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Python with AI course Training Institute in Erode


385 Google Reviews ( 4.9)

Python is a preferred language for AI due to its simplicity, readability, and extensive libraries. Libraries like TensorFlow and PyTorch facilitate deep learning, while scikit-learn supports classical machine learning. Python's versatility enables seamless integration with various AI tools and frameworks. Its dynamic typing and high-level syntax expedite development, making it an ideal choice for prototyping and production. The ecosystem's richness extends to natural language processing with NLTK and spaCy. Python's strong community ensures continuous support and innovation, fostering AI advancements. In essence, Python serves as a powerful, accessible, and robust language for AI, fueling innovation and progress in the field.

python with AI course training institute in erode

Course Details

Course :                           C programming

Duration:                         4 months

Mode of Training:          Online / offline

Assessments:                  Yes

Certifications:                 5 certifications

Placement support:      100% Assistance

Recently placed Students

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How we conduct classes in Python with AI course training Institute in Erode

In Python, AI training classes typically follow a structured curriculum. Students begin with foundational Python programming, covering syntax, data structures, and algorithms. As they progress, the focus shifts to AI concepts and libraries, such as NumPy, Pandas, and Scikit-Learn. Practical exercises often involve implementing machine learning algorithms, exploring neural networks with TensorFlow or PyTorch, and working on real-world AI applications. Classes may include theoretical discussions on topics like regression, classification, and deep learning, supplemented by hands-on projects to reinforce understanding. Instructors commonly use Jupyter Notebooks for interactive coding sessions, promoting collaborative learning. Assignments and assessments assess students' comprehension and application of AI concepts. Guest lectures and industry case studies enhance practical insights. Overall, Python-based AI training classes combine theoretical knowledge with practical coding exercises to equip students with valuable skills for the rapidly evolving field of artificial intelligence.

Presenting an Award

Python with AI course training Institute in Erode certification & Exam:

Alter Certification is recognised by all significant international businesses. We offer to freshmen as well as corporate trainees once the theoretical and practical sessions are over.

Our Alter accreditation is recognised all around the world. With the aid of this qualification, you may land top jobs in renowned MNCs throughout the world, increasing the value of your CV. Only after successfully completing our training and practice-based projects will the certification be granted.

Key Features of Python with AI course training Institute in Erode

Skill Level

We are providing Training to the needs from Beginners level to Experts level.

Course Duration

Course will be 90 hrs to 110 hrs duration with real-time projects and covers both teaching and practical sessions.

Total Learners

We have already finished 100+ Batches with 100% course completion record.

If you have any questions or concerns, don't hesitate to reach out to our advisor. Feel free to give them a call, and they'll be more than happy to assist you.


Python with AI course training Institute in Erode syllabus

customize based on your audience and objectives:

Module 1: Introduction to Python

  • Introduction to Python

  • Overview of Python

  • Setting up Python environment (IDEs, Jupyter Notebooks)

  • Basic syntax and data types

  • Control Flow and Functions

  • Conditional statements (if, else, elif)

  • Loops (for, while)

  • Functions and modules

  • Data Structures in Python

  • Lists, tuples, sets, dictionaries

  • List comprehensions

Module 2: Introduction to AI and Machine Learning

  • Introduction to Artificial Intelligence

  • Definition and history

  • AI vs. Machine Learning vs. Deep Learning

  • Introduction to Machine Learning

  • Supervised, unsupervised, and reinforcement learning

  • Types of machine learning problems (classification, regression, clustering)

  • Overview of Libraries

  • NumPy, Pandas for data manipulation

  • Matplotlib, Seaborn for data visualization

Module 3: Data Preprocessing and Feature Engineering

  • Data Cleaning and Preprocessing

  • Handling missing data

  • Data normalization and scaling

  • Encoding categorical variables

  • Feature Engineering

  • Creating new features

  • Feature scaling and selection

Module 4: Machine Learning Algorithms

  • Supervised Learning Algorithms

  • Linear Regression

  • Decision Trees and Random Forest

  • Support Vector Machines

  • k-Nearest Neighbors

  • Neural Networks (basic overview)

