Machine Learning with Python Training
Machine Learning with Python Training aims to instruct participants in Python programming to create machine learning models and use data science methods. Important subjects covered in the course include feature engineering, model evaluation, supervised and unsupervised learning, and the use of well-known libraries like Scikit-learn, Pandas, and TensorFlow. Participants learn how to handle big datasets to create prediction models for practical uses.
- 10+ Courses
- 30+ Projects
- 400 Hours
Machine Learning with Python Training is suitable for the following target audiences:
Data Scientists and Analysts: Professionals wishing to deepen their understanding of machine learning and use sophisticated predictive modeling in their jobs are best suited as data scientists and analysts.
Software Developers: Ideal for developers who wish to enhance their knowledge of Python’s data science skills and incorporate machine learning methods into their applications.
IT Professionals and Engineers: Designed for people who wish to improve organizational decision-making, automate procedures, and solve complicated challenges using machine learning approaches.
Students and Academics: Ideal for individuals who require practical expertise with Python machine learning and are pursuing jobs in data science, artificial intelligence, or similar sectors.
Data Scientist: Data scientists use data analysis and machine learning models to address business problems and create data-driven decisions.
Machine Learning Engineer: Create, develop, and implement machine learning models and algorithms in real-world settings across various sectors, including technology, healthcare, and finance.
AI Engineer: Develop applications for artificial intelligence by using machine learning models for tasks including computer vision, natural language processing, and autonomous systems.
Data Analyst: To evaluate huge datasets, derive insights, and aid in business decision-making, apply machine learning techniques.
Software Developer (ML-focused): Improve functionality and performance by incorporating machine learning techniques into software products and applications.
The increasing need for machine learning specialists in sectors including healthcare, banking, e-commerce, and technology in both the USA and Canada provides excellent employment possibilities with competitive pay and room for growth.
- A look at the ideas behind machine learning
- Getting Started with Python for Machine Learning
- Using Anaconda and Jupyter Notebook to set up the development environment
- Basic Python for analyzing data
- How the machine learning process works
- Using Pandas to change data
- Using NumPy for numerical computing
- Methods for cleaning and preparing data
- Dealing with missing values and outliers
- Exploratory data analysis (EDA)
- The basics of data visualisation
- Using Matplotlib to make charts
- Seaborn for advanced visualisation
- Seeing trends and patterns in data sets
- Ways to tell stories with data
- An introduction to learning with supervision
- Linear regression and logistic regression
- Using Scikit-learn to make classification algorithms
- Random forests and decision trees
- Training and testing the model
- K-Means and other clustering methods
- Ways to reduce dimensionality
- Principal Component Analysis (PCA)
- Finding patterns in datasets
- Uses of unsupervised learning
- Ways to check the model
- Ways to cross-validate
- Tuning hyperparameters
- Metrics for measuring how well classification and regression work
- Making the model more accurate
- The basics of neural networks
- TensorFlow and Keras are examples of deep learning frameworks.
- Making basic neural network models
- Teaching and testing deep learning models
- Uses of deep learning
- A complete machine learning project in Python
- Preparing the data, building the model, and testing it
- Case studies from the real world
- Making a resume and getting ready for an interview
- Final project presentation and help with getting certified
This course teaches you how to use Python to make machine learning models.
Those who are involved in IT, data analysis, development, or machine learning are the ideal candidates for this course.
This course is suitable for both beginners and people who already know some Python.
Pandas, NumPy, and Scikit-learn are some of the libraries.
Yes, students work on real-world machine learning projects.
I will learn how to analyze the data, make predictions, use machine learning algorithms, and test models.
AI Engineer, Data Scientist, Machine Learning Engineer, and Data Analyst.
Yes, Python is one of the most popular languages for building machine learning systems.
The program is set up as an eight-week training course.
Yes, the course uses frameworks like TensorFlow to teach deep learning.
We currently offer online sessions with flexible weekday/weekend batches for 8 weeks. All sessions are recorded. You’ll have access to the recordings, along with support from instructors and peers in our learning portal.
You can register via our website https://checkmateittech.com/, or reach out to our support teams via phone, email, or WhatsApp. We’ll help you with batch schedules and payment options.
Email info@checkmateittech. Call Us at +1-347-408-2054.
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Job opportunities in USA and Canada
Data Scientist: Data scientists use data analysis and machine learning models to address business problems and create data-driven decisions.
Machine Learning Engineer: Create, develop, and implement machine learning models and algorithms in real-world settings across various sectors, including technology, healthcare, and finance.
AI Engineer: Develop applications for artificial intelligence by using machine learning models for tasks including computer vision, natural language processing, and autonomous systems.
Data Analyst: To evaluate huge datasets, derive insights, and aid in business decision-making, apply machine learning techniques.
Software Developer (ML-focused): Improve functionality and performance by incorporating machine learning techniques into software products and applications.
The increasing need for machine learning specialists in sectors including healthcare, banking, e-commerce, and technology in both the USA and Canada provides excellent employment possibilities with competitive pay and room for growth.
