Complete Data Science BootCamp 2024
- Description
- Curriculum
- FAQ
- Reviews
Data science is the field that encompasses the various techniques and methods used to extract insights and knowledge from data. Machine learning (ML) and deep learning (DL) are both subsets of data science, and they are often used together to analyze and understand data.
In data science, ML algorithms are often used to build predictive models that can make predictions based on historical data. These models can be used for tasks such as classification, regression, and clustering. ML algorithms include linear regression, decision trees, and k-means.
DL, on the other hand, is a subset of ML that is based on artificial neural networks with multiple layers, which allows the system to learn and improve through experience. DL is particularly well-suited for tasks such as image recognition, speech recognition, and natural language processing. DL algorithms include convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
In a data science project, DL models are often used in combination with other techniques such as feature engineering, data cleaning, and visualization, to extract insights and knowledge from data. For instance, DL models can be used to automatically extract features from images, and then these features can be used in a traditional ML model.
In summary, Data science is the field that encompasses various techniques and methods to extract insights and knowledge from data, ML and DL are subsets of data science that are used to analyze and understand data, ML is used to build predictive models and DL is used to model complex patterns and relationships in data. Both ML and DL are often used together in data science projects to extract insights and knowledge from data.
IN THIS COURSE YOU WILL LEARN ABOUT :
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Life Cycle of a Data Science Project.
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Python libraries like Pandas and Numpy used extensively in Data Science.
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Matplotlib and Seaborn for Data Visualization.
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Data Preprocessing steps like Feature Encoding, Feature Scaling etc…
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Machine Learning Fundamentals and different algorithms
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Cloud Computing for Machine Learning
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Deep Learning
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5 projects like Diabetes Prediction, Stock Price Prediction etc…
ALL THE BEST !!!
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20
Introduction to Machine Learning -
21
Supervised Machine Learning
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22
Unsupervised Machine Learning
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23
Machine Learning Life Cycle
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24
Train Test Split
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25
Regression Analysis
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26
Linear Regression
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27
Logistic Regression
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28
KNN
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29
SVM
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30
Decision Tree
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31
Random Forest
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32
K Means Clustering
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33
Hyper Parameter Optimization with GridSearchCV
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34
Machine Learning Pipeline
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35
Machine Learning Model Evaluation Metrics
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36
Cloud Computing Introduction
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37
Introduction to AWS
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38
Different AWS Services
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39
Introduction to AWS SageMaker
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40
First Machine Learning Practical on AWS SageMaker
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41
Built in ML Algorithms in AWS SageMaker
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42
Linear Learner Algorithm Practical Implementation
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43
No Code ML using AWS SageMaker Canvas
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44
AWS SageMaker MarketPlace