  • Unsupervised Learning Algorithms

  • K-Means Clustering

  • Hierarchical Clustering

  • Principal Component Analysis (PCA)

Module 5: Introduction to Deep Learning

  • Neural Networks

  • Introduction to deep learning

  • Basics of neural networks (layers, activations, loss functions)

  • Deep Learning Frameworks

  • TensorFlow and Keras

  • PyTorch

Module 6: Building and Training Neural Networks

  • Building Neural Networks with Keras

  • Sequential and Functional API

  • Model compilation and training

  • Convolutional Neural Networks (CNNs)

  • Image classification

  • Transfer learning

  • Recurrent Neural Networks (RNNs)

  • Sequence prediction

  • Natural Language Processing (NLP)

Module 7: Model Evaluation and Hyperparameter Tuning

  • Model Evaluation Metrics

  • Accuracy, precision, recall, F1-score

  • Confusion matrix

  • Hyperparameter Tuning

  • Grid search and random search

  • Cross-validation

Module 8: Real-world Applications and Projects

  • AI in Practice

  • Image recognition

  • Natural language processing

  • Recommender systems

  • Capstone Project

  • Apply learned concepts to a real-world problem

  • Present and discuss results

Module 9: Ethics and Responsible AI

  • Ethical Considerations in AI

  • Bias in AI

  • Fairness and transparency

  • Privacy concerns

  • Responsible AI Practices

  • Guidelines for ethical AI development

  • Societal impact of AI

Module 10: Future Trends in AI

  • Emerging Technologies

  • Reinforcement learning

  • Generative Adversarial Networks (GANs)

  • Edge computing in AI

Corporate Training in Python with AI course training Institute in Erode :

Eligibility Criteria

Apitude Test

Placement & Training

Mock Interviews


Scheduling Interviews

Resume Prepearation

Job Placement

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Proficiency After Certification in Python with AI course training Institute in Erode :

After completing a Python with AI, one can expect to have the following skills and proficiency:

Hands-On Projects:

  • Apply your knowledge by working on real-world projects. This will deepen your understanding and provide practical experience.

  • Consider contributing to open-source projects or participating in coding challenges to expand your portfolio.

Build a Portfolio:

  • Create a portfolio showcasing the projects you've worked on during and after your certification. This can be a valuable asset when seeking employment or freelance opportunities.

Continuous Learning:

  • Stay updated with the latest developments in Python and AI. Attend webinars, workshops, or online courses to keep your skills current.

  • Follow industry blogs, forums, and social media to stay informed about trends and advancements.


  • Connect with professionals in the field through social media platforms like LinkedIn, attend meetups, and join online communities. Networking can provide valuable insights and opportunities.


  • Consider specializing in a specific area of AI that aligns with your interests or career goals. This could include natural language processing, computer vision, reinforcement learning, etc.

Certifications and Advanced Courses:

  • Explore advanced certifications or courses that build upon your existing knowledge. This can help you delve deeper into specific topics and demonstrate your expertise.Problem-Solving:

  • Engage in coding challenges, participate in hackathons, and solve algorithmic problems. This will sharpen your problem-solving skills and help you think critically.


  • Collaborate with other professionals or enthusiasts on projects. Working with others can expose you to different perspectives and ways of approaching problems.

Soft Skills:

  • Develop soft skills such as communication, teamwork, and time management. These skills are crucial in a professional setting and can contribute to your overall success.

Job Search and Internships:

  • Actively search for job opportunities or internships related to AI and Python development. Practical experience in a professional environment can be invaluable.

Working from Home
Staff Profile
  • Certified professional trainer.

  • More than 5+ years experience.

  • Trained students by giving real time examples.

  • Strong knowledge of theory and practical

  • Trainers are industry experience.

  • Trainers have Real time project experience in their industry.

  • Students can ask their doubts to the trainer.

  • Trainer prepares students on relevant subjects for the interview.

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