Course Summary
Learn how to use Python for machine learning in this hands-on course. Gain practical skills in data preprocessing, modeling, and evaluation, and apply them to real-world problems.Key Learning Points
- Learn the basics of Python programming language and its application in machine learning
- Explore data preprocessing, model selection, and evaluation techniques
- Apply your skills to real-world problems and datasets
Job Positions & Salaries of people who have taken this course might have
- USA: $112,000
- India: ₹1,100,000
- Spain: €45,000
- USA: $112,000
- India: ₹1,100,000
- Spain: €45,000
- USA: $96,000
- India: ₹900,000
- Spain: €38,000
- USA: $112,000
- India: ₹1,100,000
- Spain: €45,000
- USA: $96,000
- India: ₹900,000
- Spain: €38,000
- USA: $129,000
- India: ₹1,200,000
- Spain: €50,000
Related Topics for further study
Learning Outcomes
- Develop practical skills in Python programming for machine learning
- Understand data preprocessing and model selection techniques
- Apply machine learning to real-world problems and datasets
Prerequisites or good to have knowledge before taking this course
- Basic understanding of Python programming
- Familiarity with linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Hands-on exercises and projects
- Video lectures and quizzes
Similar Courses
- Applied Data Science with Python
- Data Science Essentials
- Applied Machine Learning
Related Education Paths
- IBM Data Science Professional Certificate
- Google IT Automation with Python Professional Certificate
- Microsoft Professional Program in Artificial Intelligence
Related Books
Description
This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.
Outline
- Introduction to Machine Learning
- Welcome
- Introduction to Machine Learning
- Python for Machine Learning
- Supervised vs Unsupervised
- Intro to Machine Learning
- Regression
- Introduction to Regression
- Simple Linear Regression
- Model Evaluation in Regression Models
- Evaluation Metrics in Regression Models
- Multiple Linear Regression
- Non-Linear Regression
- Regression
- Classification
- Introduction to Classification
- K-Nearest Neighbours
- Evaluation Metrics in Classification
- Introduction to Decision Trees
- Building Decision Trees
- Intro to Logistic Regression
- Logistic regression vs Linear regression
- Logistic Regression Training
- Support Vector Machine
- Classification
- Clustering
- Intro to Clustering
- Intro to k-Means
- More on k-Means
- Intro to Hierarchical Clustering
- More on Hierarchical Clustering
- DBSCAN
- Clustering
- Recommender Systems
- Intro to Recommender Systems
- Content-based Recommender Systems
- Collaborative Filtering
- Recommender System
- Final Project
- OPTIONAL: Signing-up for a Watson Studio Account
- OPTIONAL: Sharing Notebooks on Watson Studio
- Congratulations!
- IBM Digital Badge
- Final Exam
Summary of User Reviews
Discover the power of machine learning with Python in this comprehensive course. Students rave about the thorough explanations and hands-on practice. One key aspect that many users thought was good is the instructor's ability to break down complex topics and make them easy to understand. However, some users have mentioned a few cons, such as the course can be too basic for those with prior experience in machine learning and there are occasional technical glitches.Key Aspect Users Liked About This Course
The instructor's ability to break down complex topics and make them easy to understandPros from User Reviews
- Thorough explanations and hands-on practice
- Great for beginners
- Instructor breaks down complex topics well
Cons from User Reviews
- Can be too basic for those with prior experience in machine learning
- Occasional technical glitches
- Some lectures can be lengthy and repetitive
SAEED AGHABOZORGI
4.7 Raiting
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ペタバイト規模のデータに対して数秒でクエリを実行して処理する方法や、データの増大に合わせて自動的にスケーリングされるデータ分析に関心がある場合は、ぜひ Data Insights コースを受講してください。 この 1 週間のオンライン速習コースに参加すると、Google Cloud Platform を使用したデータ分析と可視化によって分析情報を得る方法を学習できます。本コースにはインタラクティブなシナリオとハンズオンラボが用意されており、参加者は Google BigQuery のさまざまなデータセットを使用して、分析情報の探索、マイニング、読み込み、可視化、抽出を行うことができます。本コースでは、データの読み込み、クエリの実行、スキーマのモデル化、パフォーマンスの最適化、クエリの料金、データの可視化を扱います。 前提条件 本コースを最大限に活用するには、この専門講座で以下のコースを事前に完了する必要があります。 • Exploring and Preparing your Data...
