AI and Machine Learning using Python

AI enables machines to perform tasks requiring human intelligence. Machine Learning (ML), a subset of AI, allows systems to learn from data. Using Python, popular ML libraries like scikit-learn, TensorFlow, and PyTorch facilitate developing AI applications.

About the program

In our 6-month AI and Machine Learning course with Python, you’ll gain a comprehensive education covering foundational to advanced skills. Begin with Python for data manipulation, visualization, and statistical analysis using NumPy, Pandas, and Matplotlib. Progress to machine learning with supervised/unsupervised learning, regression, classification, and clustering. Hands-on projects simulate real-world scenarios: cleaning data, feature engineering, and building models for actionable insights. Explore deep learning with TensorFlow/PyTorch, and generative AI like GANs/VAEs for synthetic data and content generation. Expert mentors offer personalized feedback, fostering collaborative learning and problem-solving. Build a portfolio showcasing Python proficiency in data science for competitive job markets or career advancement in analytics.

What Will You Learn?

Python Fundamentals

Variables, loops, functions, and essential libraries for data manipulation and machine learning.

Data Handling

Using libraries like NumPy and Pandas to manipulate, clean, and preprocess data for machine learning tasks.

ML Algorithms

Implementing supervised (regression, classification) and unsupervised (clustering) algorithms with Scikit-Learn.

Deep Learning

Neural networks, TensorFlow/PyTorch, image recognition, and NLP tasks.

Model Evaluation

Performance evaluation, cross-validation, hyperparameter tuning, and overfitting mitigation.

Real-World Applications

AI/ML in image classification, sentiment analysis, and recommendation systems.

AI and ML Course Curriculum

  • Python basics: variables, loops, functions
  • Introduction to NumPy for numerical computing
  • Data manipulation with Pandas
  • Data visualization with Matplotlib or Seaborn
  • Supervised vs. unsupervised learning
  • Linear regression and logistic regression
  • Decision trees and ensemble methods (e.g., random forests)
  • Model evaluation: cross-validation and performance metrics
  • Basics of neural networks
  • Introduction to TensorFlow for deep learning
  • Convolutional Neural Networks (CNNs) for image classification
  • Natural Language Processing (NLP) basics: sentiment analysis
  • Real-world applications: recommendation systems, project deployment

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Who Can Apply for the Course?

Roles That AI and ML Engineers Can Fulfill

ML Engineer

Designing and implementing machine learning models and systems.

AI Research Scientist

Conducting research to advance AI algorithms and techniques.

AI Software Engineer

Developing software solutions integrating AI capabilities.

AI Consultant

Advising businesses on AI strategy and implementation.

AI Project Manager

Conducts advanced data research and experiments to develop new methodologies.

AI Ethics Specialist

Ensuring ethical considerations in AI design and deployment.

Skill Covered

Python Programming

Unsupervised Learning

Python Programming

Neural Networks

ML Algorithms

Reinforcement Learning

Data Preprocessing

Time Series Analysis

Tools Covered

Placement Target Companies

Frequently Asked Questions

6 months, depending on the course you choose. Part-time and full-time options are available to fit your schedule.

Most courses offer flexible formats, including online, on-campus, and hybrid options, allowing you to choose the one that best suits your needs.

While some courses require basic knowledge of programming and statistics, others are designed for beginners. Check the prerequisites for each course before applying.

Yes, you will receive a certificate of completion or a degree, depending on the course you choose. These credentials can enhance your resume and career prospects.

Many course providers offer financing options, including installment plans, scholarships, and employer-sponsored programs. Check with the course provider for specific details.

Data science skills are in high demand across various industries. Completing these courses can open up new career opportunities, lead to higher salaries, and provide job security.

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