About the Program
Data Science is a method of huge data analysis that automates analytical model building. It is a branch of artificial intelligence. Lot of opportunities is out there in field of ml and AI in India. People already using ml in field of image processing, pattern analysis, marketing, data analysis having pretty good future in India. The Machine Learning sessions provide the participants technical training on the concepts and Machine Learning algorithms to develop the code. Participants will also learn to use different Python libraries. Instruction cum aided with live projects which will allow students to grasp concepts of the complete data science development
life-cycle.
Technologies and Tools Covered
- ANACONDA NAVIGATOR & NUMPY
- PYTHON PROGRAMMING LANGUAGE
- PYTHON PACKAGES/ LIBRARIES
- SVM and NN ALGORITHMS
LEARNING PATH
Introduction to Python and Machine Learning
SESSION | CLASS TOPICS |
| 1 | Introduction to Python Programming |
| 2 | Software Installation IDE, Anaconda Navigator |
| 3 | Introduction to Machine Learning and Deep Learning Package |
| 4 | Training Machine Learning Model |
| 5 | Inference from Machine Learning Model |
2. Data Science and Algorithm Development
Here’s how to make a Sequential Model and a few commonly used layers in deep learning.
Example of how to make convolutional layer as the input layer with the input with filters of size and use ReLU as an activation function. How to down sample the input representation, use MaxPool2d and specify the kernel size. Adding a Fully Connected Layer with just specifying the output Size. Adding dropout layer with Compiling, Training, and Evaluate
SESSION | CLASS TOPICS |
| 1 | Matplotlib and its Working – Packages & Applications |
| 2 | Scikit learn, Keras and TensorFlow – Data Science Packages |
| 3 | Working with K means algorithm |
| 4 | Creating a model using Support vector machine ( SVM Algorithm ) |
| 5 | Working with linear regression models |
| 6 | Naive bayes Algorithms & Practical Implementation |
3. Data Science Algorithm & Practical Implementation
| SESSION | CLASS TOPICS |
| 1 | Naive bayes Algorithms & Practical Implementation |
| 2 | Random forest Algorithms & Applications |
| 3 | Decision tree and its working Demo’s |
| 4 | Working with Boosting and bagging regression Algorithms |
| 5 | Long-Short Term Memory & Implicational Strategies |
Training Methodology
The Program is mix of Theory sessions, Hands on Sessions, Liver Interaction with Experts,
Assignments and Practical Exercises. Maximum Impetus is given to Hands on Sessions so as to
enable the participants with the maximum knowledge transfer and satisfaction. The ratio of the
theory, practical sessions will be 30:70.
Pantech eLearning
Agile Project Expert
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