Machine Studying Specialization

WHAT YOU WILL LEARN

• Construct ML fashions with NumPy & scikit-learn, construct & practice supervised fashions for prediction & binary classification duties (linear, logistic regression)

• Construct & practice a neural community with TensorFlow to carry out multi-class classification, & construct & use choice bushes & tree ensemble strategies

• Apply greatest practices for ML growth & use unsupervised studying strategies for unsupervised studying together with clustering & anomaly detection

• Construct recommender programs with a collaborative filtering strategy & a content-based deep studying methodology & construct a deep reinforcement studying mannequin

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SKILLS YOU WILL GAIN

• Choice Bushes
• Synthetic Neural Community
• Logistic Regression
• Recommender Programs
• Linear Regression
• Regularization to Keep away from Overfitting
• Gradient Descent
• Supervised Studying
• Logistic Regression for Classification
• Xgboost
• Tensorflow
• Tree Ensembles

About this Specialization

The Machine Studying Specialization is a foundational on-line program created in collaboration between DeepLearning.AI and Stanford On-line. This beginner-friendly program will educate you the basics of machine studying and the way to use these strategies to construct real-world AI functions.

This Specialization is taught by Andrew Ng, an AI visionary who has led essential analysis at Stanford College and groundbreaking work at Google Mind, Baidu, and Touchdown.AI to advance the AI subject.

This 3-course Specialization is an up to date model of Andrew’s pioneering Machine Studying course, rated 4.9 out of 5 and brought by over 4.8 million learners because it launched in 2012.

It offers a broad introduction to trendy machine studying, together with supervised studying (a number of linear regression, logistic regression, neural networks, and choice bushes), unsupervised studying (clustering, dimensionality discount, recommender programs), and among the greatest practices utilized in Silicon Valley for synthetic intelligence and machine studying innovation (evaluating and tuning fashions, taking a data-centric strategy to enhancing efficiency, and extra.)

By the top of this Specialization, you’ll have mastered key ideas and gained the sensible know-how to rapidly and powerfully apply machine studying to difficult real-world issues. In case you’re trying to break into AI or construct a profession in machine studying, the brand new Machine Studying Specialization is one of the best place to begin.

Utilized Studying Challenge

By the top of this Specialization, you’ll be able to:

 

• Construct machine studying fashions in Python utilizing widespread machine studying libraries NumPy and scikit-learn.

• Construct and practice supervised machine studying fashions for prediction and binary classification duties, together with linear regression and logistic regression.

• Construct and practice a neural community with TensorFlow to carry out multi-class classification.

• Apply greatest practices for machine studying growth in order that your fashions generalize to information and duties in the actual world.

• Construct and use choice bushes and tree ensemble strategies, together with random forests and boosted bushes.

• Use unsupervised studying strategies for unsupervised studying: together with clustering and anomaly detection.

• Construct recommender programs with a collaborative filtering strategy and a content-based deep studying methodology.

• Construct a deep reinforcement studying mannequin.