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| Machine Learning |
What you'll learn
- Ace Machine Learning on Python and R
- Have an incredible instinct of many Machine Learning models
- Make precise forecasts
- Make strong examination
- Make strong Machine Learning models
- Make solid increased the value of your business
- Use Machine Learning for individual reason
- Handle explicit points like Reinforcement Learning, NLP and Deep Learning
- Handle progressed methods like Dimensionality Reduction
- Realize which Machine Learning model to decide for each sort of issue
- Fabricate a multitude of strong Machine Learning models and skill to consolidate them to take care of any issue
Description
Keen on the field of Machine Learning? Then, at that point, this course is for you!
This course has been planned by two expert Data Scientists so we can share our insight and assist you with learning complex hypothesis, calculations, and coding libraries in a basic way.
We will walk you bit by bit into the World of Machine Learning. With each instructional exercise, you will foster new abilities and further develop how you might interpret this difficult yet worthwhile sub-field of Data Science.
This course is fun and invigorating, and yet, we plunge profound into Machine Learning. It is organized the accompanying way:
- Section 1 - Data Preprocessing
- Section 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Section 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Section 4 - Clustering: K-Means, Hierarchical Clustering
- Section 5 - Association Rule Learning: Apriori, Eclat
- Section 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Section 7 - Natural Language Processing: Bag-of-words model and calculations for NLP
- Section 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Section 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
- Section 10 - Model Selection and Boosting: k-overlap Cross Validation, Parameter Tuning, Grid Search, XGBoost
Besides, the course is loaded with reasonable activities that depend on genuine models. So not exclusively will you gain proficiency with the hypothesis, however you will likewise get a few active work on building your own models.
What's more as a little something extra, this course incorporates both Python and R code formats which you can download and use on your own tasks.
Significant updates (June 2020):
- CODES ALL UP TO DATE
- Profound LEARNING CODED IN TENSORFLOW 2.0
- TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!
Who this course is for:
_ Anybody keen on Machine Learning.
- Understudies who have secondary school information in math and who need to begin learning Machine Learning.
- Any moderate level individuals who know the fundamentals of AI, including the old style calculations like straight relapse or strategic relapse, however who need to dive deeper into it and investigate every one of the various fields of Machine Learning.
- Any individuals who are not that alright with coding but rather who are keen on Machine Learning and need to apply it effectively on datasets.
- Any understudies in school who need to begin a profession in Data Science.
- Any information investigators who need to step up in Machine Learning.
- Any individuals who are not happy with their work and who need to turn into a Data Scientist.
- Any individuals who need to make increased the value of their business by utilizing strong Machine Learning apparatuses.
