Python with AI course Training Institute in Erode

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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

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python with AI course training institute in erode

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python with AI course training institute in erode

itcourseerode

python with AI course training institute in erode

itcourseerode

python with AI course training institute in erode

itcourseerode

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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.

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

Beginner, Intermediate, Advance

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

Course Duration

90 Hours

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.

+9043554840

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

Interview

Scheduling Interviews

Resume Prepearation

Job Placement

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.

Networking:

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.

Specialize:

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:

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.