Artificial Intelligence Machine Learning Engineer

Step into the future of technology with Artificial Intelligence (AI) & Machine Learning (ML)! 🚀 Here’s what you’ll master:
Python for AI & ML – Learn the most popular programming language for AI development.
Mathematics & Statistics – Understand the core concepts behind machine learning models.
Supervised & Unsupervised Learning – Build models for classification, regression, and clustering.
Neural Networks & Deep Learning – Explore advanced AI with TensorFlow & PyTorch.
Natural Language Processing (NLP) – Teach machines to understand human language.
Computer Vision – Develop AI models that "see" and analyze images.
Model Deployment & Optimization – Deploy ML models into real-world applications.
Whether you're a beginner or advancing your career, AI & ML open endless possibilities! 💡 Start learning today! 🚀

Course Content

Introduction to AI and Ml
a. Definition and history of AI and ML b. Applications and impact of AI and NL in various industries

Machine Learning Fundamentals
a. Supervised, unsupervised and reinforcement learning b. Data pre-processing and feature engineering c. Evaluation metrics for ML models

Regression
a. Linear regression b. Polynomial regression c. Regularization techniques(L1,L2)

Classification
a. Logistic Regression b. Decision Tree c. Random Forests d. Support Vector machines

Clustering
a. K- Means clustering b. Hierarchical clustering c. Density based clustering

Dimensionality Reduction
a. Principal Component Analysis (PCA) b. T-SNE

Real Life Project 1

Neural Network and Deep Learning
a. Introduction to neural networks b. Convolutional Neural Networks (CNNs) c. Recurrent Neural Networks(RNNs) d. Transfer Learning

Natural Language Processing(NLP)
a. Text Pre-processing b. Word Embedding (Word2Vec,Glove) c. Recurrent Neural Networks for text classification and generation

Reinforcement Learning
a. Markov Decision Processes (MDPs) b. Q-learning c. Deep Q-learning

Model Evaluation and Deployment
a. Cross-Validation b. Hyper parameter tuning c. Model deployment and serving

Real Life Project 2

Revision

A course by

RKM Viveka Tirtha

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Price:
₹18,290.00
Instructor:RKM Viveka Tirtha
Duration: 50 hours
Lessons:0
Students:0
Level:Expert

What Will You Learn?

Course Content

Introduction to AI and Ml
a. Definition and history of AI and ML b. Applications and impact of AI and NL in various industries

Machine Learning Fundamentals
a. Supervised, unsupervised and reinforcement learning b. Data pre-processing and feature engineering c. Evaluation metrics for ML models

Regression
a. Linear regression b. Polynomial regression c. Regularization techniques(L1,L2)

Classification
a. Logistic Regression b. Decision Tree c. Random Forests d. Support Vector machines

Clustering
a. K- Means clustering b. Hierarchical clustering c. Density based clustering

Dimensionality Reduction
a. Principal Component Analysis (PCA) b. T-SNE

Real Life Project 1

Neural Network and Deep Learning
a. Introduction to neural networks b. Convolutional Neural Networks (CNNs) c. Recurrent Neural Networks(RNNs) d. Transfer Learning

Natural Language Processing(NLP)
a. Text Pre-processing b. Word Embedding (Word2Vec,Glove) c. Recurrent Neural Networks for text classification and generation

Reinforcement Learning
a. Markov Decision Processes (MDPs) b. Q-learning c. Deep Q-learning

Model Evaluation and Deployment
a. Cross-Validation b. Hyper parameter tuning c. Model deployment and serving

Real Life Project 2

Revision

A course by

Course Includes:

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Artificial Intelligence Machine Learning Engineer

What Will You Learn?

Course Content

Introduction to AI and Ml a. Definition and history of AI and ML b. Applications and impact of AI and NL in various industries

Machine Learning Fundamentals a. Supervised, unsupervised and reinforcement learning b. Data pre-processing and feature engineering c. Evaluation metrics for ML models

Regression a. Linear regression b. Polynomial regression c. Regularization techniques(L1,L2)

Classification a. Logistic Regression b. Decision Tree c. Random Forests d. Support Vector machines

Clustering a. K- Means clustering b. Hierarchical clustering c. Density based clustering

Dimensionality Reduction a. Principal Component Analysis (PCA) b. T-SNE

Real Life Project 1

Neural Network and Deep Learning a. Introduction to neural networks b. Convolutional Neural Networks (CNNs) c. Recurrent Neural Networks(RNNs) d. Transfer Learning

Natural Language Processing(NLP) a. Text Pre-processing b. Word Embedding (Word2Vec,Glove) c. Recurrent Neural Networks for text classification and generation

Reinforcement Learning a. Markov Decision Processes (MDPs) b. Q-learning c. Deep Q-learning

Model Evaluation and Deployment a. Cross-Validation b. Hyper parameter tuning c. Model deployment and serving

Real Life Project 2

Revision

A course by

Student Ratings & Reviews

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Links

Follow Us

Introduction to AI and Ml
a. Definition and history of AI and ML b. Applications and impact of AI and NL in various industries

Machine Learning Fundamentals
a. Supervised, unsupervised and reinforcement learning b. Data pre-processing and feature engineering c. Evaluation metrics for ML models

Regression
a. Linear regression b. Polynomial regression c. Regularization techniques(L1,L2)

Classification
a. Logistic Regression b. Decision Tree c. Random Forests d. Support Vector machines

Clustering
a. K- Means clustering b. Hierarchical clustering c. Density based clustering

Dimensionality Reduction
a. Principal Component Analysis (PCA) b. T-SNE

Neural Network and Deep Learning
a. Introduction to neural networks b. Convolutional Neural Networks (CNNs) c. Recurrent Neural Networks(RNNs) d. Transfer Learning

Natural Language Processing(NLP)
a. Text Pre-processing b. Word Embedding (Word2Vec,Glove) c. Recurrent Neural Networks for text classification and generation

Reinforcement Learning
a. Markov Decision Processes (MDPs) b. Q-learning c. Deep Q-learning

Model Evaluation and Deployment
a. Cross-Validation b. Hyper parameter tuning c. Model deployment and serving