Machine Learning A-Z
- Offered byUDEMY
Machine Learning A-Z at UDEMY Overview
Duration | 41 hours |
Mode of learning | Online |
Difficulty level | Intermediate |
Credential | Certificate |
Future job roles | Account Planning, .Net, Black Box Testing, Assistant Vice President - IT Knowledge Banking , E Commerce Analyst |
Machine Learning A-Z at UDEMY Highlights
- 41 hours of video content
- Earn a certificate upon successful completion
- Gain Lifetime Access to Courseware
Machine Learning A-Z at UDEMY Course details
- Anyone interested in Machine Learning.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any students in college who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
- Any people who want to create added value to their business by using powerful Machine Learning tools
- Master Machine Learning on Python & R
- Have a great intuition of many Machine Learning models
- Make accurate predictions
- Make powerful analysis
- Make robust Machine Learning models
- Create strong added value to your business
- Use Machine Learning for personal purpose
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Handle advanced techniques like Dimensionality Reduction
- Know which Machine Learning model to choose for each type of problem
- Build an army of powerful Machine Learning models and know how to combine them to solve any problem
- This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.
- 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.
Machine Learning A-Z at UDEMY Curriculum
Welcome to the course!
Part 1: Data Preprocessing
Part 2: Regression
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Support Vector Regression (SVR)
Decision Tree Regression
Random Forest Regression
Evaluating Regression Models Performance
Part 3: Classification
Logistic Regression
K-Nearest Neighbors (K-NN)
Support Vector Machine (SVM)
Kernel SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
Evaluating Classification Models Performance
Part 4: Clustering
K-Means Clustering
Hierarchical Clustering
Part 5: Association Rule Learning
Apriori
Eclat
Part 6: Reinforcement Learning
Upper Confidence Bound (UCB)
Thompson Sampling
Part 7: Natural Language Processing
Part 8: Deep Learning
Artificial Neural Networks
Convolutional Neural Networks
Part 9: Dimensionality Reduction
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Kernel PCA
Part 10: Model Selection & Boosting
Model Selection
XGBoost
Bonus Lectures