Introduction to Machine Learning

About Course

Dive into the world of machine learning with this comprehensive course from Duke University. Learn the fundamentals of machine learning models like logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, and more. Discover how these models can solve real-world problems in fields such as medical diagnostics, image recognition, and text prediction.

This course provides hands-on experience implementing machine learning algorithms with PyTorch, a widely-used open-source library trusted by tech giants like Google, NVIDIA, CocaCola, eBay, Snapchat, Uber, and many others.

**This course is completely FREE and available on Theetay. It’s curated from top platforms like Udemy, Udacity, Coursera, MasterClass, NearPeer, and more.**

**Key topics covered:**

Machine Learning Fundamentals
Logistic Regression
Multilayer Perceptrons
Convolutional Neural Networks
Natural Language Processing
PyTorch Implementation
Real-World Applications

**Start your machine learning journey today!**

Course Content

Introduction to Machine Learning

A Message from the Professor
001 01_course-information_instructions.html

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005 02_why-machine-learning-is-exciting.mp4

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009 03_what-is-machine-learning.mp4

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010 04_intro-to-machine-learning_quiz.html

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014 05_logistic-regression.mp4

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018 06_interpretation-of-logistic-regression.mp4

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019 07_math-for-data-science_instructions.html

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023 08_motivation-for-multilayer-perceptron.mp4

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024 09_logistic-regression_quiz.html

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028 01_multilayer-perceptron-concepts.mp4

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032 02_multilayer-perceptron-math-model.mp4

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033 03_multilayer-perceptron_quiz.html

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037 04_deep-learning.mp4

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041 05_example-document-analysis.mp4

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045 06_interpretation-of-multilayer-perceptron.mp4

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049 07_transfer-learning.mp4

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050 08_deep-learning_quiz.html

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054 09_model-selection.mp4

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055 10_model-selection_quiz.html

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059 11_early-history-of-neural-networks.mp4

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060 12_history-of-neural-networks_quiz.html

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064 01_hierarchical-structure-of-images.mp4

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068 02_convolution-filters.mp4

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072 03_convolutional-neural-network.mp4

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073 04_cnn-concepts_quiz.html

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077 05_cnn-math-model.mp4

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081 06_how-the-model-learns.mp4

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085 07_advantages-of-hierarchical-features.mp4

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086 08_cnn-math-model_quiz.html

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090 01_cnn-on-real-images.mp4

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094 02_applications-in-use-and-practice.mp4

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098 03_deep-learning-and-transfer-learning.mp4

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099 04_applications-in-use-and-practice_quiz.html

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100 05_week-1-comprehensive_exam.html

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103 01_introduction-to-pytorch.mp4

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111 02_how-do-we-evaluate-our-networks.mp4

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112 03_lesson-one_quiz.html

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116 01_how-do-we-learn-our-network.mp4

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120 02_how-do-we-handle-big-data.mp4

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124 03_early-stopping.mp4

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125 04_lesson-2_quiz.html

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126 01_week-2-comprehensive_exam.html

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129 02_model-learning-with-pytorch.mp4

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133 01_motivation-diabetic-retinopathy.mp4

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137 02_breakdown-of-the-convolution-1d-and-2d.mp4

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138 03_lesson-one_quiz.html

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142 01_core-components-of-the-convolutional-layer.mp4

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146 02_activation-functions.mp4

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150 03_pooling-and-fully-connected-layers.mp4

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151 04_lesson-2_quiz.html

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155 01_training-the-network.mp4

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159 02_transfer-learning-and-fine-tuning.mp4

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160 03_lesson-3_quiz.html

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161 01_week-3-comprehensive_exam.html

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164 02_cnn-with-pytorch.mp4

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168 01_introduction-to-the-concept-of-word-vectors.mp4

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172 02_words-to-vectors.mp4

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175 03_example-of-word-embeddings.mp4

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176 04_lesson-1_quiz.html

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180 01_neural-model-of-text.mp4

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183 02_the-softmax-function.mp4

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186 03_methods-for-learning-model-parameters.mp4

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189 04_more-details-on-how-to-learn-model-parameters.mp4

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190 05_lesson-2_quiz.html

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193 01_the-recurrent-neural-network.mp4

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196 02_long-short-term-memory.mp4

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199 03_long-short-term-memory-review.mp4

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202 04_use-of-lstm-for-text-synthesis.mp4

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203 05_lesson-3_quiz.html

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206 01_simple-and-effective-alternative-methods-for-neural-nlp.mp4

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207 02_week-4-comprehensive_exam.html

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210 01_natural-language-processing-with-pytorch.mp4

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216 02_relationships-between-word-vectors.mp4

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219 03_inner-products-between-word-vectors.mp4

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222 04_intuition-into-meaning-of-inner-products-of-word-vectors.mp4

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225 01_introduction-of-attention-mechanism.mp4

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228 02_queries-keys-and-values-of-attention-network.mp4

