Data Science (DS) and Machine Learning (ML) are two closely related fields that involve the extraction of valuable insights and predictions from data using advanced analytical techniques and algorithms. They play a crucial role in various industries by enabling data-driven decision-making, automation, and the development of intelligent systems. While related, Data Science and Machine Learning have distinct focuses and methodologies.
An ISO 9001:2015 &
Teacher Scientist Network Certified
Welcome to the Professional Certification Program in Data Science and Machine Learning! This comprehensive 90-day course is designed for beginners who are eager to embark on a journey into the world of data science and machine learning. With over 20 years of industry and teaching experience, our expert instructors will guide you through the fundamentals of data analysis, machine learning algorithms, and practical applications. By the end of this program, you will have a solid foundation in data science and machine learning concepts, empowering you to solve real-world problems and make data-driven decisions.
– Introduce the fundamentals of data science and its applications.
– Develop essential skills in data manipulation, exploration, and visualization.
– Understand the principles of machine learning and its algorithms.
– Gain hands-on experience in building machine learning models.
– Apply your knowledge through practical projects and case studies.
The 90-day program is divided into ten modules, each focusing on core concepts in data science and machine learning:
– What is Data Science?
– What is Machine Learning?
– The Data Science Process
– Overview of Python for Data Science
– Introduction to Python
– Working with NumPy, Pandas, and Matplotlib
– Data Cleaning and Preprocessing
– Data Visualization with Seaborn and Matplotlib
– Descriptive Statistics
– Probability and Distributions
– Hypothesis Testing
– Introduction to Supervised and Unsupervised Learning
– Model Evaluation and Metrics
– Overfitting and Underfitting
– Linear Regression
– Logistic Regression
– Decision Trees and Random Forests
– Support Vector Machines (SVM)
– K-Means Clustering
– Hierarchical Clustering
– Dimensionality Reduction with PCA
– Neural Networks Overview
– Building and Training Neural Networks with TensorFlow/Keras
– Text Preprocessing
– Text Classification with NLP
– Collaborative Filtering
– Content-Based Filtering
1. Exploratory Data Analysis (EDA) on a Dataset:
Perform data cleaning, visualization, and basic statistical analysis on a real-world dataset.
2. Predicting House Prices:
Build a linear regression model to predict house prices based on features like area, location, etc.
3. Customer Segmentation:
Apply K-Means clustering to group customers based on their purchasing behavior.
4. Image Classification with Deep Learning:
Build a neural network to classify images from a standard dataset like MNIST or CIFAR-10
5. Sentiment Analysis on Movie Reviews:
Create an NLP model to predict whether a movie review is positive or negative.
6. Titanic Survival Prediction:
Use logistic regression or decision trees to predict whether a passenger survived the Titanic disaster
7. Spam Email Classification:
Implement a text classification model to classify emails as spam or non-spam.
8. Credit Card Fraud Detection:
Apply anomaly detection techniques to identify fraudulent credit card transactions.
9. Movie Recommender System:
Create a movie recommender system using collaborative filtering or content-based filtering.
10. Capstone Project:
Choose a real-world problem or dataset and apply various data science and machine learning techniques to solve it.
Course Essentials :
This program is ideal for beginners with little or no prior experience in data science and machine learning but have a keen interest in data analysis and predictive modeling. Whether you are a recent graduate, a working professional looking to switch careers, or someone interested in data-driven decision-making, this course will provide you with the essential skills to kickstart your journey in the data science field.
Earned with Excellence: