LEARN ML WITH PYTHON
Mastering Machine Learning with Python
LEARN ML WITH PYTHON
“Learn ML with Python” is an educational initiative or resource designed to introduce individuals to the concepts and practices of machine learning (ML) using Python, one of the most popular programming languages in the data science and ML community. This program typically covers fundamental ML concepts, algorithms, and techniques, guiding learners through practical applications, hands-on coding exercises, and real-world projects. By leveraging Python libraries such as NumPy, pandas, scikit-learn, and TensorFlow, participants gain essential skills in data manipulation, model building, and evaluation, making them proficient in implementing machine learning solutions. Overall, it serves as a comprehensive pathway for beginners and intermediate learners to develop their expertise in ML through a structured and user-friendly approach.
To Download Our Brochure: https://www.justacademy.co/download-brochure-for-free
Message us for more information: +91 9987184296
1 - Introduction to Machine Learning: Understand the basics of machine learning, its definition, and its significance in various industries.
2) Python Programming Basics: Get an introduction to Python language fundamentals, including data types, control structures, and functions.
3) Setting Up the Environment: Learn how to install and configure Python, Jupyter notebooks, and necessary libraries such as NumPy and Pandas.
4) Data Manipulation with Pandas: Explore how to use Pandas for data manipulation, cleaning, and preparation for machine learning projects.
5) NumPy for Numerical Data: Gain insights into NumPy for efficient numerical computations and handling large datasets.
6) Data Visualization Techniques: Discover various data visualization libraries like Matplotlib and Seaborn to represent data insights graphically.
7) Understanding Algorithms: Learn about different machine learning algorithms, including supervised and unsupervised learning techniques.
8) Feature Engineering: Understand the importance of feature selection, extraction, and engineering in improving model performance.
9) Model Training and Testing: Get practical experience in splitting data into training and testing sets and evaluating model performance.
10) Working with Scikit learn: Dive into the Scikit learn library to implement various ML algorithms effectively.
11) Hyperparameter Tuning: Learn techniques to optimize model performance through hyperparameter tuning and grid search.
12) Performance Metrics: Explore various metrics for evaluating model performance, including accuracy, precision, recall, and F1 score.
13) Building Predictive Models: Work on real life projects to build predictive models using historical datasets.
14) Deep Learning Introduction: Get an overview of deep learning concepts and frameworks such as TensorFlow and Keras.
15) Deployment of ML Models: Learn how to deploy machine learning models into production environments and the basics of monitoring and maintaining models.
This structured training program aims to equip students with a thorough understanding of machine learning concepts using Python, enabling them to apply their knowledge to real world problems.
Browse our course links : https://www.justacademy.co/all-courses
To Join our FREE DEMO Session: Click Here
Contact Us for more info:
- Message us on Whatsapp: +91 9987184296
- Email id: info@justacademy.co
