This Machine Learning course curriculum is an intensive application-oriented, real-world scenario-based program using Python & Machine Learning. This course is a 60 hours program, intensive skill-oriented, practical training program required for building ML-based models. It is designed to give the participant enough exposure to the variety of applications that can be built using techniques covered under this program. This course is designed for professionals from a variety of IT backgrounds.
Key Learning Outcomes:
- Stay Industry-relevant and grow in your career.
- Create ML solutions for various business problems.
- Understand how to apply ML practices in real-world scenarios
- Define effective objectives for ML projects.
- Work with different types of data.
- Build and deploy production-grade ML applications.
200 hours (80 hours ONLINE LIVE sessions + 120 Hours of assignment)
- 14th June – 03:00 to 05:00 PM
- 29th June – 05:00 to 07:00 PM
- 4th July – 05:00 to 07:00 PM
- 20th July – 03:00 to 05:00 PM
- 3rd Aug – 05:00 to 07:00 PM
- 16th Aug – To be decided
Test & Evaluation
1. During the program, the participants will have to take all the assignments given to them for better learning.
2. At the end of the program, a final assessment will be conducted.
Welcome to Machine Learning
- Introduction To Machine Learning
- Concept of Machine Learning – What/When/Why
- Evolution of ML
- Find out where Machine Learning is applied in Technology and Science. Applications in real scenarios.
Basics of Python
- Basic programming syntax of Python, File Handling
Fundamental of Machine Learning Categories
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Working with Data
- Reading Files, Scraping the Web, Cleaning, Munging and Manipulating
- Bar Charts, Line Charts, Scatterplots
Machine Learning Python Packages
- Data Analysis Packages
Supervised Machine Learning
- Generalization, Overfitting and Underfitting
UnSupervised Machine Learning
- Challenges in unsupervised learning
Supervised Machine Learning Algorithms
- Linear models
- k-Nearest Neighbor
- Naive Bayes Classifiers
- Decision trees
Supervise ML – Classification
- Classification – LogisticRegression, Sigmoid Function
Supervised ML – Regression
- Understand how continuous supervised learning is different from discrete learning.
- Code a Linear Regression in Python with scikit-learn.
UnSupervise ML – Clustering
- k-Means clustering
Prepare Your Data For Machine Learning
- Need For Data Pre-processing
- Data Transforms
- Rescale Data
- Standardize Data
- Normalize Data
- Binarize Data
Evaluate the Performance of Machine Learning Algorithms with Resampling
- Evaluate Machine Learning Algorithms
- Split into Train and Test Sets
- K-fold Cross Validation
- Leave One Out Cross Validation
- Repeated Random Test-Train Splits
- Understand different error metrics such as MSE and MAE in the context of Machine Learning.
- Which Techniques to Use When
Industrial use case walk through
- Industrial use case walk through for supervised learning
- Industrial use case walk through for unsupervised learning
Basic Knowledge of Python Programming .
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