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Artificial Intelligence Course

Master Artificial Intelligence and Machine Learning

Build comprehensive artificial intelligence expertise while mastering modern machine learning strategies. This comprehensive course combines AI fundamentals with advanced machine learning concepts, preparing you for real-world AI challenges and industry certifications. Learn to build intelligent systems, design neural networks, and develop AI solutions required for artificial intelligence professional roles.

What You Will Learn:

  • AI Foundations: Understand the fundamentals of artificial intelligence, including machine learning algorithms, neural networks, and deep learning architectures. Learn core AI principles and modern AI frameworks like TensorFlow and PyTorch.
  • Machine Learning and Deep Learning: Master supervised and unsupervised learning, neural network design, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Implement advanced models for computer vision, natural language processing, and predictive analytics.
  • Data Science and Analytics: Gain proficiency in data preprocessing, feature engineering, and statistical analysis methodologies for AI applications. Learn to work with big data, data visualization, and model evaluation techniques.
  • AI Model Development and Deployment: Learn how to implement end-to-end AI pipelines, model optimization, and deployment strategies for production environments. Master MLOps practices and cloud-based AI services integration.
  • AI Tools and Technologies: Explore essential AI development tools such as Python, Jupyter, TensorFlow, PyTorch, and cloud AI platforms for model training and deployment. Get hands-on experience with industry-standard AI development tools.
  • AI Ethics and Performance Optimization: Dive into responsible AI development, bias detection, and model interpretability. Use advanced techniques for optimizing AI model performance and addressing ethical considerations in AI systems.
  • AI Project Portfolio: Apply your skills in real-world scenarios with hands-on AI projects. Develop a comprehensive capstone project that showcases your mastery of artificial intelligence practices.

Why Choose This Course:

  • Expert Instruction: Learn from certified AI professionals and machine learning experts who bring real-world experience and current AI knowledge to the classroom.
  • Hands-On AI Labs: Engage in interactive coding exercises and practical AI project scenarios that reinforce your learning and provide practical artificial intelligence experience.
  • Flexible Learning: Study at your own pace with lifetime access to all course materials, including video lectures, AI labs, and downloadable resources.
  • Comprehensive AI Curriculum: Cover all essential aspects of artificial intelligence, from basic concepts to advanced machine learning techniques, ensuring a well-rounded understanding of AI systems.
  • AI Community: Benefit from a supportive learning environment with access to the course forum, where you can discuss AI topics, share insights, and collaborate with fellow artificial intelligence enthusiasts.
Ideal For:
  • Beginners with no prior AI experience looking to start a career in artificial intelligence or machine learning.
  • IT professionals who want to transition into AI and learn advanced machine learning concepts and practices.
  • Data scientists and developers seeking to enhance their skills in modern AI frameworks, neural networks, or deep learning techniques.
Enroll Today:
  • Take the first step towards mastering artificial intelligence. Enroll now and start your journey to becoming a skilled AI professional!

This comprehensive artificial intelligence course will provide you with the knowledge and hands-on experience needed to build intelligent systems for various industries. Join thousands of students who have successfully launched their AI careers with our expert-led training program.

Curriculum

Overview of AI and its applications30:25🔒
History and evolution of AI25:40🔒
Key concepts and terminology (machine learning, deep learning, neural networks)20:15🔒
Ethical considerations and societal impact of AI20:15🔒

Linear Algebra35:15🔒
Probability and Statistics28:40🔒
Calculus22:30🔒

Introduction to programming languages commonly used in AI (Python)30:25🔒
Key libraries and frameworks (NumPy, pandas, Matplotlib)25:40🔒
Basics of data manipulation and visualization20:15🔒
Introduction to Jupyter Notebooks for experimentation20:15🔒

Linear Regression: Concepts, implementation, and evaluation30:25🔒
Classification Algorithms: Logistic Regression, Decision Trees, k-NN, SVM25:40🔒
Metrics (accuracy, precision, recall, F1-score, ROC-AUC)35:30🔒
Cross-validation and hyperparameter tuning32:15🔒
Dimensionality Reduction: PCA, t-SNE28:45🔒
Clustering: k-Means, Hierarchical Clustering30:32🔒

