Machine Learning with Python
Learn with hands-on examples in 40hours
About the Course
THREE components: Python for Machine Learning, Machine Learning and Projects.
Course in Brief
Python 3.x (Any Platform) with Pycharm and Jupyter Notebook
Python Mini Course: Fundamentals with Python and IDEs, Basic tokens, Standard I/O, Control Structures, Strings, Container Types [List, Set, Tuple and Dictionary], Exception Handling, Local File Handling, Working with CSV.
Machine Learning: Working with datasets, Descriptive Statistics, Data Visualization, ML Algorithms, Model selections, Pipelines, One ML Project.
Prerequisites for this course
No prior programming language knowledge,
Comfortable with variables (dependent and independent), linear/quadratic equations, graphs of functions, histograms, probability and statistics.
Basic Computer H/W and S/W Knowledge
Desired to Learn
Learners’ must have a system with any 64-bit OS, preferably Windows 10
Instructor: Mr. Ranjit Pr
Areas of expertise: IoT, Embedded Systems, Data sciences, Machine learning, and Data visualisation
Over 20+ years of industry experience
Trained over 1000 professionals and students
Python Language & Libraries
1. Download, Install Python in various platforms and Setting the environmental variables
- IDEs & Settings
- Basic Tokens (Data Types, Operators, Input/Output, Control Structure)
2. Container types (String, List, Set, Tuple, Dictionary)
3. Working with Functions / Modules / Libraries
4. Quick OOPs
5. Data File & Exception Handling
6. Working with CSV
- Understand Data with Descriptive Statistics.
- Understand Data with Visualization.
7 .Step-by-step lessons you will complete:
- Get Around in Python, NumPy, SciPy, Matplotlib and Pandas.
* Practice assignments:
Working with lists and flow control,
Creating simple plots in Matplotlib.
Pandas Series and Data Frames.
* Load Datasets from CSV.
* Understand Data with Descriptive Statistics.
* Understand Data with Visualization.
Machine Learning Algorithms
8. Supervised Machine Learning
- Algorithms:Regression (Linear and Polynomial)
- Classification (Decision Tree, SVM, K-Nearest
Neighbour, & Logistic Regression)
9. Unsupervised Machine Learning
- Algorithms:Clustering (K-means, Hierarchical clustering & Density-Based Clustering)
- Association (Apriori, FP-Growth)
- Model Selection.
- Ensemble Methods.
- Model Finalization.
11. Projects- Three end-to-end projects you will complete.