Unlock the Future of Technology with ENCODE-IT’s Machine Learning Course

Machine learning (ML) is transforming industries by enabling computers to learn from data and
make predictions or decisions without being explicitly programmed. With machine learning
applications in everything from healthcare and finance to self-driving cars and AI, the demand for
skilled professionals in this field has never been higher. ENCODE-IT’s Machine Learning course
provides the foundational knowledge and hands-on experience needed to master this
transformative technology. Whether you are looking to enter the world of AI or enhance your data
science skill set, this course will give you the tools to design, build, and deploy machine learning
models.

In this comprehensive course, you will learn core ML concepts, algorithms, and techniques. From
supervised and unsupervised learning to deep learning and reinforcement learning, you will cover
the breadth of ML topics that are driving today’s advancements in artificial intelligence.

Course Overview
The Machine Learning course at ENCODE-IT covers both theoretical aspects and practical
applications of machine learning. Designed for beginners and experienced professionals alike, this
course focuses on building strong foundations in data analysis, modeling, and evaluation
techniques. You'll learn to implement popular machine learning algorithms such as linear regression,
decision trees, and neural networks, using Python and libraries like scikit-learn, TensorFlow, and
Keras. Throughout the course, you’ll work on real-world projects, gaining practical experience in
designing machine learning models and solving complex problems.

By the end of the course, you will have the skills to design, train, and deploy machine learning
models, as well as evaluate their performance and optimize them for real-world applications.

Salary Scale in India
Machine learning professionals are in high demand across industries. Entry-level machine learning
engineers in India can expect a salary range of ₹6 lakh to ₹12 lakh annually. With experience (2-5
years), the salary can increase to ₹12 lakh to ₹20 lakh per year, depending on the company and
location. Senior machine learning engineers or specialists can earn ₹20 lakh to ₹35 lakh or more
annually. Machine learning is one of the fastest-growing fields in tech, making it an excellent career
choice with high earning potential.

Placement Assistance & Certification in India
ENCODE-IT’s Machine Learning course offers placement assistance to help you transition from
learning to career success. We provide resume building, mock interviews, and job placement
support to help you secure your desired role in the tech industry. Upon successful completion of the
course, you’ll receive an ENCODE-IT Certificate of Completion, which is highly regarded by
employers. With our placement support and industry connections, we aim to help you land a
rewarding career in machine learning.

Course Curriculum

1. Introduction to Machine Learning
o What is Machine Learning? History and Evolution
o Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
o Machine Learning vs Traditional Programming
o Overview of ML Algorithms: Classification, Regression, Clustering, and
Dimensionality Reduction
o Introduction to the Python Programming Language for Machine Learning
o Setting up Your Development Environment (Python, Jupyter, Anaconda)

2. Data Preprocessing and Feature Engineering
o Importance of Data Cleaning in ML
o Handling Missing Data and Outliers
o Normalization and Standardization of Data
o Feature Selection and Dimensionality Reduction (PCA, LDA)
o Handling Categorical Data: One-Hot Encoding, Label Encoding
o Feature Engineering: Creating New Features for Better Model Performance

3. Supervised Learning Algorithms
o Overview of Supervised Learning: Concepts and Applications
o Linear Regression: Simple and Multiple Regression Models
o Logistic Regression: Binary Classification and Multiclass Classification
o Decision Trees and Random Forests: Creating and Tuning Decision Trees
o Support Vector Machines (SVM): Hyperplanes and Kernel Functions
o K-Nearest Neighbors (KNN): Classification and Regression with KNN
o Model Evaluation: Cross-Validation, Confusion Matrix, ROC Curve

4. Unsupervised Learning Algorithms
o Overview of Unsupervised Learning and Applications
o Clustering: K-Means, DBSCAN, Agglomerative Clustering
o Principal Component Analysis (PCA): Reducing Dimensionality for Clustering
o Anomaly Detection: Identifying Outliers in Data
o Hierarchical Clustering and Dendrograms
o Association Rule Mining: Market Basket Analysis

5. Neural Networks and Deep Learning
o Introduction to Neural Networks: Neurons, Layers, and Activation Functions
o Training Neural Networks: Gradient Descent, Backpropagation
o Building Neural Networks with Keras and TensorFlow
o Convolutional Neural Networks (CNN): Image Classification
o Recurrent Neural Networks (RNN): Time Series and Sequence Data
o Transfer Learning and Pretrained Models
o Advanced Topics in Deep Learning: Generative Adversarial Networks (GANs),
Autoencoders

6. Model Optimization and Hyperparameter Tuning
o Hyperparameter Tuning: Grid Search, Random Search
o Overfitting and Underfitting: Bias-Variance Tradeoff
o Cross-Validation Techniques: K-Fold, Leave-One-Out
o Regularization Techniques: L1, L2, Dropout
o Model Selection: Choosing the Right Algorithm for the Problem

7. Reinforcement Learning
o Introduction to Reinforcement Learning: Agents, States, and Rewards
o Markov Decision Process (MDP)
o Value Iteration and Q-Learning
o Policy Gradient Methods
o Deep Reinforcement Learning with Neural Networks
o Applications of Reinforcement Learning: Robotics, Game AI, Finance

8. Natural Language Processing (NLP) with Machine Learning
o Introduction to NLP: Text Preprocessing and Tokenization
o Bag-of-Words Model and TF-IDF
o Sentiment Analysis and Text Classification
o Named Entity Recognition (NER) and Part-of-Speech Tagging
o Building Chatbots and Language Models
o Word Embeddings: Word2Vec, GloVe, FastText
o Recurrent Neural Networks (RNN) for NLP

9. Model Deployment and Production

o Introduction to Model Deployment: Moving from Development to Production
o Building REST APIs for ML Models with Flask and FastAPI
o Deploying Models to Cloud Platforms: AWS, GCP, Azure
o Model Versioning and Monitoring with MLflow and DVC
o Containerization with Docker for ML Models
o Continuous Integration and Continuous Deployment (CI/CD) for ML Models

10. Real-World Machine Learning Projects
o Predicting House Prices with Regression Models
o Building a Sentiment Analysis Model for Social Media Data
o Customer Segmentation using K-Means Clustering
o Detecting Fraud in Financial Transactions using Anomaly Detection
o Developing a Recommender System for E-Commerce Platforms
o Image Classification with Convolutional Neural Networks (CNNs)

11. Final Project and Certification Exam
o Final Project: Building an End-to-End Machine Learning Application
o Model Development, Evaluation, and Deployment
o Final Exam: Comprehensive Evaluation of Machine Learning Skills
o Certification of Completion from ENCODE-IT and Job Placement Assistance

Key Features
 Tools & Platforms: Python, scikit-learn, TensorFlow, Keras, Flask, AWS, GCP, Docker
 Real-World Projects: Apply machine learning algorithms to solve real business problems and
gain hands-on experience.
 Certification & Placement Support: Receive an ENCODE-IT certificate and job placement
assistance upon completion of the course.
 Expert Instructors: Learn from industry experts with years of experience in machine learning
and AI.
 Career Advancement: Acquire essential machine learning skills for high-demand roles in
data science, AI, and machine learning.

ENCODE-IT’s Machine Learning course provides a deep dive into the world of AI and ML. You will
acquire the skills and practical experience needed to build machine learning models and deploy

them into real-world applications. Enroll today to start your journey into the exciting world of
machine learning and AI!