Introduction to neural networks and deep learning45:30🔒
Feedforward Neural Networks38:20🔒
Convolutional Neural Networks (CNNs)42:15🔒
Introduction to Transfer Learning and Pre-trained Models35:45🔒

Basics of text preprocessing (tokenization, stemming, lemmatization)42:30🔒
Understanding and implementing word embeddings (Word2Vec, GloVe)38:45🔒
Introduction to language models (BERT, GPT)35:20🔒
Applications in text classification, sentiment analysis, and machine translation28:15🔒

Introduction to reinforcement learning concepts (agents, environments, rewards)50:20🔒
Basics of Q-Learning and Policy Gradient methods35:45🔒
Applications of reinforcement learning in game playing and robotics42:30🔒

Introduction to popular AI frameworks (TensorFlow, Keras, PyTorch)50:20🔒
Setting up and using these frameworks for model development35:45🔒
erimentation with cloud-based AI platforms (Google Colab, Azure Machine Learning)42:30🔒

Understanding ethical issues in AI (bias, fairness, transparency)50:20🔒
Implementing ethical guidelines and best practices35:45🔒
Exploring regulations and standards for responsible AI development42:30🔒

What is AI?25:30🔒
AI Applications: AI in everyday life: Examples in healthcare, finance, entertainment, and more30:45🔒

Linear Algebra: Vectors, matrices, and operations35:45🔒
Calculus: Derivatives, gradients, and optimization25:20🔒
Probability and Statistics: Basic probability concepts: Bayes' theorem, conditional probability32:30🔒

Definition of machine learning, types: supervised, unsupervised, and reinforcement learning35:20🔒
The machine learning process: Data collection, preprocessing, model training, and evaluation40:15🔒
Data Preprocessing: Handling missing data, feature scaling, and encoding categorical variables32:45🔒
Model Evaluation: Performance metrics: Accuracy, precision, recall, F1 score, and ROC-AUC32:45🔒

Introduction to Python38:30🔒
Data Handling with Pandas32:15🔒
Visualization with Matplotlib and Seaborn32:15🔒

Support Vector Machines (SVM): Concept, kernel trick, and applications32:15🔒
Clustering: K-Means, Hierarchical clustering, DBSCAN28:40🔒
Concepts: Agents, environments, rewards35:20🔒
Q-Learning, Deep Q-Networks (DQN)41:30🔒

Introduction to Neural Networks45:30🔒
Understanding Deep Learning Frameworks38:25🔒
Working with Convolutional Neural Networks42:15🔒
Advanced CNN architectures: VGG, ResNet42:15🔒

Introduction to NLP35:15🔒
NLP with Machine Learning28:30🔒
Advanced NLP Techniques32:45🔒
Transformers and BERT: Understanding the architecture and applications41:20🔒

Image processing: Filters, edge detection, color spaces39:45🔒
Image segmentation: U-Net, Mask R-CNN42:20🔒
Face recognition, autonomous vehicles, medical imaging48:35🔒
Generative Adversarial Networks (GANs): Concepts and applications in image generation44:10🔒

Recurrent Neural Networks (RNNs)45:25🔒
Attention Mechanisms and Transformers38:15🔒
Deep Reinforcement Learning52:50🔒

Big Data Processing42:30🔒
Scalable Machine Learning55:15🔒
AI and Data Engineering38:40🔒

Understanding and identifying bias in AI models51:20🔒
Concepts of interpretability and explainability43:35🔒
Ethical guidelines, regulatory frameworks for AI39:15🔒
AI in policy-making, ensuring transparency and accountability32:45🔒
Price
From

$99.99

Courses Title
Web Development
Lessons
16 Videos
Language
English
Course Level
Beginner
Reviews
4.7(5.5k)
Quizzes
08
Duration
7 Weeks
Students
2.5k
Certifications
Yes
Pass Percentage
88%
Deadline
01 Jun, 2024
Instructor
Denial Lie
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FAQs

The duration is of 2 months for recorded & 3 months for live.

Yes at the end of the course completion you will get certificates.

ABCPanda team will arrange a doubt clearance session accordingly.

Yes for recorded sessions access duration 1 year. Live session access duration 2 years.

No our mentors will teach from basic. If you have experience, it would add an advantage.