Machine Learning Certification Training Program

Name: Certified Machine Learning Professional
Brand: Skillfloor
SKU: 40
Rating: 5 (295 reviews)

Overview

Developing expertise in machine learning concepts, such as model selection, data preprocessing, and performance testing, is necessary for those who want to become Certified Machine Learning Associates. Aside from proving one's competence, this certification makes it possible for a person to take advantage of possibilities to actively participate in the development and application of novel algorithms and techniques. In addition, obtaining this certification gives people the abilities and information needed to succeed in a variety of professions in the ever-changing field of machine learning. Certified experts may efficiently discuss the complexity of modern data analysis and predictive modeling by improving their skills in these critical areas, thereby contributing significantly to a variety of sectors.

Students in this program explore ideas including reinforcement teaching, supervised and unsupervised learning, and neural systems as they go into the complex subject of machine learning. Learners get practical experience in creating and executing machine learning models to address challenging challenges in a variety of fields through hands-on tasks and real-world applications. The curriculum also highlights the value of lifelong learning and keeping up with the most recent developments in machine learning techniques and technology. Those who complete the Certified Machine Learning Associate program will be in a good position to pursue rewarding jobs in machine learning, where they may spur innovation and add positively to the quickly changing field of artificial intelligence.

~~₹60,000~~

₹14,900

Skill Level

Advanced

Internship

1-month Unpaid

Live Project

4

Certificate

Yes

Live Training

Yes

Career Assistance

Yes

Expiry Period

Lifetime

Enrol Now

Course Highlights

Introduction to Machine Learning: Understand the basics of how computers can learn from data and make predictions.
Data Preprocessing: Learn how to clean and prepare data for analysis, ensuring accurate results.
Supervised Learning: Explore methods where the model learns from labelled data, making predictions based on past examples.
Unsupervised Learning: Discover techniques for finding patterns and relationships in unlabeled data without predefined outcomes.
Model Evaluation: Understand how to assess the performance of machine learning models to ensure reliability.
Feature Engineering: Gain insights into selecting and transforming features to improve model accuracy.
Regression Analysis: Delve into predicting continuous outcomes, such as sales or prices, using regression techniques.
Classification Algorithms: Learn about categorizing data into groups or classes, like spam or not-spam emails, using classification algorithms.
Clustering Algorithms: Explore methods for grouping similar data points, and uncovering hidden structures within datasets.
Model Deployment and Interpretability: Discover how to deploy machine learning models into real-world applications and interpret their results effectively.

Certification

After you complete the course with Skillfloor, course certification, offer a comprehensive introduction to machine learning using TensorFlow, a popular open-source machine learning framework. This program equips learners with essential skills in building and deploying machine learning models, enabling them to work on real-world projects and enhance their career prospects in the field of artificial intelligence and machine learning. This certification serves as a testament to your foundational knowledge and skills in machine learning concepts, algorithms, and techniques. It validates your ability to preprocess data, build predictive models, and evaluate their performance. With this certification, you showcase your readiness to contribute to machine learning projects and demonstrate your commitment to staying at the forefront of emerging technologies. As a Certified Machine Learning Associate, you not only gain recognition for your expertise but also open doors to entry-level positions in data science, artificial intelligence, and related fields, where you can leverage your skills to make meaningful contributions to data-driven decision-making and innovation.

Top 10 reasons

Career Advancement: Propel your career in data science and AI by becoming a certified machine learning associate, equipped with foundational skills to excel in this rapidly growing field.
Industry Demand: Meet the increasing demand for professionals with machine learning expertise, as businesses leverage data-driven insights to gain competitive advantage and drive innovation.
Foundational Knowledge: Gain a solid understanding of machine learning concepts, algorithms, and techniques, providing a strong foundation for further specialization and career growth.
Practical Application: Apply theoretical concepts through hands-on projects and real-world simulations, honing your skills in developing and deploying machine learning models.
Data Analysis Skills: Develop proficiency in data preprocessing, exploration, and visualization techniques, essential for preparing data for machine learning tasks.
Model Development: Get to know how to create and hone machine learning models with well-known frameworks and libraries like PyTorch, TensorFlow, and Scikit learn
Assessment and Enhancement: Acquire knowledge about techniques for assessing model efficacy and refining hyperparameters to enhance model precision and applicability.
Learn: Gain an understanding of methods for deciphering and elucidating machine learning models, which will increase openness and confidence in artificial intelligence (AI) systems.
Ethical Issues: To guarantee responsible AI deployment, investigate ethical issues and concerns in machine learning, such as bias, fairness, privacy, and responsibility.
Constant Learning: Attend conferences and forums in the field and pursue continual professional development to stay informed of new developments and trends in machine learning.

