2. Artificial Neural Networks (ANN): Structure and Function
  3. Activation Functions, Loss Functions, and Optimizers
  4. Hands-on Session: Building a Simple Neural Network with TensorFlow/Keras
  5. Introduction to Convolutional Neural Networks (CNNs)
  1. Convolutional Layers: Concept and Function
  2. Pooling Layers: Types and Applications
  3. Fully Connected Layers: Concept and Use Cases
  4. Hands-on Session: Implementing a CNN in TensorFlow/Keras
  5. Introduction to Recurrent Neural Networks (RNNs)
  1. Long Short-Term Memory (LSTM) Networks
  2. Applications of RNNs and LSTMs: Time Series Prediction, Natural Language Processing
  3. Hands-on Session: Implementing an RNN/LSTM in TensorFlow/Keras
  4. Introduction to Generative Adversarial Networks (GANs)
  5. Architecture of GAN: Generator and Discriminator
  1. Applications of GANs: Image Generation, Data Augmentation
  2. Hands-on Session: Implementing a Simple GAN in TensorFlow/Keras
  3. Natural Language Processing (NLP): Introduction and Key Concepts
  4. Hands-on Session: Implementing NLP tasks in Python
  5. AI Applications in Various Industries: Healthcare, Finance, Autonomous Vehicles
  1. Current Trends in AI
  2. Future Directions in AI
  3. Course Review and Recap
  4. Final Project: Guidelines and Expectations
  5. Final Project Work Session
  6. Final Project Presentation and Discussion
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Success Students Feedback
Praveen K

Senior AI Engineer

Few Details of Course
  • Time duration : 2 Months
  • Industry-Relevant Skills
  • Expert Instruction
  • Comprehensive Curriculum
  • Career Advancement
  • Online Demo time : 9PM Monday & Friday
  • Classroom Demo time:8 PM Monday to Friday
  • Weekend Demo time 11AM Classroom
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Real-Time Projects

Our SDLC training program includes real-time projects that simulate actual industry scenarios.

Unlimited Access

We offer unlimited access to our course materials, including video tutorials, interactive exercises, and comprehensive documentation.

24/7 Learning Assistance

Our dedicated support team is available around the clock to assist you with any questions or issues you might encounter.

Track Growth

Our training program includes tools and assessments to track your growth and performance throughout the course.

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Testimonials

These reviews highlight various aspects of your training program, emphasizing the practical application, support, and value that students experience.

AI & ML Students at SDLC Training , I had an outstanding experience with this SDLC training program. The real-time projects provided practical insights and hands-on experience that were invaluable. The unlimited access to course materials allowed me to learn at my own pace, and the 24/7 learning assistance was always helpful. Highly recommended for anyone looking to advance their skills in software development."
Excellent Learning Experience
Excellent Learning ExperienceManjunath
"This training program is comprehensive and well-structured. The modules covered all aspects of the SDLC, and the real-time projects made the learning process engaging. The support team was available whenever I needed help, and the ability to track my progress kept me on track. I feel much more confident in my software development skills now."
Comprehensive and Supportive
Comprehensive and SupportiveRamesh
"The value I received from this SDLC training program exceeded my expectations. The content was thorough, the projects were relevant, and having unlimited access to the resources was a major plus. The 24/7 learning assistance ensured that any questions I had were answered promptly. I believe this training is an excellent investment for anyone serious about a career in software development."
Great Value for Money
Great Value for MoneySeenu
"What sets this training apart is its focus on practical application. The real-time projects provided a realistic view of the challenges faced in the industry, and the interactive elements of the course made learning enjoyable. The constant support from the team was reassuring, and the growth tracking tools helped me stay motivated and on top of my learning goals."
Highly Interactive and Practical
Highly Interactive and PracticalKeerthi
"This SDLC training program was professional and efficient. The course material was well-organized, and the real-time projects added significant value. The unlimited access and 24/7 support were incredibly useful, and I appreciated the detailed feedback on my progress. I would recommend this training to anyone looking to enhance their understanding of the software development life cycle."
Professional and Efficient
Professional and EfficientKarthik
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