Study to create Machine Studying Algorithms in Python and R from two Information Science specialists. Code templates included.
What you will learn
- Grasp Machine Studying on Python & R
- Have an amazing instinct of many Machine Studying fashions
- Make correct predictions
- Make highly effective evaluation
- Make strong Machine Studying fashions
- Create sturdy added worth to your corporation
- Use Machine Studying for private goal
- Deal with particular subjects like Reinforcement Studying, NLP and Deep Studying
- Deal with superior strategies like Dimensionality Discount
- Know which Machine Studying mannequin to decide on for every kind of downside
- Construct a military of highly effective Machine Studying fashions and know the best way to mix them to resolve any downside
- Just a few highschool arithmetic degree.
This course has been designed by two skilled Information Scientists in order that we are able to share our information and allow you to be taught complicated principle, algorithms and coding libraries in a easy manner.
We are going to stroll you step-by-step into the World of Machine Studying. With each tutorial you'll develop new abilities and enhance your understanding of this difficult but profitable sub-field of Information Science.
This course is enjoyable and thrilling, however on the identical time we dive deep into Machine Studying. It's structured the next manner:
- Half 1 - Information Preprocessing
- Half 2 - Regression: Easy Linear Regression, A number of Linear Regression, Polynomial Regression, SVR, Determination Tree Regression, Random Forest Regression
- Half 3 - Classification: Logistic Regression, Okay-NN, SVM, Kernel SVM, Naive Bayes, Determination Tree Classification, Random Forest Classification
- Half 4 - Clustering: Okay-Means, Hierarchical Clustering
- Half 5 - Affiliation Rule Studying: Apriori, Eclat
- Half 6 - Reinforcement Studying: Higher Confidence Sure, Thompson Sampling
- Half 7 - Pure Language Processing: Bag-of-words mannequin and algorithms for NLP
- Half 8 - Deep Studying: Synthetic Neural Networks, Convolutional Neural Networks
- Half 9 - Dimensionality Discount: PCA, LDA, Kernel PCA
- Half 10 - Mannequin Choice & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
And as a bonus, this course consists of each Python and R code templates which you'll obtain and use by yourself initiatives.
Who this course is for:
- Anybody fascinated by Machine Studying.
- College students who've a minimum of highschool information in math and who need to begin studying Machine Studying.
- Any intermediate degree individuals who know the fundamentals of machine studying, together with the classical algorithms like linear regression or logistic regression, however who need to be taught extra about it and discover all of the completely different fields of Machine Studying.
- Any people who find themselves not that comfy with coding however who're fascinated by Machine Studying and need to apply it simply on datasets.
- Any college students in school who need to begin a profession in Information Science.
- Any information analysts who need to degree up in Machine Studying.
- Any people who find themselves not glad with their job and who need to change into a Information Scientist.
- Any individuals who need to create added worth to their enterprise through the use of highly effective Machine Studying instruments.