Published 6/2024
Duration: 6h48m | Video: .MP4, 1920×1080 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 2.85 GB
Genre: eLearning | Language: English
Comprehensive Guide to Building and Deploying Real-World AI Models with Python
What you’ll learn
Understand the fundamentals of AI and ML, including key concepts, historical development, and current applications in various industries.
Apply basic ML algorithms for tasks like classification and regression, using popular libraries such as scikit-learn and TensorFlow.
Develop proficiency in data preprocessing, feature engineering, and model evaluation techniques essential for effective ML implementation.
Gain hands-on experience in building and deploying AI models, addressing real-world problems while considering ethical implications
Requirements
Requirements for this course: Basic computer skills and familiarity with using a web browser. No prior programming experience required, but basic understanding of logic and problem-solving is helpful. High school level math (basic algebra and statistics) is sufficient to start. A computer with internet access capable of running Python (Windows, Mac, or Linux). Enthusiasm to learn about AI and Machine Learning! This course is designed for beginners, so don’t worry if you’re new to the field. We’ll start with the fundamentals and gradually build up to more advanced concepts. If you’re curious about AI and willing to put in the effort, you’re ready to begin this learning journey!
Description
Are you ready to dive into the fascinating world of Artificial Intelligence (AI) and Machine Learning (ML)? This comprehensive course, “Master AI and ML with Python: Foundations to Applications,” is designed to take you from the basics to advanced topics, providing you with the knowledge and skills needed to build and deploy real-world AI and ML models using Python.
What You’ll Learn
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Foundations of AI and ML
: Understand the key concepts, history, and types of AI and ML, including supervised, unsupervised, and reinforcement learning.
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Programming Basics
: Master Python programming, essential libraries (NumPy, Pandas, Matplotlib), and data management techniques.
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Mathematical Foundations
: Grasp the mathematical concepts crucial for AI and ML, such as linear algebra, calculus, and probability.
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Data Preprocessing and Visualization
: Learn data cleaning, transformation, and visualization techniques to prepare your data for modeling.
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Supervised Learning
: Explore algorithms like linear regression, decision trees, support vector machines, and ensemble methods.
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Unsupervised Learning
: Dive into clustering algorithms (K-means, hierarchical, DBSCAN), dimensionality reduction (PCA, t-SNE), and anomaly detection.
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Deep Learning
: Understand neural networks, CNNs, RNNs, LSTM, and implement them using TensorFlow/Keras.
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Natural Language Processing
: Learn text preprocessing, sentiment analysis, named entity recognition, and building chatbots.
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Computer Vision
: Explore image processing, object detection, image segmentation, and generative models (GANs).
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Reinforcement Learning
: Understand the basics, implement Q-learning, and explore applications in robotics and game playing.
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AI in Practice
: Learn MLOps, model deployment, AI applications across industries, and ethical considerations.
Course Structure
The course is structured into 12 comprehensive sections, each designed to build on the previous ones, ensuring a smooth learning curve
1.
Introduction to AI and ML
: Get an overview of AI and ML, understand their importance, and explore real-world applications.
2.
Foundations of Machine Learning
: Learn ML concepts, set up a Python environment, and implement basic algorithms.
3.
Programming Basics for AI
: Master Python, essential libraries, and data manipulation techniques.
4.
Mathematical Foundations
: Dive into linear algebra, calculus, and probability to understand the math behind AI algorithms.
5.
Data Preprocessing and Visualization
: Learn data cleaning, handling missing data, normalization, and visualization techniques.
6.
Supervised Machine Learning
: Implement and evaluate linear regression, logistic regression, decision trees, SVM, and ensemble methods.
7.
Unsupervised Machine Learning
: Explore clustering algorithms, dimensionality reduction, association rule learning, and anomaly detection.
8.
Deep Learning and Neural Networks
: Understand and implement neural networks, CNNs, RNNs, LSTM, and transfer learning.
9.
Natural Language Processing
: Learn text preprocessing, word embeddings, sentiment analysis, and building chatbots.
10.
Computer Vision
: Implement image processing, object detection, image segmentation, and GANs.
11.
Reinforcement Learning
: Understand reinforcement learning, implement Q-learning and SARSA, and explore applications.
12.
AI in Practice and Future Trends
: Learn AI project lifecycle, MLOps, model deployment, emerging trends, and ethical considerations.
Hands-On Projects and Exercises
Throughout the course, you’ll work on numerous practical exercises and hands-on projects to reinforce your learning. Each lesson is accompanied by downloadable materials, including example datasets and Jupyter notebooks with detailed instructions. By the end of this course, you’ll have a portfolio of AI and ML projects that showcase your skills and knowledge.
Who This Course Is For
– Aspiring data scientists and AI/ML engineers
– Software developers and engineers looking to enhance their skills in AI and ML
– Business professionals and analysts seeking to leverage AI for data-driven decision-making
– Anyone interested in understanding and applying AI and ML in real-world scenarios
Requirements
– Basic understanding of programming concepts (knowledge of Python is a plus but not mandatory)
– A computer with internet access to download and install necessary software
Join Us
Embark on this exciting journey to master AI and ML with Python. Enroll now and start building intelligent systems that can transform industries and create innovative solutions to real-world problems. Let’s unlock the power of AI together!
All lessons are subtitled.
Who this course is for
This course is ideal for: Beginners interested in entering the field of AI and Machine Learning, with no prior experience required. IT professionals looking to expand their skill set into AI and ML technologies. Students in computer science, data science, or related fields wanting practical knowledge in AI and ML. Business professionals seeking to understand how AI can be applied in various industries. Data analysts aiming to upgrade their skills with machine learning techniques. Software developers interested in incorporating AI and ML into their applications. Entrepreneurs and innovators looking to leverage AI in their projects or startups. Anyone curious about AI and its real-world applications, regardless of their background. The course is designed to take you from the basics to more advanced concepts, making it suitable for both complete beginners and those with some programming or data analysis experience. If you’re passionate about technology and eager to learn about one of the most transformative fields in computing, this course is for you!
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