This Data Science course curriculum is an intensive application-oriented, real-world scenario-based program using Artificial Intelligence, Machine Learning & Deep Learning. This course is a 80 hours program, intensive skill-oriented, practical training program required for building AI-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 experienced professionals from a variety of IT backgrounds.
Key Learning Outcomes
- Stay Industry-relevant and grow in your career.
- Use Data Science solutions for various business problems.
- Understand how to apply Data Science practices in real-world scenarios.
- Define effective objectives for Data Science projects.
- Work with different types of data.
- Build and deploy production-grade AI/ML applications.
- Learn to Apply methods, techniques, and tools of Data Science.
200 hours ( 80 hours ONLINE LIVE sessions + 120 Hours of assignment
- 29th June – 03:00 to 05:00 PM
- 4th July – 03:00 to 05:00 PM
- 20th July – 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.
Introduction to Data Science:
- Introduction To Data Science, AI & ML
- Concept of Data Science – What/When/Why
- Evolution of ML & AI
- Find out where Data Science is applied in Technology and Science. Applications in real scenarios.
Basics of Python:
- Basic programming syntax of Python, File Handling
Fundamental of ML Categories:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Working with Data:
- Reading Files, Scraping the Web, Cleaning, Munging and Manipulating
Prepare Your Data For Machine Learning Data Collection, Pre-processing and Transformation for Machine Learning:
- Types of Data collection- Offline Data and Online Data
- Practical Implimentation of Reading the offline dataset using Numpy
- Practical Implimentation of Reading the online dataset using Numpy
- Practical Implimentation of Reading the offline dataset using Pandas
- Practical Implimentation of Reading the online iris dataset using Pandas
- Need For Data Pre-processing
- Prepare Your Data For Machine Learning
- Bar Charts, Line Charts, Scatterplots
Data Visualization for Machine Learning using Matplotlib:
- Concept of Data Visualization and matplotlib
- Plotting Lines to represent the data for Machine Learning
- Univariate Plotting
- Multivariate Plotting
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.
Practical implimentation of Supervised Machine Learning Algorithm:
- Implimentation of Supervised Machine Learning Algirithms
- Regression and Classification
- Linear Regression and Logistic Regression
- Practical Implimentation of Machine Learning Supervised Algirithms- LinearRegression , LogisticRegression, Concept of Sigmoid Function
UnSupervise ML – Clustering:
- k-Means clustering
Practical implimentation of Unsupervised Machine Learning Algorithm:
- Concepts and Steps of Unsupervised Machine Learning Algorithm and Clustering ,
- Practical Implimentation of Machine Learning UnSupervised Algirithms- 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
Deep Learning – Introduction:
- A revolution in Artificial Intelligence
- Limitations of Machine Learning
- What is Deep Learning?
- Advantage of Deep Learning over Machine learning
Introduction To Neural Networks:
- How Deep Learning Works?
- The Neuron
- Introduction to Neural Networks
Deep dive into Neural Networks:
- Neural Network Layers
- Neural Network Architecture
- Activation Functions
- Training a Perceptron
- Generalization, Overfitting, Underfitting
Neural Networks with TensorFlow:
- Understand limitations of a Single Perceptron
- Deepening the network
- Images and Pixels
- How humans recognise images
- Convolutional Neural Networks
Industrial use case walk through -1:
- Industrial use case walk through for supervised learning
- Industrial use case walk through for unsupervised learning
Industrial use case walk through – 2:
- Industrial use case walk through for Deep Neural Network
Basic Knowledge of Python Programming .
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