Description
In this course, you will :
- Understand fundamental and advanced Machine Learning and NLP concepts.
- Apply theoretical and practical expertise to real-world applications involving machine learning, NLP, and MLOPS.
- Understand and apply the mathematical ideas underlying ML algorithms.
- Create and optimize ML models with industry-standard tools and methodologies.
- Understand the fundamental ideas of deep learning, including optimizers, loss functions, neural networks, and CNN.
Syllabus:
- Python Programming Language
- Python Control Flow
- Inbuilt Data Structures In Python
- Functions In Python
- Function Practice Question
- Inbuilt Data Structure - Practice Question
- Importing Creating Modules And Packages
- File Handling In Python
- Exception Handling In Python
- OOPS Concepts With Classes And Objects
- Advance Python
- Data Analysis With Python
- Working With Sqlite
- Logging In Python
- Python Multi Threading and Multi Processing
- Memory Management With Python
- Getting Started With Flask Framework
- Getting Started With Streamlit Web Framework
- Getting Started With Statistics
- Introduction To Probability
- Probability Distribution Function For Data
- Inferential Statistics
- Feature Engineering
- Exploratory Data Analysis and Feature Engineering
- Introduction To Machine Learning
- Understanding Complete Linear Regression Indepth Intuition And Practicals
- Ridge,Lasso And ElasticNet ML ALgorithms
- Steps By Step Project Implementation With LifeCycle OF ML Project
- Logistic Regression
- Support Vector Machines
- Naive Bayes Theorem
- K Nearest Neighbour ML Algorithm
- Decision Tree Classifier And Regressor
- Random Forest Machine Learning
- Adaboost Machine Learning Algorithm
- Gradient Boosting
- Xgboost Machine Learning Algorithms
- Unsupervised Machine Learning
- PCA
- K Means Clustering Unsupervised ML
- Hierarichal Clustering
- DBSCAN Clustering
- Silhoutte Clustering
- Anomaly Detection Machine Learning Algorithms
- Dockers For Beginners
- GIT For Beginners
- End To End Machine Learning Project With AWS,Azure Deployment
- End To End MLOPS Projects With ETL Pipelines- Building Network Security System
- MLFlow Dagshub and BentoML-Complete ML Project Lifecycle
- NLP for Machine Learning
- Deep Learning
- End to End Deep Learning Project Using ANN
- NLP With Deep Learning
- Simple RNN Indepth Intuition
- End To End Deep Learning Project With Simple RNN
- LSTM And GRU RNN Indepth Intuition
- LSTM And GRU End To End Deep Learning Project- Predicting Next Word
- Bidirectional RNN Architecture And Indepth Intuition
- Encoder Decoder |Sequence To Sequence Architecture
- Attention Mechanism- Seq2Seq Networks
- Transformers