Machine Learning Training Course

Machine Learning Course Overview

This comprehensive Machine Learning Course will turn you into an expert in developing algorithms using Python and R. Learn to utilize key libraries such as Numpy, Pandas, SciPy, Scikit-learn, and Matplotlib to create efficient machine learning models. The course covers essential techniques including Linear Regression, Logistic Regression, Decision Trees, Random Forest, and more, all through practical, real-world projects.

In addition, we offer Tableau and SQL classes to enhance your data visualization skills, ranging from basic to advanced levels, with hands-on project assignments included.

Training Options for Machine Learning Course

Choose between live instructor-led sessions or self-paced recorded videos. For personalized instruction, opt for a one-on-one session with a selected trainer, providing flexible timing and additional support such as interview preparation and resume guidance.

Advantages

The Machine Learning market is projected to reach USD $10 billion, with high demand in sectors like medicine, research, and automotive. Machine Learning engineers enjoy lucrative salaries, with opportunities at top MNCs like Amazon, Google, and Microsoft.

Why Choose This Course?

Machine Learning, a subset of AI, helps businesses make data-driven decisions, from predicting user behavior on top websites to advancing medical research and developing autonomous vehicles. Equip yourself with the latest skills to build powerful algorithms using R and Python.

Eligibility

This course is ideal for business intelligence associates, software developers, data analysts, and project managers, as well as beginners with basic math and statistics knowledge.

Our curriculum includes Python programming, statistics, math, and R programming, ensuring you are well-prepared for a career in Machine Learning.

Welcome to the Machine Learning Course! Gain the knowledge to enable computers to learn from experience without explicit programming, mastering best practices and applications in various domains.

Module 1: Introduction to Data Analytics and Python

Objectives:

• Understand fundamental concepts of data analytics and machine learning.
• Learn the basics of Python programming and its role in data science.

Topics:

• Introduction to Data Analytics and Business Intelligence
• Overview of Python for Data Science
• Basic Python syntax and data types
• Python libraries: NumPy, Pandas, Matplotlib
• Installing Python and setting up the environment (Anaconda, Jupyter Notebooks)
• Introduction to Integrated Development Environments (IDEs) for Python

Module 2: Python Programming for Data Science

Objectives:

• Gain proficiency in Python programming for data manipulation and analysis.

Topics:

• Variables, Scalars, and Data Types
• Lists, Tuples, and Dictionaries
• Control Flow: Conditionals and Loops
• Functions and Modules
• Error Handling and Debugging
• Working with Python libraries: NumPy, Pandas, and Matplotlib
• Introduction to Seaborn for data visualization

Module 3: Data Manipulation with Pandas

Objectives:

• Master data manipulation techniques using Pandas.

Topics:

• Introduction to Pandas DataFrames
• Data Cleaning and Transformation
• Data Aggregation and Grouping
• Merging, Joining, and Concatenating DataFrames
• Handling Missing Data
• Applying Functions and Lambda Expressions

Module 4: Data Visualization with Matplotlib and Seaborn

Objectives:

• Learn how to visualize data effectively using Matplotlib and Seaborn.

Topics:

• Creating Basic Plots with Matplotlib: Line, Bar, Histogram
• Customizing Plots: Labels, Legends, and Annotations
• Advanced Visualization Techniques with Seaborn: Boxplots, Violin Plots, Pair Plots
• Creating Interactive Visualizations with Plotly

Module 5: Introduction to Machine Learning

Objectives:

• Understand the fundamentals of machine learning and its algorithms.

Topics:

• Overview of Machine Learning: Supervised vs. Unsupervised Learning
• Introduction to Scikit-Learn
• Data Preprocessing: Scaling, Encoding, and Splitting Data
• Evaluating Model Performance: Accuracy, Precision, Recall, F1 Score

Module 6: Supervised Learning Algorithms

Objectives:

• Learn and implement various supervised learning algorithms.

Topics:

• Linear Regression and its Applications
• Logistic Regression and Classification
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• k-Nearest Neighbors (k-NN)
• Model Selection and Hyperparameter Tuning

Module 7: Unsupervised Learning Algorithms

Objectives:

• Understand and apply unsupervised learning techniques.

Topics:

• Clustering Algorithms: k-Means, Hierarchical Clustering
• Principal Component Analysis (PCA)
• Association Rule Learning
• Dimensionality Reduction Techniques

Module 8: Advanced Machine Learning Techniques

Objectives:

• Explore advanced techniques and applications in machine learning.

Topics:

• Ensemble Learning: Bagging, Boosting, and Stacking
• Introduction to Neural Networks and Deep Learning
• Model Deployment and Performance Monitoring
• Introduction to Natural Language Processing (NLP)
• Time Series Analysis and Forecasting

Module 9: Data Visualization with Tableau

Objectives:

• Learn how to use Tableau for interactive data visualization and dashboard creation.

Topics:

• Introduction to Tableau and its Interface
• Connecting to Data Sources
• Creating Basic Charts and Visualizations
• Building Interactive Dashboards
• Advanced Tableau Features: Calculated Fields, Parameter Controls

Module 10: Project Work and Case Studies

Objectives:

• Apply learned concepts and techniques to real-world projects.

Topics:

• Case Studies in Machine Learning
• Developing and Presenting a Machine Learning Project
• End-to-End Project Implementation: Data Collection, Processing, Modeling, and Visualization
• Final Project Presentation and Review