Certificate in AI-ML

Blended

Fee: ₹12,000

Surendra Prabhakar | Data Science Trainer

Associated Courses

Certificate in AI-ML

The Certificate in AI-ML course equips you with expertise in Artificial Intelligence and Machine Learning, covering Python programming, statistics, exploratory data analysis, machine learning algorithms, and deep learning. Gain hands-on experience in building AI-driven models and applying data-driven insights to real-world problems. Ideal for aspiring AI professionals, this course provides a strong foundation in AI-ML techniques to enhance career opportunities in data science and automation.

Objectives

The Certificate in AI-ML course aims to equip learners with a strong foundation in Artificial Intelligence and Machine Learning, enabling them to develop AI-driven solutions using Python, statistics, and machine learning algorithms. This course covers key AI-ML concepts, including Python programming, statistical analysis, exploratory data analysis (EDA), and machine learning techniques. Learners will gain hands-on experience in supervised and unsupervised learning, deep learning frameworks like TensorFlow, and AI-ML tools such as Scikit-learn and Pandas. By the end of the course, participants will be able to analyze data, build and optimize machine learning models, and apply AI-driven decision-making in real-world scenarios, enhancing their technical expertise and career opportunities.

This course covers key AI-ML concepts, including Python programming, statistical analysis, exploratory data analysis (EDA), and machine learning techniques.

Learners will gain hands-on experience in supervised and unsupervised learning, deep learning frameworks like TensorFlow, and AI-ML tools such as Scikit-learn and Pandas.

What Will You Learn

By the end of the course, participants will be able to analyze data, build and optimize machine learning models, and apply AI-driven decision-making in real-world scenarios, enhancing their technical expertise and career opportunities.

Skills you will gain

Working with AI-ML Libraries (Scikit-learn, Pandas, NumPy)

AI Applications & Real-world Implementation

Deep Learning with TensorFlow & Keras

Machine Learning Model Development

Exploratory Data Analysis (EDA)

Prepare for your career path

A Machine Learning Engineer is responsible for designing and developing machine learning systems, implementing appropriate ML algorithms, conducting experiments, and staying updated with the latest developments in the field. They work with data to create models, perform statistical analysis, and train and retrain systems to optimize performance.

Key Skills to Learn

machine learning algorithms , supervised learning, unsupervised learning , reinforcement learning, Random forest , predictive analytics, Decision Tree, Regression algorithm

As an AI Engineer designs, you develops, and implements artificial intelligence systems and applications that can simulate human intelligence processes through the creation and validation of algorithms, neural networks, and other machine learning techniques. Implement AI solutions that integrate with existing business systems to enhance functionality and user interaction.

Key Skills to Learn

Knowledge of AI , Advanced learning of algorithms, neural networks , machine learning techniques, implementation of AI models

"As a Data Scientist you will be a professional who collects large amounts of data using analytical, statistical, and programmable skills. It is their responsibility to use data to develop solutions tailored to meet the organisation's unique needs. Analyzing large amounts of information to find patterns and solutions Developing prediction systems and machine learning algorithms Presenting results in a clear manner Propose solutions and strategies to tackle business challenges."

Key Skills to Learn

Data mining , Analytics, Big data , statistical methods, Computational stats, Deep learning, NLP and Its Concepts.

Jobs in India

2.1L+

Average Salary

5.5 Lakhs

Job Growth

35.20%

Companies Looking For Machine Learning Engineer, AI Engineer

and many more

Curriculum

Module 1 : AI Introduction and Basics of Python
- Course requirement and Introduction
- Data science and buzzword
- Difference between Data analysis and analytics and DataScience
- AI vs ML vs DL
- Fundamentals of AI
- Trends Accelerating AI
- Natural Intelligence v/a Artificial Intelligence
- Challenges and Risk of using AI
- Examining the components of AI
- Python Introduction
- Installation of Anaconda Navigator
- Jupyter Notebook Interface
- How to Open ,save and Rename a file
- Variables and Data Types
- Loops (Conditional)
- Loops (iterative while)
- Loops(iterative For)
- Python Functions(Built in,Lambda,User defined functions)
- Lists
- Tuples
- Sets
- Dictionaries

Module 2: Statistics for Machine Learning
- Numpy Arrays in Python
- Three Dimensional Arrays Indexing and Slicing
- Matrices
- Pandas in Python
- Importing CSV, Excel Files
- Exporting CSV File
- Pandas Data Frames
- Querying Data in Pandas
- Data Aggregation with Pandas Data Frames
- Concatenating and Appending Data Frames
- Joining Data Frames
- Statistics with Pandas Data Frames
- Handling Missing Data
- Pivot Tables
- Plotting in Pandas

Module 3 : Exploratory Data Analysis
- Data Preprocessing
- Data Preprocessing in Python
- Importing Libraries and Dataset
- Taking Care of Missing Data
- Encoding Categorical Data
- Splitting the Dataset into the Training Set and Test Set
- Feature Scaling

Module 4 : Machine Learning
- Introduction to Machine Learning- Available in Hindi also
- Past, Present, and Future of Machine Learning- Available in Hindi also
- Various Applications of Machine Learning- Available in Hindi also
- Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
- Machine learning forms: Supervised, Unsupervised, and Reinforcement learning
- Learning decision trees
- Learning, its Representations, and Complexity
- Decision Tree, its Representations, its Advantages, Disadvantages, and Applications
- Terminologies and Issues in Learning Decision Trees
- Various Learning Decision Trees Algorithms

Module 5: Deep Learing
- Introduction to deep learning and its applications
- Overview of artificial neural networks (ANNs)
- Basic principles of Backpropagation
- ANN Model for Image Classifiaction
- Introduction to CNNs, architecture and its applications
- CNNs Operation / Mechanism and its Demonstration
- Padding / Strides in CNN and its Practical
- Max Poolling Layers in CNN and its Practical
- Mini Project - Dog vs Cat Image Classification
- Introduction to RNN
- Types of RNN
- Project - RNN Model for text generation using Tensorflow
- Introduction to Long Short-Term Memory (LSTM)
- Language Translation using LSTM

Live Classes

Final Assessment

Instructor

Surendra Prabhakar

Data Science Trainer

Python, EDA, Statistics, Machine Learning, Deep Learning, MySQL,

With four years of experience in the field, the Data Science Trainer has successfully guided over 1,000 students in mastering Data Science, Artificial Intelligence, and Machine Learning. In addition to mentoring students, he specialize in delivering corporate training programs for both government and private organizations. His areas of expertise encompass Python, Machine Learning, Statistics, Deep Learning, DBMS (MySQL), and Power BI.

4.1 2000+ 1000+ Learners

Shareable Certificate

Other Details