Artificial intelligence
Machine Learning and Deep Learning Training
Start from scratch and learn Machine Learning and Deep Learning with Python projects. Learn how to use predictive analytics, image classification, and text analysis to build and use smart AI models in the real world.
5/5(4,890 Reviews)
Level
Advanced
Duration
8 weeks
What is Machine Learning and Deep Learning Training?
This training gives a full introduction to Machine Learning and Deep Learning, including important algorithms, neural networks, and how they are used in the real world. Students will learn how to use Python, ML libraries, and deep learning frameworks like TensorFlow and Keras by working on real-world projects like text analysis, image classification, and predictive analytics. At the end of the course, students will know how to build, test, and use smart models to solve problems in the real world.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks
The candidate will get access to the presentation taken-up during the session.
Notes
Consolidated notes in word document.
Assignments
Assignments for every module will be covered.
MCQs
MCQs for every module covered in the session would be provided.
Video Recording
Daily Session would be recorded and shared to the candidate.
Dumps for Certification
We will provide dumps for the certification exam, which will help you to prepare for the exam.
Updated Content
We provide Generative AI Driven content By experts.
Projects
3 Live Projects will be provided for practice.
Machine Learning and Deep Learning Training Course Curriculum
.
Introduction to Machine Learning
Section 1
This unit covers the basics of Machine Learning, including what it is, the different types (supervised, unsupervised, and reinforcement learning), and how it is used in the real world. Students will also learn why data preprocessing is important and what basic evaluation metrics like accuracy, precision, recall, and F1-score are.
Overview of Machine Learning
Data Preprocessing
Evaluation Metrics
.
Python for Machine Learning
Section 2
Learn the fundamentals of AI, ML, and how they have evolved through practical applications.
Python basics: Data types, loops, functions, and libraries
NumPy for numerical computing
Pandas for data manipulation
Matplotlib and Seaborn for data visualization
Jupyter Notebook and environment setup
.
Data Preprocessing and Feature Engineering
Section 3
Learn Python programming and the necessary libraries to implement machine learning successfully.
Data cleaning and handling missing values
Encoding categorical data
Feature scaling and normalization
Feature selection and dimensionality reduction (PCA, LDA)
Outlier detection and data transformation
.
Supervised Learning Algorithms
Section 4
Examine potent algorithms for classification and predictive modeling applications.
Linear Regression and Polynomial Regression
Logistic Regression
Decision Trees and Random Forests
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Naïve Bayes Classifier
Model evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1-score
.
Unsupervised Learning Algorithms
Section 5
Learn how to find hidden groupings and patterns in unlabeled data.
Clustering: K-Means, Hierarchical, DBSCAN
Dimensionality Reduction: PCA, t-SNE
Association Rule Learning (Apriori, Eclat)
Anomaly Detection techniques
.
Ensemble Learning and Model Optimization
Section 6
Enhance model performance by using ensemble methods and fine-tuning strategies.
Bagging, Boosting, and Stacking
AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost
Hyperparameter tuning (Grid Search, Random Search)
Cross-validation techniques
.
Introduction to Deep Learning
Section 7
Learn the foundations of deep learning architectures and neural networks.
What is Deep Learning and Neural Networks
Biological vs Artificial Neural Networks
Activation Functions (ReLU, Sigmoid, Tanh, Softmax)
Loss functions and Optimization (Gradient Descent, Adam, RMSProp)
Backpropagation algorithm
.
Neural Network Implementation
Section 8
Use the TensorFlow and Keras frameworks to create and train artificial neural networks.
Building ANN models with TensorFlow and Keras
Feedforward and Backpropagation in practice
Weight initialization and dropout regularization
Evaluation metrics for neural networks
.
Convolutional Neural Networks (CNN)
Section 9
Discover CNN architectures and transfer learning for image-based deep learning.
Fundamentals of CNN architecture
Convolution, Pooling, and Fully Connected layers
Image classification and object detection
Implementing CNNs using Keras/TensorFlow
Transfer learning with pre-trained models (VGG, ResNet, Inception)
.
Recurrent Neural Networks (RNN) and LSTMs
Section 10
RNNs and LSTMs are used for time-series prediction and master sequence modeling.
