3.50
(2 Ratings)

Machine Learning Specialization

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About Course

Dive into the world of Machine Learning with this free Coursera Specialization from Theetay. Offered by DeepLearning.AI and Stanford Online, this program is led by renowned AI expert Andrew Ng.

Master the fundamentals of machine learning, covering supervised and unsupervised techniques, popular libraries like NumPy and scikit-learn, building predictive models, and exploring neural networks with TensorFlow. Discover advanced topics including decision trees, ensemble methods, clustering, anomaly detection, and recommender systems.

Unlock the power of AI and apply your knowledge to solve real-world problems. This comprehensive program from Coursera is completely free, empowering you to start your journey towards a successful career in AI.

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What Will You Learn?

  • Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
  • Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods
  • Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection
  • Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model

Course Content

01_week_01_introduction-to-machine-learning

  • A Message from the Professor
  • 01_welcome-to-machine-learning.mp4
    02:44
  • 02_applications-of-machine-learning.mp4
    04:28
  • 04_what-is-machine-learning.mp4
    05:35
  • 05_supervised-learning-part-1.mp4
    06:56
  • 06_supervised-learning-part-2.mp4
    07:16
  • 07_unsupervised-learning-part-1.mp4
    08:53
  • 08_unsupervised-learning-part-2.mp4
    03:39
  • 09_jupyter-notebooks.mp4
    04:29
  • 10_linear-regression-model-part-1.mp4
    10:26
  • 11_linear-regression-model-part-2.mp4
    06:43
  • 12_cost-function-formula.mp4
    09:04
  • 13_cost-function-intuition.mp4
    15:46
  • 14_visualizing-the-cost-function.mp4
    08:33
  • 15_visualization-examples.mp4
    06:00
  • 16_gradient-descent.mp4
    08:03
  • 17_implementing-gradient-descent.mp4
    09:59
  • 18_gradient-descent-intuition.mp4
    07:01
  • 19_learning-rate.mp4
    09:03
  • 20_gradient-descent-for-linear-regression.mp4
    06:36
  • 21_running-gradient-descent.mp4
    05:48
  • Course Material Download Link
    00:00

02_week_02_regression-with-multiple-input-variables

03_week_03_classification

04_week_04_neural-networks

05_week_05_neural-network-training

06_week_06_advice-for-applying-machine-learning

07_week_07_decision-trees

08_week_08_unsupervised-learning

09_week_09_recommender-systems

10_week_10_reinforcement-learning

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Student Ratings & Reviews

3.5
Total 2 Ratings
5
1 Rating
4
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3
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2
1 Rating
1
0 Rating
SN
2 months ago
there are no labs and quiz and assignmnts
MH
3 months ago
It was a wonderful course that has laid a very strong foundation, basics and behind the scenes what Machine Learning is. Great Course

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