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Keep shoppingDeep learning and Machine Learning with Python
140 students
Deep learning and Machine Learning with Python
Master Artificial Intelligence and Deep Learning with Python
Created bySelfcode Academy
Last updated 6/2024
English
What you'll learn
- Data visualization libraries such as Pandas, Matplotlib, Seaborn, and NumPy.
- Key concepts of machine learning, including supervised and unsupervised learning, and understand the differences between them.
- Implementation of linear regression models.
- Understanding the concept of cost functions.
- Employing gradient descent for optimization.
- Decision tree algorithms, including XGBoost and Random Forests.
- Understand how ensemble methods work and their applications in predictive modeling, enabling them to construct more accurate and robust models.
- They will also be able to extend their skills to logistic regression, including cost functions and gradient descent specific to classification problems.
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Course content
17 sections • 30 lectures • 10h 12m total length
Requirements
- A basic python knowledge required
Description
Master Deep Learning with Python for AI Excellence
Course Description:
This meticulously crafted course is designed to empower you with comprehensive knowledge and practical skills to thrive in the world of artificial intelligence.
Immerse yourself in engaging lectures and hands-on lab sessions that cover fundamental concepts, cutting-edge methodologies, and real-world applications of deep learning. Gain expertise in essential Python libraries, machine learning algorithms, and advanced techniques, setting a solid foundation for your AI career.
Course Highlights:
In-Demand Skills: Acquire the highly sought-after skills demanded by today's AI-centric job market, opening doors to data science, machine learning, and AI development roles.
Hands-On Learning: Learn by doing! Our interactive lab sessions ensure you gain practical experience, from data preprocessing to model evaluation, making you a proficient deep learning practitioner.
Comprehensive Curriculum: From foundational Python libraries like Pandas and NumPy to cutting-edge neural network architectures like CNNs and RNNs, this course covers it all. Explore linear regression, logistic regression, decision trees, clustering, anomaly detection, and more.
Expert Guidance: Our experienced instructors are committed to your success. Receive expert guidance, personalized feedback, and valuable insights to accelerate your learning journey.
Project-Based Learning: Strengthen your skills with real-world projects that showcase your deep learning capabilities, building a compelling portfolio.
Practical Applications: Understand how deep learning powers real-world applications, including image recognition, natural language processing, recommendation systems, and autonomous vehicles.
Who Should Enroll:
Aspiring Data Scientists: Start your journey into data science and AI with the skills and knowledge needed to excel.
Machine Learning Enthusiasts: Deepen your understanding of machine learning and take it to the next level with deep learning applications.
AI Developers: Enhance your proficiency in deep learning to stay ahead in this rapidly evolving field.
Whether you're new to AI or an experienced professional, this course empowers you to harness the full potential of deep learning and Python, opening doors to limitless opportunities. Don't miss this chance to shape your future in artificial intelligence.
Course Curriculum
Section 1: Introduction
Understand the significance of deep learning and its implications.
Get familiar with essential Integrated Development Environments (IDEs).
Section 2: Python Libraries
Master data manipulation with Pandas.
Explore numerical operations with NumPy.
Dive into scientific analysis using Scipy.
Create visually appealing graphics with Matplotlib.
Craft elegant visualizations with Seaborn.
Section 3: Introduction to Deep Learning
Uncover the fundamental principles of deep learning.
Grasp the pivotal role of neural networks.
Section 4: Supervised vs. Unsupervised Learning
Demystify supervised and unsupervised learning.
Section 5: Linear Regression
Master linear regression for prediction.
Section 6: Multiple Linear Regression
Predict multiple outcomes using advanced techniques.
Section 7: Logistic Regression
Equip computers with decision-making capabilities.
Section 8: Decision Trees
Explore decision trees and essential companions like Xgboost and Random Forest.
Section 9: Clustering
Organize data through clustering.
Section 10: Anomaly Detection
Identify anomalies in data.
Section 11: Collaborative and Content-Based Filtering
Deliver personalized recommendations.
Section 12: Reinforcement Learning
Immerse in dynamic reinforcement learning.
Section 13: Neural Networks
Delve into the core of AI with neural networks.
Section 14: TensorFlow
Master the acclaimed deep learning library.
Section 15: Keras
Build and train deep learning models with ease.
Section 16: PyTorch
Explore the dynamic and versatile deep-learning library.
Section 17: RNN and CNN
Unlock specialized architectures for sequential data and image processing.
Upon course completion, you'll possess a profound understanding of deep learning, ready to tackle diverse AI and machine learning challenges using Python's robust toolkit.
This course equips you to confidently step into the realm of AI mastery. Experience the magic of AI and command your computer to achieve remarkable feats!
Enroll now and unlock the magic of Deep Learning and Python!"
Who this course is for:
- Beginner Python Developers enthusiastic about Learning Deep Learning and Data Science
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Deep Learning and want to apply it easily on datasets.
- Any data analysts who want to level up in Deep Learning.
- Anyone interested in Deep Learning.