Deep Learning and Machine Leaning with python
Master Deep Learning with Python for Limitless Possibilities
Deep Learning,Machine Learning
Lectures -30
Resources -1
Duration -10 hours
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Course 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!"
Goals
What will you learn in this course:
Our goal is to empower students with comprehensive knowledge and practical skills in deep learning and Python, providing them with a competitive edge in the dynamic world of artificial intelligence. By the end of this course, students will:
Acquire In-Demand Skills: Gain expertise in deep learning and Python, meeting the high demand in the AI-centric job market and opening doors to data science, machine learning, and AI development roles.
Hands-On Proficiency: Develop practical experience through interactive lab sessions, from data preprocessing to model evaluation, making you a proficient deep learning practitioner.
Build a Solid Foundation: Master essential Python libraries, machine learning algorithms, and advanced techniques, establishing a strong foundation for a successful AI career.
Receive Expert Guidance: Benefit from the knowledge and insights of experienced instructors, personalized feedback, and valuable guidance to accelerate your learning journey.
Create a Compelling Portfolio: Strengthen your skills through real-world projects that showcase your deep learning capabilities, enhancing your job prospects.
Understand Real-World Applications: Explore how deep learning powers practical applications like image recognition, natural language processing, recommendation systems, and autonomous vehicles, providing you with a holistic understanding of AI's impact on the world.
This course is designed to benefit both aspiring data scientists looking to start their journey in AI and experienced professionals aiming to stay ahead in the rapidly evolving field. It equips students to harness the full potential of deep learning and Python, shaping their future in artificial intelligence and providing them with limitless opportunities.
Prerequisites
What are the prerequisites for this course?
Basic Programming Knowledge: Familiarity with the fundamentals of programming concepts, such as variables, loops, and functions, is essential.
Understanding of Mathematics and Statistics: A foundational understanding of algebra, calculus, probability, and statistics will be beneficial for comprehending the course material.
Familiarity with Python: Prior experience with Python programming, including a basic understanding of data structures and control flow, is recommended.
Knowledge of Machine Learning Basics: A grasp of fundamental machine learning concepts, such as supervised and unsupervised learning, will aid in the understanding of deep learning principles.
Basic Knowledge of Data Analysis: Familiarity with data analysis techniques and concepts, including data preprocessing and visualization, will be helpful in understanding the practical aspects of the course.
While these prerequisites are recommended, the course is structured to accommodate learners with varying levels of experience and expertise. Students with a strong foundation in these areas will be well-prepared to delve deeper into the intricacies of deep learning and Python.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
- Introduction to Deep learning and Introduction to IDE 04:02 04:02
Python Libraries
5 Lectures
Introduction to Deep Learning
1 Lectures
Supervised vs Unsupervised Learning
1 Lectures
Linear Regression
6 Lectures
Multiple Linear Regression
1 Lectures
Logistic Regression
3 Lectures
Decision Trees
3 Lectures
Clustering
1 Lectures
Anomaly Detection
1 Lectures
Collaborative and Content Based Filtering
1 Lectures
Reinforcement Learning
1 Lectures
Neural Networks
1 Lectures
TensorFlow
1 Lectures
Keras
1 Lectures
Pytorch
1 Lectures
RNN and CNN
1 Lectures
Instructor Details
Selfcode Academy
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