Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS

Course Name : Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS
Students : 5K+
Duration : 94 Hours
Avg Rating : 4.7
Original Price : $119.99
Discount Price : 90%OFF
Instructor / Provider : Udemy
Course Type : Self Paced Online Course. Lifetime Access
Coupon : Click on ENROLL NOW to apply discount code

What You’ll Learn 

• Core Concepts: Gain a foundational understanding of key data science and machine learning principles.
• Python Essentials: Master Python for data science, including popular libraries like NumPy, Pandas, and Matplotlib.
• Data Visualization: Create advanced 2D/3D plots and interactive visualizations using Seaborn, Plotly, and Folium.
• Probability & Statistics: Learn statistical foundations essential for machine learning, including inference and estimations.
• Feature Engineering: Improve model performance with techniques like feature selection, transformation, and dimensionality reduction.
• Deep Learning Models: Build and train Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs).
• Hands-On Projects: Work on real-world datasets and build your own AI applications.

Frequently Bought Together

Course Overview

Python for Data Science and Data Analysis

• Learn the basics of Python, leading to advanced indexing and data visualization.
• No prior programming knowledge required.
• Master essential libraries such as NumPy, Pandas, and Matplotlib.
• Build a strong foundation for transitioning to other programming languages.

Data Understanding and Visualization

• Dive into advanced data manipulation and visualization techniques.
• Use libraries like Seaborn, Plotly, and Folium to create dynamic visualizations and interactive maps.
• Work with complex datasets and enhance your proficiency with Python-based tools.

Probability and Statistics in Python

• Explore theoretical foundations critical to data science, including conditional probability and statistical inference.
• Apply key concepts to practical scenarios with Python coding examples.

Machine Learning Fundamentals

• Gain a comprehensive understanding of machine learning principles.
• Build pipelines and apply ML techniques using Scikit-Learn (Sklearn).
• Prepare for advanced ML concepts and real-world applications.

Feature Engineering and Dimensionality Reduction

• Enhance model performance through data preparation and transformation techniques.
• Handle missing data and optimize features to improve accuracy and efficiency.
• Apply real-world case studies to understand the impact of dimensionality reduction.

Deep Learning Models

• Artificial Neural Networks (ANNs)
  • Learn to use TensorFlow for designing and training ANN models.
  • Apply ANNs to solve real-world problems across diverse domains.
• Convolutional Neural Networks (CNNs)
  • Gain expertise in CNNs for computer vision, audio processing, and more.
  • Work on practical applications like facial recognition and neural style transfer.
• Recurrent Neural Networks (RNNs)
  • Build RNNs for sequential data analysis, such as time series and natural language processing tasks.

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