In this course, you will learn how to extract insights and knowledge from data using various data science and machine learning techniques. We will cover the essential topics that form the foundation of data science and machine learning, and equip you with the necessary tools and techniques to apply them in real-world scenarios.
Lesson 1: Introduction to Data Science and Machine Learning
- What is data science?
- What is machine learning?
- The relationship between data science and machine learning
- The importance of data science and machine learning in today's world
Lesson 2: Data Preparation and Cleaning
- Data collection and acquisition
- Data cleaning and transformation
- Exploratory data analysis
- Data visualization
Lesson 3: Supervised Learning
- What is supervised learning?
- Types of supervised learning algorithms: regression, classification
- Linear regression
- Logistic regression
Lesson 4: Unsupervised Learning
- What is unsupervised learning?
- Types of unsupervised learning algorithms: clustering, dimensionality reduction
- K-means clustering
- Principal Component Analysis (PCA)
Lesson 5: Deep Learning
- What is deep learning?
- Artificial Neural Networks (ANNs)
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
Lesson 6: Natural Language Processing (NLP)
- What is NLP?
- Text preprocessing and cleaning
- Feature extraction
- Sentiment analysis
Lesson 7: Reinforcement Learning
- What is reinforcement learning?
- Markov Decision Processes (MDPs)
- Q-Learning
- Deep Q-Learning
Lesson 8: Deployment and Productionization
- Challenges in deploying machine learning models
- Tools for model deployment and productionization
- Model monitoring and maintenance
- Best practices for machine learning productionization
After completing the Data Science & Machine Learning course. you will gain a solid foundation in data science and machine learning concepts and tools, and are ready to apply them to real-world scenarios. Remember, data science and machine learning are rapidly evolving fields, and there is always more to learn. Keep practicing, stay curious, and never stop learning!
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