Machine Learning Specialization

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.

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

01_multiple-features.mp4

09:51
02_vectorization-part-1.mp4

06:54
03_vectorization-part-2.mp4

06:52
04_gradient-descent-for-multiple-linear-regression.mp4

07:45
05_feature-scaling-part-1.mp4

06:34
06_feature-scaling-part-2.mp4

07:34
07_checking-gradient-descent-for-convergence.mp4

05:39
08_choosing-the-learning-rate.mp4

06:06
09_feature-engineering.mp4

03:04
10_polynomial-regression.mp4

05:51

03_week_03_classification

01_motivations.mp4

09:47
02_logistic-regression.mp4

09:48
03_decision-boundary.mp4

10:42
04_cost-function-for-logistic-regression.mp4

11:59
05_simplified-cost-function-for-logistic-regression.mp4

05:44
06_gradient-descent-implementation.mp4

06:31
07_the-problem-of-overfitting.mp4

11:52
08_addressing-overfitting.mp4

08:15
09_cost-function-with-regularization.mp4

09:03
10_regularized-linear-regression.mp4

08:52
11_regularized-logistic-regression.mp4

05:32

04_week_04_neural-networks

01_welcome.mp4

02:53
02_neurons-and-the-brain.mp4

10:51
03_demand-prediction.mp4

16:22
04_example-recognizing-images.mp4

06:35
05_neural-network-layer.mp4

09:49
06_more-complex-neural-networks.mp4

08:09
07_inference-making-predictions-forward-propagation.mp4

05:23
08_inference-in-code.mp4

06:39
09_data-in-tensorflow.mp4

11:18
10_building-a-neural-network.mp4

08:21
11_forward-prop-in-a-single-layer.mp4

05:06
12_general-implementation-of-forward-propagation.mp4

07:51
13_is-there-a-path-to-agi.mp4

10:34
14_how-neural-networks-are-implemented-efficiently.mp4

04:21
15_matrix-multiplication.mp4

09:28
16_matrix-multiplication-rules.mp4

09:30
17_matrix-multiplication-code.mp4

06:25

06_week_06_advice-for-applying-machine-learning

01_deciding-what-to-try-next.mp4

03:41
02_evaluating-a-model.mp4

10:25
03_model-selection-and-training-cross-validation-test-sets.mp4

14:52
04_diagnosing-bias-and-variance.mp4

11:12
05_regularization-and-bias-variance.mp4

10:36
06_establishing-a-baseline-level-of-performance.mp4

09:25
07_learning-curves.mp4

12:13
08_deciding-what-to-try-next-revisited.mp4

08:46
09_bias-variance-and-neural-networks.mp4

10:44
10_iterative-loop-of-ml-development.mp4

07:42
11_error-analysis.mp4

08:20
12_adding-data.mp4

14:23
13_transfer-learning-using-data-from-a-different-task.mp4

12:10
14_full-cycle-of-a-machine-learning-project.mp4

08:44
15_fairness-bias-and-ethics.mp4

09:55
16_error-metrics-for-skewed-datasets.mp4

11:35
17_trading-off-precision-and-recall.mp4

11:49
17_trading-off-precision-and-recall.mp4

11:49

07_week_07_decision-trees

01_decision-tree-model.mp4

07:01
02_learning-process.mp4

11:20
03_measuring-purity.mp4

07:37
04_choosing-a-split-information-gain.mp4

11:51
05_putting-it-together.mp4

09:07
06_using-one-hot-encoding-of-categorical-features.mp4

05:25
07_continuous-valued-features.mp4

06:53
08_regression-trees-optional.mp4

09:50
09_using-multiple-decision-trees.mp4

03:55
10_sampling-with-replacement.mp4

03:59
11_random-forest-algorithm.mp4

06:22
12_xgboost.mp4

06:51
13_when-to-use-decision-trees.mp4

06:19

08_week_08_unsupervised-learning

01_welcome.mp4

03:21
02_what-is-clustering.mp4

04:11
03_k-means-intuition.mp4

06:49
04_k-means-algorithm.mp4

09:49
05_optimization-objective.mp4

11:12
06_initializing-k-means.mp4

08:53
07_choosing-the-number-of-clusters.mp4

07:57
08_finding-unusual-events.mp4

11:53
09_gaussian-normal-distribution.mp4

10:50
10_anomaly-detection-algorithm.mp4

12:07
11_developing-and-evaluating-an-anomaly-detection-system.mp4

11:38
12_anomaly-detection-vs-supervised-learning.mp4

08:08
13_choosing-what-features-to-use.mp4

14:57

09_week_09_recommender-systems

01_making-recommendations.mp4

05:32
02_using-per-item-features.mp4

11:21
03_collaborative-filtering-algorithm.mp4

13:55
04_binary-labels-favs-likes-and-clicks.mp4

08:27
05_mean-normalization.mp4

08:45
06_tensorflow-implementation-of-collaborative-filtering.mp4

11:37
07_finding-related-items.mp4

06:32
08_collaborative-filtering-vs-content-based-filtering.mp4

09:45
09_deep-learning-for-content-based-filtering.mp4

09:42
