Are you ready to elevate your Python programming skills to new heights and delve into the exciting world of machine learning? Infoclub Ltd is proud to present the “Advanced Python Programming with Machine Learning” course, a comprehensive and hands-on learning experience that will equip you with the knowledge and tools to become a proficient Python programmer and machine learning practitioner.
Mastering Python Fundamentals: This course starts by reinforcing your foundational Python skills and then takes you on a journey through advanced programming techniques. You’ll become a Python pro, proficient in object-oriented programming, data structures, and handling exceptions.
Data Handling and Manipulation: Learn to work with data effectively, using libraries like NumPy, Pandas, and Matplotlib. Discover how to preprocess and visualize data, essential skills for any data scientist or machine learning engineer.
Machine Learning Fundamentals: Delve into the core concepts of machine learning, including supervised and unsupervised learning, classification, regression, clustering, and more. Understand the theory behind machine learning algorithms and their practical applications.
Hands-on Coding Projects: Apply your newfound knowledge in real-world projects. You’ll work on a variety of machine learning tasks, such as image recognition, natural language processing, and predictive modeling, which will sharpen your coding skills and build your portfolio.
Advanced Libraries and Frameworks: Explore powerful machine learning libraries such as Scikit-Learn and TensorFlow. Gain the expertise needed to implement machine learning models and neural networks efficiently.
Model Evaluation and Optimization: Understand how to assess the performance of your machine learning models and fine-tune them for optimal results. Dive into cross-validation, hyperparameter tuning, and feature selection techniques.
Practical Applications: Discover how machine learning is used in real-world applications, from healthcare and finance to recommendation systems and autonomous vehicles. Explore the latest trends and developments in the field.
Industry-Ready Skills: Infoclub Ltd’s instructors, who are experienced professionals in the field, will guide you through industry best practices, enabling you to be well-prepared for a career in data science, machine learning, or software development.
Introduction to AI and Machine Learning
- Understanding AI, machine learning, and their applications
- Overview of supervised, unsupervised, and reinforcement learning
- Case Studies in machine Learning
- Ethical Issues in Machine Learning
Foundations of Machine Learning with Python
- Stages in a Machine Learning project
- Data preprocessing and cleaning
- Exploratory data analysis (EDA) with Python libraries
- Feature engineering and selection
- Model evaluation metrics and techniques
Supervised Learning Algorithms
- Linear regression and regularization techniques
- Python programs to do linear regression
- Practical exercises using Python libraries
- Model evaluation and accuracy.
- Decision trees and ensemble methods (Random Forest, Gradient Boosting)
- Support Vector Machines (SVM)
- Python programs to do linear classification
- Confusion matrix
- Model evaluation and accuracy. Deployment
Unsupervised Learning and Clustering
- K-Means clustering
- Hierarchical clustering
- Dimensionality reduction techniques (PCA, t-SNE)
- Applying clustering to real-world datasets
Neural Networks and Deep Learning
- Introduction to neural networks
- Building and training feedforward neural networks using TensorFlow/Keras
- Convolutional Neural Networks (CNN) for image recognition
- Recurrent Neural Networks (RNN) for sequence data
Advanced Topics and Project Presentations
- Reinforcement learning concepts
- Hyperparameter tuning and model optimization
- Ethical considerations in AI and machine learning
(Each participant will do at least 1 project below or alternate & present)
- Image Classification with Convolutional Neural Networks (CNN): Develop an image classification model using CNNs to classify images into different categories. Use dataset CIFAR-10 or a custom dataset related to a specific domain.
- Sentiment Analysis and Chatbot Integration: Build a sentiment analysis model using Azure Text Analytics to classify user feedback from social media or a website as positive, negative, or neutral. Then, integrate this model with a chatbot using Azure Bot Service to respond contextually.
- Predictive Analytics with Azure Machine Learning: Create a predictive model using Azure Machine Learning Studio to forecast trends or outcomes in a specific domain, such as stock prices or customer behavior. Deploy the model and build a web app to showcase predictions in real-time.
COURSE ORGANISATION & TIMING
We have created a schedule for this course to match with most availabilities.
BENEFITS OF THIS COURSE
There are many benefits in becoming a Pro Python Programmer
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