Complete A.I. & Machine Learning, Data Science Bootcamp

Course Name : Complete A.I. & Machine Learning, Data Science Bootcamp
Students : 130,268
Duration : 43 hrs
Avg Rating : 4.6
Original Price : $119.99
Discount Price : $12.99
Instructor / Provider : Andrei Neagoie, Udemy
Course Type : Self Paced Online Course. Lifetime Access
Coupon : Click on ENROLL NOW to apply discount code

Key Topics Covered

• Data exploration and visualization with Matplotlib and Seaborn
• Neural networks and deep learning with TensorFlow 2.0
• Supervised and unsupervised learning (e.g., regression, classification)
• Model evaluation, hyperparameter tuning, and cross-validation
• Image recognition, ensemble learning, and random forests
• Data engineering basics (Hadoop, Spark, Kafka, Flink)
• Kaggle competitions for hands-on practice
• Environment setup with Conda, Jupyter Notebooks, and Google Colab

Courses to bought together

By End of this Course You’ll: 

• Become a Data Scientist: Gain the knowledge and skills necessary to land a job as a data scientist.
• Master Machine Learning: Learn to implement machine learning algorithms and use them in professional settings.
• Deep Learning & Neural Networks: Understand and apply deep learning concepts, including transfer learning and neural networks, using TensorFlow 2.0.
• Work with Industry Tools: Use cutting-edge tools employed by major tech companies like Google, Apple, Amazon, and Meta.
• Present Data Science Projects: Develop the ability to effectively present insights and projects to management and stakeholders.
• Choose the Right Model: Learn how to select the appropriate machine learning model for various problem types.
• Real-World Applications: Engage with real-life case studies and projects to understand how data science is applied in the industry.
• Best Practices: Adopt industry best practices for data science workflows.
• Python for Data Science: Master Python programming using the latest version (Python 3).
• Model Optimization: Learn techniques to improve the accuracy and performance of machine learning models.
• Data Handling: Preprocess, clean, and analyze large datasets efficiently.
• Build Your Portfolio: Create a professional portfolio with projects and code to showcase your skills.
• Hands-on Experience: Set up developer environments for data science and machine learning tasks.
• Supervised & Unsupervised Learning: Explore both learning paradigms, including applications to time series data.
• Data Visualization: Use Matplotlib and Seaborn to create meaningful visualizations from large datasets.
• Data Wrangling: Harness Pandas for data manipulation and analysis.
• Core Libraries: Dive deep into NumPy and Scikit-learn for implementing machine learning workflows.
• Data Engineering Basics: Understand how tools like Hadoop, Spark, and Kafka are used in big data environments.
• Classification & Regression: Perform tasks such as regression analysis and classification modeling.
• Transfer Learning: Apply pre-trained models to new tasks for efficient problem-solving.

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