10_recommending-from-a-large-catalogue.mp4

07:52
11_ethical-use-of-recommender-systems.mp4

10:48
12_tensorflow-implementation-of-content-based-filtering.mp4

04:48
13-Reducing the number of features (optional).mp4

12:23
14-PCA algorithm (optional).mp4

17:50
15-PCA in code (optional).mp4

11:03

10_week_10_reinforcement-learning

01_what-is-reinforcement-learning.mp4

08:48
02_mars-rover-example.mp4

06:41
03_the-return-in-reinforcement-learning.mp4

10:18
04_making-decisions-policies-in-reinforcement-learning.mp4

02:37
05_review-of-key-concepts.mp4

05:34
06_state-action-value-function-definition.mp4

10:36
07_state-action-value-function-example.mp4

05:22
08_bellman-equations.mp4

12:52
09_random-stochastic-environment-optional.mp4

08:24
10_example-of-continuous-state-space-applications.mp4

06:24
11_lunar-lander.mp4

05:53
12_learning-the-state-value-function.mp4

16:50
13_algorithm-refinement-improved-neural-network-architecture.mp4

03:00
14_algorithm-refinement-greedy-policy.mp4

08:59
15_algorithm-refinement-mini-batch-and-soft-updates-optional.mp4

11:43
16_the-state-of-reinforcement-learning.mp4

02:54
17_summary-and-thank-you.mp4

03:11

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Student Ratings & Reviews

3.5

Total 2 Ratings

1 Rating

0 Rating

1 Rating

0 Rating

Sami Neem

4 months ago

there are no labs and quiz and assignmnts

M Hammad Sarwar

4 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

About Course

What Will You Learn?

Course Content

01_week_01_introduction-to-machine-learning

A Message from the Professor

01_welcome-to-machine-learning.mp4

02_applications-of-machine-learning.mp4

04_what-is-machine-learning.mp4

05_supervised-learning-part-1.mp4

06_supervised-learning-part-2.mp4

07_unsupervised-learning-part-1.mp4

08_unsupervised-learning-part-2.mp4

09_jupyter-notebooks.mp4

10_linear-regression-model-part-1.mp4

11_linear-regression-model-part-2.mp4

12_cost-function-formula.mp4

13_cost-function-intuition.mp4

14_visualizing-the-cost-function.mp4

15_visualization-examples.mp4

16_gradient-descent.mp4

17_implementing-gradient-descent.mp4

18_gradient-descent-intuition.mp4

19_learning-rate.mp4

20_gradient-descent-for-linear-regression.mp4

21_running-gradient-descent.mp4

Course Material Download Link

02_week_02_regression-with-multiple-input-variables

01_multiple-features.mp4

02_vectorization-part-1.mp4

03_vectorization-part-2.mp4

04_gradient-descent-for-multiple-linear-regression.mp4

05_feature-scaling-part-1.mp4

06_feature-scaling-part-2.mp4

07_checking-gradient-descent-for-convergence.mp4

08_choosing-the-learning-rate.mp4

09_feature-engineering.mp4

10_polynomial-regression.mp4

03_week_03_classification

01_motivations.mp4

02_logistic-regression.mp4

03_decision-boundary.mp4

04_cost-function-for-logistic-regression.mp4

05_simplified-cost-function-for-logistic-regression.mp4

06_gradient-descent-implementation.mp4

07_the-problem-of-overfitting.mp4

08_addressing-overfitting.mp4

09_cost-function-with-regularization.mp4

10_regularized-linear-regression.mp4

11_regularized-logistic-regression.mp4

04_week_04_neural-networks

01_welcome.mp4

02_neurons-and-the-brain.mp4

03_demand-prediction.mp4

04_example-recognizing-images.mp4

05_neural-network-layer.mp4

06_more-complex-neural-networks.mp4

07_inference-making-predictions-forward-propagation.mp4

08_inference-in-code.mp4

09_data-in-tensorflow.mp4

10_building-a-neural-network.mp4

11_forward-prop-in-a-single-layer.mp4

12_general-implementation-of-forward-propagation.mp4

13_is-there-a-path-to-agi.mp4

14_how-neural-networks-are-implemented-efficiently.mp4

15_matrix-multiplication.mp4

16_matrix-multiplication-rules.mp4

17_matrix-multiplication-code.mp4

05_week_05_neural-network-training

01_tensorflow-implementation.mp4

02_training-details.mp4

03_alternatives-to-the-sigmoid-activation.mp4

04_choosing-activation-functions.mp4

05_why-do-we-need-activation-functions.mp4

06_multiclass.mp4

07_softmax.mp4

08_neural-network-with-softmax-output.mp4

09_improved-implementation-of-softmax.mp4

10_classification-with-multiple-outputs-optional.mp4

11_advanced-optimization.mp4

12_additional-layer-types.mp4