<blockquote style=" margin: 0px 0px 1.6rem; font-size: 2.4rem; font-weight: 700; max-width: 118.4rem; font-family: var(--font-stack-heading); line-height: 1.2">Description</blockquote>


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<p style=" font-size: inherit; max-width: 118.4rem">Interested in the field of Machine Learning?&nbsp;Then this course is for you!</p>
<p style=" margin: 0.8rem 0px 0px; font-size: inherit; max-width: 118.4rem">This course has been designed by a<span>&nbsp;</span><strong>Data Scientist and a Machine Learning expert</strong><span>&nbsp;</span>so that we can share our knowledge and help you learn complex theory,&nbsp;algorithms, and coding libraries in a simple way.</p>
<p style=" margin: 0.8rem 0px 0px; font-size: inherit; max-width: 118.4rem"><strong>Over 1&nbsp;Million students</strong><span>&nbsp;</span>world-wide trust this course.</p>
<p style=" margin: 0.8rem 0px 0px; font-size: inherit; max-width: 118.4rem">We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.</p>
<p style=" margin: 0.8rem 0px 0px; font-size: inherit; max-width: 118.4rem">This course can be completed by either doing either the<span>&nbsp;</span><strong>Python tutorials, or R tutorials,</strong><span>&nbsp;</span>or both -&nbsp;Python &amp;&nbsp;R. Pick the programming language that you need for your career.</p>
<p style=" margin: 0.8rem 0px 0px; font-size: inherit; max-width: 118.4rem">This course is&nbsp;fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:</p>
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	<p style=" font-size: inherit; max-width: 118.4rem">Part 1 - Data Preprocessing</p>
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	<p style=" font-size: inherit; max-width: 118.4rem">Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression,&nbsp;Polynomial&nbsp;Regression,&nbsp;SVR, Decision Tree Regression,&nbsp;Random Forest Regression</p>
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	<p style=" font-size: inherit; max-width: 118.4rem">Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification,&nbsp;Random&nbsp;Forest Classification</p>
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	<p style=" font-size: inherit; max-width: 118.4rem">Part 4 - Clustering: K-Means,&nbsp;Hierarchical Clustering</p>
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	<p style=" font-size: inherit; max-width: 118.4rem">Part 5 - Association Rule Learning: Apriori,&nbsp;Eclat</p>
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	<p style=" font-size: inherit; max-width: 118.4rem">Part 6 - Reinforcement Learning:&nbsp;Upper Confidence Bound,&nbsp;Thompson Sampling</p>
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	<p style=" font-size: inherit; max-width: 118.4rem">Part 7 - Natural Language Processing: Bag-of-words model&nbsp;and&nbsp;algorithms for NLP</p>
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	<p style=" font-size: inherit; max-width: 118.4rem">Part 8 - Deep Learning: Artificial Neural Networks,&nbsp;Convolutional Neural Networks</p>
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	<p style=" font-size: inherit; max-width: 118.4rem">Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA</p>
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	<p style=" font-size: inherit; max-width: 118.4rem">Part 10 - Model Selection &amp; Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search,&nbsp;XGBoost</p>
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