Syllabus

What is Machine Learning? – Overview of Machine Learning (ML), history, and importance.

Types of Machine Learning – Supervised, Unsupervised, and Reinforcement Learning.

ML Process Flow – Data Collection, Preprocessing, Model Building, Evaluation, and Deployment.

Real-world Applications of Machine Learning – Case studies in various domains (Healthcare, Finance, Marketing, etc.).

Machine Learning Tools and Libraries – Introduction to Scikit-learn, TensorFlow, PyTorch, etc.

Ethics in Machine Learning – Addressing bias, fairness, and interpretability.

Understanding Data Types – Numerical, Categorical, Text, and Time-series data.

Handling Missing Data – Techniques to manage missing data and outliers.

Data Normalization and Standardization – Scaling, normalization, and feature engineering techniques.

Data Visualization – Using Matplotlib, and Seaborn for visualizing datasets.

Exploratory Data Analysis (EDA) – Descriptive statistics, distributions, and identifying trends.

Dimensionality Reduction – PCA, t-SNE, and other dimensionality reduction techniques.

Linear Regression – Concepts, assumptions, and real-world applications.

Logistic Regression – Binary classification and decision boundaries.

Decision Trees and Random Forests – Understanding tree-based models.

Support Vector Machines (SVM) – Classification with margin maximization.

K-Nearest Neighbors (KNN) – Understanding instance-based learning.

Model Evaluation Metrics – Accuracy, Precision, Recall, F1-Score, and ROC-AUC.

Clustering Algorithms – K-Means, Hierarchical Clustering, and DBSCAN.

Association Rule Learning – Apriori and FP-Growth algorithms.

Dimensionality Reduction Techniques Revisited – Feature extraction using PCA and LDA.

Anomaly Detection – Identifying outliers and detecting anomalies in datasets.

Gaussian Mixture Models (GMM) – Probabilistic clustering and applications.

Principal Component Analysis (PCA) – Advanced dimensionality reduction.

Introduction to Neural Networks – Neurons, activation functions, and architecture.

Training Neural Networks – Forward propagation, backpropagation, and gradient descent.

Convolutional Neural Networks (CNNs) – Application in image processing.

Recurrent Neural Networks (RNNs) – Sequential data processing and applications.

Transfer Learning – Pre-trained models and fine-tuning techniques.

Deep Learning Frameworks – TensorFlow and Keras for building deep learning models.

Model Deployment Techniques – Strategies for deploying ML models in production (e.g., Flask, Docker).

Model Serving – Real-time vs batch model serving approaches.

Model Interpretability – SHAP, LIME, and model explanation methods.

Capstone Project Overview – End-to-end machine learning project development.

Collaborative Project – Group project focusing on building and deploying a machine learning solution.

Final Evaluation and Certification – Submission and evaluation of capstone project and final exam.

FAQ

A certified machine learning associate is someone who has completed a recognized certification program in machine learning, demonstrating their ability to apply machine learning techniques to solve real-world problems.

Certification in machine learning validates your skills, enhances your credibility, and increases your job prospects in fields where machine learning is applied, such as data science and artificial intelligence.

To get certified, you typically need to complete specific training courses, gain practical experience with machine learning algorithms and tools, and pass an exam that tests your knowledge of machine learning concepts.

Skills include understanding of machine learning algorithms, proficiency in programming languages like Python or R, knowledge of data preprocessing and feature engineering, and experience with machine learning libraries like scikit-learn or TensorFlow.

With certification, you can pursue roles such as machine learning engineer, data scientist, AI specialist, or research scientist in various industries.

It varies, but typically takes several months to a year of study and practice to get certified.

It can lead to better job prospects, higher salaries, and opportunities for career advancement in the field of machine learning and data science.

Certified Machine Learning Professional

Overview