Interested in the field of Machine Learning? Here is a short and useful Review of Machine Learning Course A-Z: Hands-On Python & R in Data Science. This course potentiality brings you to build your successful career in data science.
This is one of the Best Selling courses on Udemy where over 526.6K+ students enrolled and have a 4.5/5 star rating with 103.5K+ reviews. With this Best Machine Learning tutorial, you will learn to create Machine Learning Algorithms in both Python and R from Data Science experts.
Review of Machine Learning Course A-Z: Hands-On Python & R In Data Science
1. What to Learn in this highest-selling Machine Learning Course?
- 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 for your business
- Use Machine Learning for personal purposes
- 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
2. What is included in this Machine Learning Certification Course Syllabus?
- Lecture 1: Data Preprocessing
- Lecture 2: Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Lecture 3: Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Lecture 4: Clustering: K-Means, Hierarchical Clustering
- Lecture 5: Association Rule Learning: Apriori, Eclat
- Lecture 6: Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Lecture 7: Natural Language Processing: Bag-of-words model and algorithms for NLP
- Lecture 8: Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Lecture 9: Dimensionality Reduction: PCA, LDA, Kernel PCA
- Lecture 10: Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
3. Who is the target audience?
- Interested in Machine Learning.
- Having at least a high school knowledge in math and who knows the basics of Machine Learning.
- Code free Machine Learning and want to apply it easily to datasets.
- Fascinated to start a career in Data Science.
- Wants to become a Data Scientist.
- Any data analysts who are not satisfied with their job.
- And who wants to add value to their business by using powerful Machine Learning tools?
4. Who are the Instructors of this Machine Learning Course A-Z?
Kirill Eremenko is a data science coach and lifestyle entrepreneur and an aspiring Data Scientist & Forex Systems Expert with a 4.5 average rating and 324.5K+ reviews. He has 1,062.8K+ Students and 82 Courses.
Hadelin de Ponteves is an Artificial Intelligence (AI) Entrepreneur with a 4.5 average rating and 176.3K+ reviews. He got more than 749.3K+ students and 54 courses.
SuperDataScience Team has 301K+ reviews and 996.5K+ students with 4.5 average ratings. They help Data Scientists to become Succeed in the Data Science field.
Final Word
The Machine Learning A-Z has been designed in a way where the instructors share their knowledge and help students to learn complex theories, algorithms, and coding libraries in a simple way. Hope this Review of Machine Learning Course A-Z will help you to get started.
Furthermore, the course is packed with practical exercises which are based on real-life examples. Along with the theory, you will also learn hands-on practice building your own models.
The course includes both Python and R code templates which are downloadable and can be used on your own projects as well.
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