Our Courses Overview
VM Coder is an IT training provider that offers Python and Machine Learning certification training to students aiming to build a strong career in the field of technology. The training programs provided by VM Coder are designed to equip students with the necessary skills and knowledge to succeed in the industry.
Python is a versatile and popular programming language widely used in the field of data science and machine learning. It provides a robust and flexible platform for developing various applications, including web development, data analysis, artificial intelligence, and more. Understanding Python and its applications is crucial for anyone interested in pursuing a career in machine learning.
Machine learning is a subset of artificial intelligence that focuses on creating algorithms and models that enable computers to learn and make predictions or decisions without explicit programming. It involves statistical and mathematical techniques that allow computers to identify patterns and make data-driven predictions or decisions. Machine learning has numerous applications across various industries, such as healthcare, finance, marketing, and more.
VM Coder's training program in Python and Machine Learning is designed to provide students with a comprehensive understanding of these technologies. The program covers the fundamental concepts of Python programming, including syntax, data types, control structures, functions, and modules. Students also learn about object-oriented programming (OOP) concepts, file handling, error handling, and working with libraries and frameworks commonly used in Python development.
The machine learning aspect of the training program introduces students to the key concepts and algorithms used in this field. They learn about supervised and unsupervised learning, regression, classification, clustering, dimensionality reduction, and evaluation metrics. Additionally, students gain hands-on experience by working on real-world projects and implementing machine learning algorithms using Python.
VM Coder provides a practical and project-based approach to learning, allowing students to apply their knowledge in real-world scenarios. The training is delivered by experienced instructors who have expertise in Python and machine learning. They provide guidance and support throughout the learning process, ensuring that students develop a strong foundation and practical skills.
Upon completion of the training program, students receive a certification from VM Coder, validating their skills and knowledge in Python and Machine Learning. This certification can be a valuable addition to their resumes and can help them stand out in the job market.
Overall, VM Coder's Python and Machine Learning certification training provides students with a solid foundation in these technologies, enabling them to pursue rewarding careers in the IT industry.
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CURRICULUM & PROJECTS
- Introduction to Python
- Basic syntax and data types
- Control flow (if-else, loops)
- Functions and modules
- File handling
- Python Data Science Libraries
- NumPy: Numerical computing with arrays
- Pandas: Data manipulation and analysis
- Matplotlib: Data visualization
- Scikit-learn: Machine learning library
- Data Preprocessing
- Data cleaning and handling missing values
- Feature scaling and normalization
- Handling categorical data
- Handling outliers
- Supervised Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines
- Naive Bayes
- Unsupervised Learning Algorithms
- K-means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Association Rule Mining
- Model Evaluation and Selection
- Cross-validation techniques
- Performance metrics (accuracy, precision, recall, F1-score)
- Confusion matrix
- Overfitting and underfitting
- Neural Networks and Deep Learning
- Introduction to neural networks
- Multilayer Perceptron (MLP)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Transfer Learning
- Natural Language Processing (NLP)
- Text preprocessing
- Text classification
- Sentiment analysis
- Language modeling
- Deployment and Model Serving
- Pickle serialization
- Web frameworks (Flask, Django)
- API development
- Containerization (Docker)
- Project Work
- Applying machine learning techniques to real-world problems
- Building and deploying a machine learning model
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