Machine Learning, Data Science and Deep Learning with Python

Coupon Details

Machine Learning, Data Science and Deep Learning with Python, Hands-on with the hottest machine learning, Tensorflow, Keras, artificial intelligence, and neural network techniques.

Created by Sundog Education by Frank Kane, Frank Kane

Sure! Here are some topics that you can cover in your blog article about Machine Learning, Data Science and Deep Learning with Python:

• Introduction to Machine Learning, Data Science and Deep Learning
• Overview of Python programming language
• Python libraries for Machine Learning and Data Science
• Overview of Deep Learning
• Deep Learning frameworks in Python
• Applications of Machine Learning and Data Science in real-world scenarios

You can find more information about these topics on the following websites:

• Kaggle
• Data Science with chniques … - Springering - Towards Dev
• Deep Learning: A ComprePython — Deep Learnhensive Overview on Te

I hope this helps! Let me know if you have any other questions.

What Will I Learn?

• Build artificial neural networks with Tensorflow and Keras
• Make predictions using linear regression, polynomial regression, and multivariate regression
• Build Deep Learning networks to classify images with Convolutional Neural Networks
• Implement machine learning, clustering, and search using TF/IDF at massive scale with Apache Spark's MLLib
• Implement Sentiment Analysis with Recurrent Neural Networks
• Understand reinforcement learning - and how to build a Pac-Man bot
• Classify medical test results with a wide variety of supervised machine learning classification techniques
• Cluster data using K-Means clustering and Support Vector Machines (SVM)
• Build a spam classifier using Naive Bayes
• Use decision trees to predict hiring decisions
• Apply dimensionality reduction with Principal Component Analysis (PCA) to classify flowers
• Predict classifications using K-Nearest-Neighbor (KNN)
• Develop using iPython notebooks
• Understand statistical measures such as standard deviation
• Visualize data distributions, probability mass functions, and probability density functions
• Visualize data with matplotlib
• Use covariance and correlation metrics
• Apply conditional probability for finding correlated features
• Use Bayes' Theorem to identify false positives
• Understand complex multi-level models
• Use train/test and K-Fold cross validation to choose the right model
• Build a movie recommender system using item-based and user-based collaborative filtering
• Clean your input data to remove outliers
• Design and evaluate A/B tests using T-Tests and P-Values

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