Understanding sequential data
RNN architecture and limitations
Long Short-Term Memory (LSTM) and GRU networks
Text processing and sentiment analysis
Time series prediction
.
Natural Language Processing (NLP)
Section 11
Utilize state-of-the-art NLP models and techniques to analyze and process textual data.
Text preprocessing: Tokenization, Lemmatization, Stopword removal
Bag of Words, TF-IDF, and Word2Vec
Sentiment analysis and text classification
Introduction to Transformer models (BERT, GPT)
.
Reinforcement Learning
Section 12
Recognize how agents interact with their surroundings to determine the best course of action.
Core concepts: Agents, Environment, Actions, and Rewards
Q-learning and Deep Q-Networks (DQN)
Applications: Game AI, Robotics, and Autonomous systems
.
Model Deployment and Production
Section 13
Easily integrate machine learning models into actual production settings.
Saving and loading models
Building ML APIs using Flask or FastAPI
Deployment on cloud platforms (AWS, GCP, Azure)
Model monitoring and versioning
.
Capstone Project
Section 14
Use your end-to-end ML and DL abilities on a practical project to demonstrate your proficiency.
End-to-end ML/DL project (from data preprocessing to deployment)
Real-world dataset implementation
Presentation and documentation
Key Projects
Machine Learning and Deep Learning Training
Predictive Analytics with Machine Learning
For this project, students will create a predictive model to guess things like house prices, stock market trends, or how many customers will leave. The project includes gathering and cleaning data, dealing with missing values, creating features, and using machine learning algorithms like Linear Regression, Logistic Regression, Decision Trees, and Random Forest. Participants will use metrics like accuracy, precision, recall, and F1-score to judge how well the model works. At the end of this project, students will have a working predictive model that they can look at and study to learn more about how things work in the real world.
Image Classification using Convolutional Neural Networks (CNN)
The main goal of this project is to make a deep learning model that can sort images into different groups, like animals, handwritten numbers, or things. Using TensorFlow or Keras, students will build Convolutional Neural Networks (CNN) that use techniques like pooling, dropout, and activation functions. To make the model more general, data augmentation will be used. The result will be a trained CNN that can correctly sort images, which shows how powerful deep learning can be for computer vision tasks.
Sentiment Analysis or Text Generation using RNN/LSTM
In this project, learners will create a Recurrent Neural Network (RNN) or Long Short-Term Memory (LSTM) model to process and analyze sequential text data. The project can involve sentiment analysis to determine whether text is positive, negative, or neutral, or text generation to produce coherent sequences based on a given dataset. Techniques such as tokenization, embedding layers, and sequence modeling will be applied using Python NLP libraries. By the end of the project, participants will have a model that can analyze sentiments or generate text, showcasing practical applications of deep learning in natural language processing.
Machine Learning and Deep Learning Training – Associate Training Program
Category
Associate
Exam Name:
Machine Learning and Deep Learning Training – Associate
Exam Code:
N.A.
Exam Duration:
N.A.
Exam Format:
N.A.
Passing Score:
N.A.
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Frequently Asked Questions (FAQs)
What is the difference between Machine Learning and Deep Learning?
Deep Learning is a subset of machine learning that uses multi-layered neural networks for complex data, such as text or images, whereas machine learning focuses on algorithms that learn from data to make predictions.
Who can enroll in the Machine Learning and Deep Learning Training at Tech Pratham?
Anyone interested in data science or with a programming background is welcome to join. Tech Pratham's course is intended for students, IT professionals, and those looking to change careers.
Do I need prior coding knowledge before joining?
Although it's not required, having a basic understanding of programming helps. Before delving into the Machine Learning and Deep Learning modules, Tech Pratham starts with the basics of Python.
What tools and frameworks are covered in the course?
As part of Tech Pratham's hands-on curriculum, you will investigate TensorFlow, Keras, Scikit-learn, PyTorch, Pandas, and Matplotlib.
How does Tech Pratham’s training differ from online tutorials?
Tech Pratham is different from other tutorials because it offers structured learning, live mentorship, guided projects, and help with finding a job to make sure you learn real-world skills.
Can I pursue this training while working full-time?
Yes, Tech Pratham provides working professionals with flexible learning options, such as online and weekend batches.
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