Machine Learning - Python & R

Full udemy people for free.

What You Will Learn

Master Machine Learning connected Python & R
Have a large intuition of galore Machine Learning models
Make close predictions
Make a almighty analysis
Make robust Machine Learning models
Create beardown added worth to your business
Use Machine Learning for idiosyncratic purpose
Handle circumstantial topics similar Reinforcement Learning, NLP and Deep Learning
Handle precocious techniques similar Dimensionality Reduction
Know which Machine Learning exemplary to take for each benignant of problem
Build an service of almighty Machine Learning models and cognize however to harvester them to lick immoderate problem

Description
Interested successful the tract of Machine Learning? Then this people is for you!

This people has been designed by 2 nonrecreational Data Scientists truthful that we tin stock our cognition and assistance you larn analyzable theory, algorithms and coding libraries successful a elemental way.

We volition locomotion you step-by-step into the World of Machine Learning. With each tutorial you volition make caller skills and amended your knowing of this challenging yet lucrative sub-field of Data Science.

This people is amusive and exciting, but astatine the aforesaid clip we dive heavy into Machine Learning. It is structured the pursuing way:

Part 1 - Data Preprocessing
Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Part 4 - Clustering: K-Means, Hierarchical Clustering
Part 5 - Association Rule Learning: Apriori, Eclat
Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
Part 7 - Natural Language Processing: Bag-of-words exemplary and algorithms for NLP
Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Moreover, the people is packed with applicable exercises which are based connected real-life examples. So not lone volition you larn the theory, but you volition besides get immoderate hands-on signifier gathering your ain models.

And arsenic a bonus, this people includes some Python and R codification templates which you tin download and usage connected your ain projects.

Who this people is for:
Anyone funny successful Machine Learning.
Students who person astatine slightest precocious schoolhouse cognition successful mathematics and who privation to commencement learning Machine Learning.
Any intermediate level radical who cognize the basics of instrumentality learning, including the classical algorithms similar linear regression oregon logistic regression, but who privation to larn much astir it and research each the antithetic fields of Machine Learning.
Any radical who are not that comfy with coding but who are funny successful Machine Learning and privation to use it easy connected datasets.
Any students successful assemblage who privation to commencement a vocation successful Data Science.
Any information analysts who privation to level up successful Machine Learning.
Any radical who are not satisfied with their occupation and who privation to go a Data Scientist.
Any radical who privation to make added worth to their concern by utilizing almighty Machine Learning tools.

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