Data Science , ML, AI with Python ( End-to-End Live Training)

admin@academe
Last Update October 23, 2024

About This Course

The Data Science , ML, AI with Python ( End-to-End Live Training): An All-in-One Comprehensive Learning Journey

The Data Science Expert Program is a carefully curated course that empowers students with the skills, knowledge, and practical tools required to thrive in the field of data science. Whether you are just starting your data science journey or you are an experienced professional looking to sharpen your analytical edge, this course offers an end-to-end learning experience.

Designed to meet the diverse needs of learners, the curriculum covers core concepts such as statistics, programming fundamentals, and exploratory data analysis, while also diving deep into machine learning, neural networks, cloud computing, and Big Data processing. Learners will have the opportunity to apply their knowledge through real-world datasets, build interactive dashboards, and deploy models to cloud platforms like AWS and Azure.

By the end of the course, learners will have gained hands-on experience, industry insights, and an in-depth understanding of what it takes to become a job-ready data scientist in today’s competitive job market.


Why This Course is Perfect for Both Beginners and Experienced Professionals

  • Beginners will find step-by-step guidance to understand foundational concepts, build confidence, and develop their technical skills. Each module includes interactive exercises to solidify learning.
  • Experienced professionals can dive straight into advanced modules, focusing on deep learning, cloud deployments, and Big Data analytics, while refining their machine learning models with cutting-edge techniques.

What Makes This Course Unique?

  • Comprehensive Curriculum: Covers everything from the basics to advanced-level content, ensuring a complete learning journey.
  • Practical Hands-On Learning: Build a portfolio with 5+ industry-standard projects across various domains, such as finance, healthcare, and e-commerce.
  • Cloud & Big Data Exposure: Gain expertise in AWS, Azure, Spark, and Hadoop, which are critical for large-scale data science operations.
  • Flexibility for All Skill Levels: Designed to accommodate both beginners and experienced learners by providing tailored pathways within the course modules.

What Beginners Will Gain:

  • A Strong Foundation in Python, SQL, R, and statistics to build confidence.
  • Step-by-step guidance through projects, such as exploratory data analysis (EDA) and linear regression models.
  • Foundational understanding of machine learning, focusing on simple algorithms like linear and logistic regression.
  • Introduction to visualization tools such as Power BI and Tableau, helping students tell a compelling story with data.

What Experienced Professionals Will Gain

  • Advanced Machine Learning Techniques: Explore ensemble learning (XGBoost, Random Forest) and dive into natural language processing (NLP) using tools like spaCy and Transformers.
  • Deep Learning Mastery: Gain hands-on experience building neural networks using TensorFlow and Keras and train models for image recognition and NLP tasks.
  • Big Data Expertise: Process and analyze massive datasets using Spark and Kafka, and learn to deploy data pipelines on cloud platforms like AWS and Azure.
  • Deployment and Monitoring: Create and deploy APIs using Flask or FastAPI, and monitor models in production using MLflow or Prometheus.

How This Program Ensures Your Success

  1. Learn by Doing: Each module concludes with a hands-on project designed to simulate real-world challenges, ensuring you gain practical experience.
  2. Tailored Learning Paths: Customize your journey based on your background—progress quickly through familiar topics or explore advanced concepts deeply.
  3. Real-World Datasets: Work with diverse datasets—from predictive models in finance to customer segmentation in retail—allowing you to apply concepts in multiple industries.
  4. Continuous Support and Mentorship: Access to mentors and industry experts for 1-on-1 guidance and feedback throughout your learning journey.
  5. Lifetime Access to Resources: Revisit lessons, materials, and recorded sessions any time with lifetime access to all course content.

Projects Designed for Every Skill Level

Throughout the course, learners will complete several projects tailored to match their proficiency level:

For Beginners:

  • EDA on the Titanic Dataset: Learn data cleaning, manipulation, and visualization.
  • Customer Behavior Analysis Using SQL: Extract and analyze sales data to understand customer trends.
  • Simple Linear Regression: Predict housing prices using basic regression techniques.

For Experienced Professionals:

  • Sentiment Analysis with NLP Models: Analyze product reviews from e-commerce data using advanced NLP techniques.
  • Time Series Forecasting Using ARIMA Models: Predict future sales or stock prices.
  • Deploying a Machine Learning Model on AWS Lambda: Build and deploy a real-time recommendation engine using Flask.