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231 03_self-attention-and-positional-encodings.mp4

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234 01_attention-based-sequence-encoder.mp4

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237 02_coupling-the-sequence-encoder-and-decoder.mp4

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240 03_cross-attention-in-the-sequence-to-sequence-model.mp4

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243 01_multi-head-attention.mp4

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246 02_the-complete-transformer-network.mp4

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249 01_introduction-to-reinforcement-learning.mp4

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252 02_reinforcement-learning-problem-setup.mp4

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255 03_example-of-reinforcement-learning-in-practice.mp4

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256 04_reinforcement-learning-quiz_quiz.html

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259 05_reinforcement-learning-with-pytorch.mp4

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262 01_moving-to-a-non-myopic-policy.mp4

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265 02_q-learning.mp4

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268 03_extensions-of-q-learning.mp4

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269 04_q-learning-quiz_quiz.html

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272 01_limitations-of-q-learning-and-introduction-to-deep-q-learning.mp4

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275 02_deep-q-learning-based-on-images.mp4

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278 03_connecting-deep-q-learning-with-conventional-q-learning.mp4

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279 04_deep-q-learning-quiz_quiz.html

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Section Quiz

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About Course

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Course Content

Introduction to Machine Learning

A Message from the Professor

001 01_course-information_instructions.html

005 02_why-machine-learning-is-exciting.mp4

009 03_what-is-machine-learning.mp4

010 04_intro-to-machine-learning_quiz.html

014 05_logistic-regression.mp4

018 06_interpretation-of-logistic-regression.mp4

019 07_math-for-data-science_instructions.html

023 08_motivation-for-multilayer-perceptron.mp4

024 09_logistic-regression_quiz.html

028 01_multilayer-perceptron-concepts.mp4

032 02_multilayer-perceptron-math-model.mp4

033 03_multilayer-perceptron_quiz.html

037 04_deep-learning.mp4

041 05_example-document-analysis.mp4

045 06_interpretation-of-multilayer-perceptron.mp4

049 07_transfer-learning.mp4

050 08_deep-learning_quiz.html

054 09_model-selection.mp4

055 10_model-selection_quiz.html

059 11_early-history-of-neural-networks.mp4

060 12_history-of-neural-networks_quiz.html

064 01_hierarchical-structure-of-images.mp4

068 02_convolution-filters.mp4

072 03_convolutional-neural-network.mp4

073 04_cnn-concepts_quiz.html

077 05_cnn-math-model.mp4

081 06_how-the-model-learns.mp4

085 07_advantages-of-hierarchical-features.mp4

086 08_cnn-math-model_quiz.html

090 01_cnn-on-real-images.mp4

094 02_applications-in-use-and-practice.mp4

098 03_deep-learning-and-transfer-learning.mp4

099 04_applications-in-use-and-practice_quiz.html

100 05_week-1-comprehensive_exam.html

103 01_introduction-to-pytorch.mp4

111 02_how-do-we-evaluate-our-networks.mp4

112 03_lesson-one_quiz.html

116 01_how-do-we-learn-our-network.mp4

120 02_how-do-we-handle-big-data.mp4

124 03_early-stopping.mp4

125 04_lesson-2_quiz.html

126 01_week-2-comprehensive_exam.html

129 02_model-learning-with-pytorch.mp4

133 01_motivation-diabetic-retinopathy.mp4

137 02_breakdown-of-the-convolution-1d-and-2d.mp4

138 03_lesson-one_quiz.html

142 01_core-components-of-the-convolutional-layer.mp4

146 02_activation-functions.mp4

150 03_pooling-and-fully-connected-layers.mp4

151 04_lesson-2_quiz.html

155 01_training-the-network.mp4

159 02_transfer-learning-and-fine-tuning.mp4

160 03_lesson-3_quiz.html

161 01_week-3-comprehensive_exam.html

164 02_cnn-with-pytorch.mp4

168 01_introduction-to-the-concept-of-word-vectors.mp4

172 02_words-to-vectors.mp4

175 03_example-of-word-embeddings.mp4

176 04_lesson-1_quiz.html

180 01_neural-model-of-text.mp4

183 02_the-softmax-function.mp4

186 03_methods-for-learning-model-parameters.mp4

189 04_more-details-on-how-to-learn-model-parameters.mp4

190 05_lesson-2_quiz.html

193 01_the-recurrent-neural-network.mp4

196 02_long-short-term-memory.mp4

199 03_long-short-term-memory-review.mp4

202 04_use-of-lstm-for-text-synthesis.mp4

203 05_lesson-3_quiz.html

206 01_simple-and-effective-alternative-methods-for-neural-nlp.mp4

207 02_week-4-comprehensive_exam.html

210 01_natural-language-processing-with-pytorch.mp4

216 02_relationships-between-word-vectors.mp4

219 03_inner-products-between-word-vectors.mp4

222 04_intuition-into-meaning-of-inner-products-of-word-vectors.mp4