Data Science and Machine Learning Course

Data Science and Machine Learning Course | Become a Data Scientist

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About This Course

This comprehensive 120-hour course provides a deep dive into the world of data science and machine learning. It equips you with the essential skills and knowledge to extract valuable insights from data and build intelligent applications.

What You’ll Learn?

Programming Fundamentals: Python, Data structures, algorithms, and object-oriented programming.
Data Analysis: Clean, preprocess, and analyze data using statistical techniques and visualization tools.
Machine Learning: Explore various machine learning algorithms, including supervised, unsupervised, and reinforcement learning.

Deep Learning: Dive into neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs)for advanced applications.
Generative AI: Principles and applications of generative AI, including models like ChatGPT.
Practical Projects: Apply your knowledge to real-world data science and machine learning projects.

Who is this program for

Want to transition into a data science career
Are looking to enhance their existing data analysis skills
Are interested in building AI-powered applications

Topics for This Course

Introduction to Python Language
Grammar of Python
Python collections: List, Tuple, Set, Dict, Range
Function and Recursion in Python
Object Oriented Programming (OOP) Concept in Python
Exception handling in Python
Regular Expression and Multithreading
Data Structures in Python
Algorithm Design Techniques
Practicing Python libraries

Data Definition Language (DDL): Commands like CREATE, ALTER, and DROP to define and modify the structure of database objects.
Data Manipulation Language (DML): Commands like INSERT, UPDATE, and DELETE to retrieve and manipulate data.
Data Query Language (DML): Commands like SELECT, ORDER BY,GROUP BY, and JOIN to query the database.
Data Control Language (DCL): Commands like GRANT and REVOKE to control access to data.
Transaction Control Language (TCL): Commands like COMMIT and ROLLBACK to manage database transactions.
ACID Properties and Database Normalization.
Data Analysis in & using SQL

HTML: Introduction to HTML, Practicing HTML tags
CSS: CSS Basics, CSS properties for tags, CSS Grid and Flexbox, Responsive Design, Frameworks (e.g., Bootstrap)
JavaScript: Introduction to JavaScript, Datatypes, Variables, Operators, Conditions, Loop, Arrays, Functions, Objects, Object properties and methods, JS Events, JS Strings and methods, JS RegExp, JS Classes, JS Web APIs, JS JSON, jQuery

Introduction to Data Science: What is Data Science?, Overview of Data Science tools and technologies, Introduction to Python programming for Data Science.
Data Collection and Preprocessing: Data sources and types, Data acquisition techniques, Data cleaning and preprocessing using pandas.
Data Visualization: Principles of data visualization, Plotting with matplotlib and seaborn, Interactive visualizations with Plotly.
Inferential Statistics: Basics of Probability, Discrete Probability Distributions, Continuous Probability Distributions, Central Limit Theorem
Hypothesis Testing: Concepts of Hypothesis Testing - I(Null and Alternate Hypothesis, Making a Decision, and Critical Value Method), Concepts of Hypothesis Testing - II(p-Value Method and Types of Errors), Industry Demonstration of Hypothesis Testing (Two-Sample Mean and PROPORTION Test, A/B Testing)
Exploratory Data Analysis: Descriptive statistics, Distribution analysis, Correlation and covariance, Hypothesis testing.
Capstone Project: Apply the knowledge and skills learned throughout the course to complete a data science project, Present findings and insights derived from the project.

Introduction to Machine Learning: Overview of machine learning and its applications, Types of machine learning: supervised, unsupervised, and reinforcement learning, Machine learning workflow and common terminology.
Supervised Learning: Linearregression, Logistic regression, Support Vector Machines (SVM).
Unsupervised Learning: K-means clustering, Hierarchical clustering, Principal Component Analysis (PCA).
Evaluation and Model Selection: Model evaluation metrics: accuracy, precision,recall, F1-score, ROC curve, Cross-validation techniques, Hyperparameter tuning.
Introduction to Natural Language Processing: Basics of NLP, Modelling on Text data, Deep Learning for NLP, Basic Lexical Processing, Advanced Lexical Processing, Sentiment analysis, Text summarization, and Machine translation.
BAGGING & RANDOM FOREST: Popular Ensembles, Introduction to Random Forests, Feature Importance in Random Forests, Random Forests in Python.
BOOSTING: Introduction to Boosting and AdaBoost, Gradient Boosting.
MODEL SELECTION & GENERAL ML TECHNIQUES: Principles of Model Selection, Model Evaluation, Model Selection Best Practices.
PRINCIPAL COMPONENT ANALYSIS: Principal Component Analysis and Singular Value Decomposition, Principal Component Analysis in Python.
ADVANCED REGRESSION: GENERALISED Linear Regression, REGULARISED Regression
TIME SERIES FORECASTING: Introduction to Time Series and its Components, Working with Stationary Time Series, End-to-End Analysis of Time Series.
Introduction to Deep Learning: Basics of neural networks, Structure of Neural Networks, Feed Forward in Neural Networks, Backpropagation in Neural Networks, Modifications to Neural Networks, Hyperparameter Tuning in Neural Networks, Optimization of Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Introduction to TensorFlow and Keras, Deep Learning Applications in Computer Vision, Time Series Modeling.
Project Work: Apply machine learning algorithms and techniques to real-world datasets, Design and implement a machine learning project, Present findings and insights derived from the project.