What you’ll learn-12 hours
- You will learn the key factors in Machine and Deep Learning. Overview of Machine Learning. Introduction to Machine Learning.
- Learn Definition and Importance of the Machine Learning which includes Types of Machine Learning, Basics of Python for Machine Learning Include Data types
- Learn Control Flow and Functions, NumPy and Pandas for Data Manipulation.
- Introduction to Data Preprocessing and Visualization. Which include : Data Cleaning and Preprocessing , Handling Missing Values and Feature Scaling
- Learn After that Data Visualization base on Matplotlib and Seaborn for Visualization and also Exploratory Data Analysis (EDA).
- You will be able to learn about Supervised Learning including Regression in Linear Regression and Polynomial Regression.
- Details about Regression is a type of supervised learning including Ridge Regression, Lasso Regression: Elastic Net Regression, Support Vector Regression (SVR)
- Model Evaluation and Hyperparameter Tuning include Cross-Validation, Grid Search.
- Unsupervised Learning, including K means clustering, Hierarchical Clustering Part of this Module
- Learn about Introduction to Deep Learning including Neural Networks Basics, Role of Perceptions and Activation Functions, Feedforward Neural Networks.
- Introduction to TensorFlow and Keras include : Basics of TensorFlow, Building Neural Networks with Keras.
- Deep Learning Techniques include Convolutional Neural Networks (CNNs) base on Architecture of CNNs and Image Classification with CNNs
- Recurrent Neural Networks (RNNs) base on Architecture of RNNs and Sequence Generation with RNNs
- Transfer Learning and Fine-Tuning base on Pretrained Models and : Fine-Tuning Models
- Advanced Deep Learning, Generative Adversarial Networks (GANs) , Understanding GANs Image Generation with GANs
- Reinforcement Learning, include Basics of Reinforcement Learning and Q-Learning and Deep Q-Networks (DQN).
- Learn about Deployment and Model Management, Model Deployment, Flask for Web APIs, Dock erization, Model Management and Monitoring
- Bias and Fairness in ML Models, Understanding Bias, Mitigating Bias ,privacy and security in Ml include Data Privacy, Model Security
How to Enroll Certification in Machine Learning and Deep Learning course?
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