Assessment and Certification

  • Quizzes & Assignments after each module to assess your knowledge.
  • A Capstone Project to demonstrate your expertise by solving a real-world business problem.
  • Upon successful completion, earn a Data Science Expert Certification to boost your resume and career prospects.

Course Delivery and Support

  • Interactive Learning Platform: Participate in live classes and discussions with instructors and peers.
  • 24/7 Mentor Support: Resolve your queries and get feedback any time you need.
  • Self-Paced Learning Option: Follow a flexible schedule that suits your pace and availability.
  • Career Guidance & Placement Assistance: Access mock interviews, resume-building workshops, and job opportunities through our industry connections.

Course Features

  • Project-Based Learning: Apply what you learn through 5+ industry-relevant projects.
  • Interactive Quizzes & Assignments: Test your knowledge with coding challenges.
  • Certification: Receive a recognized Data Science Certification.
  • 24/7 Mentor Support: Get your doubts clarified anytime by industry experts.
  • Job Assistance: Access resume building, mock interviews, and job placement support.

Course Enrollment & Payment Options

  • Flexible Payment Plans: Pay in installments or opt for scholarships.
  • Corporate Training Packages: Available for teams and organizations.
  • Discounts: Early-bird offers and referral bonuses.

Why Choose Us?

  • Industry Experts as Instructors: Learn from seasoned professionals.
  • Real-world Projects: Work on datasets from finance, healthcare, and e-commerce.
  • Certification: Add a recognized certification to your resume.
  • Placement Assistance: Get job-ready with personalized support.

Curriculum

227 Lessons

Introduction to Python

What is Python?00:00:00
Why Python?00:00:00
Data Types and Data Structures00:00:00
Installing Python00:00:00
Python IDEs00:00:00
Why does Data Science require Python?00:00:00
Installation of Anaconda00:00:00
Understanding Jupyter Notebook00:00:00
Basic commands in Jupyter Notebook00:00:00
Understanding Python Syntax00:00:00
Python Basic Data types00:00:00
Data Structures :Lists, Dictionaries, Tuples, Sets00:00:00
Slicing00:00:00
Conditional Statement00:00:00
Loops00:00:00
Functions00:00:00
Array00:00:00
Selection by position & Labels00:00:00

Statistics in Data science

Data Gathering Techniques

Descriptive Statistics

Probability Distribution

Inferential Statistics

Numpy – Numerical Python

Data Manipulation with Pandas

Data Visualization using Matplotlib and Pandas

MODULE 2:Machine Learning Using Python

Introduction to Machine Learning

Regression Techniques

Multiple Linear Regression

Polynomial Regression

Regularization Techniques

Case Study on Linear, Multiple Linear Regression, Polynomial Regression using Python.

Logistic Regression:

Evaluation Metrics for Classification Models:

Naive Bayes

Decision Trees

Case Study: A Case Study on Decision Tree using Python

Random Forest

Ensemble Methods in Tree Based Models

Boosting: AdaBoost, Gradient Boosting

Case Study: Ensemble Methods – Random Forest Techniques using Python Distance Based Algorithm

Case Study: A Case Study on k-NN using Python Support Vector Machines

Case Study: A Case Study on SVM using Python UNSUPERVISED LEARNING

• Why Unsupervised Learning
• How it Different from Supervised Learning
• The Challenges of Unsupervised Learning

Principal Components Analysis

Case Study: A Case Study on PCA using Python K-Means Clustering

Hierarchical Clustering

Case Study: A Case Study on Clustering using Python RECOMENDATION SYSTEMS

Case Study: Movie Recommendation System using Python MODULE 3 :DEEP LEARNING

Introduction to Neural Network

Building Deep learning Environment

Tensorflow Basics

Activation Functions

Training Neural Network for MNIST dataset

Exploring the MNIST dataset

Classifying Images with Convolutional Neural Networks(CNN)

Introduction to Recurrent Neural Networks(RNN)

Sequence-to-Sequence Models for Building Chatbot Hand Written Digits and letters Classification Using CNN

Your Instructors

admin@academe

0/5
18 Courses
0 Reviews
0 Students
See more

Want to receive push notifications for all major on-site activities?

✕

Don't have an account yet? Sign up for free

No apps configured. Please contact your administrator.
No apps configured. Please contact your administrator.