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Course Description
The MASTERING DEEP LEARNING is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. Dive into real-world case studies as AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.
Objectives
Audience
Looking to take your career in AI and technology to new heights? Our program is designed to empower professionals of all backgrounds, whether you're a fresher, software engineer, data scientist/analyst, business analyst or tech professional involved in the field of Data. With our program's comprehensive curriculum, you'll master the art of deep learning, build critical skills, and gain a competitive edge in the industry. By enrolling in our training program, you'll open doors to limitless opportunities in the dynamic world of AI and technology. Get ready to become a trailblazer in deep learning with the skills and knowledge you'll gain through our program.
Prerequisites
Anyone can attend the course.
Content
Unit-1: Python Programming
Chapter I: - Introduction to Python
a.) Introduction
b.) Features
c.) Advantages
d.) Applications
e.) IDE
Chapter II: - Basics of Python
a.) Tokens
b.) Variables
c.) Data Types
d.) Escape Sequence
e.) Comments
Chapter III: - Control Flow Statements
a.) Selection Flow
b.) Loop Flow
c.) Sequential Flow
Chapter IV: - Python Data Structures
a.) Indexing
b.) Slicing
c.) Strings
d.) Lists
e.) Tuples
f.) Sets
g.) Dictionary
Chapter V: - Functions
a.) User-Defined Functions
b.) Built-In Functions
c.) Variable Scope
d.) Match-Case Functions
e.) Lambda Functions
f.) Recursion Functions
g.) Modules
Chapter VI: - Object Oriented Programming
a.) OOP Fundamentals
b.) Access Modifiers
c.) Constructors
d.) Encapsulation
e.) Inheritance
f.) Polymorphism
g.) Abstraction
Chapter VII: - File Handling
a.) Introduction
b.) Access Modes
c.) File Methods
d.) File Operations
Chapter VIII: - Errors and Exceptions
a.) Errors
b.) Exceptions
c.) Built-in Exceptions
d.) User-Defined Exceptions
e.) Exception Handling
f.) Logging
Chapter IX: - Miscellaneous Concepts
a.) Iterators
b.) Generators
c.) Decorators
d.) Closures
e.) Getters and Setters
f.) Output Formatting
Unit-2: Data Visualization
Chapter I: - Data Visualization Foundation
a.) Introduction
b.) Data Visualization Lifecycle
c.) Steps of Data Visualization
Chapter II: - NumPy
a.) Introduction
b.) Datatypes
c.) Attributes
d.) Numerical Ranges
e.) Array Manipulation
f.) Broadcasting
g.) View & Copy
h.) Indexing & Slicing
i.) Linear Algebra
j.) Sorting
Chapter III : - Pandas
a.) Introduction
b.) Data Structures
c.) Re-Shaping Data
d.) Grouping Data
e.) Subset Observations
f.) Subset Variables
g.) Logic Operations
h.) Summarization
i.) Join & Merge
j.) Missing & Tidy Data
Chapter IV: - Matplotlib
a.) Introduction
b.) Functions
c.) Colormaps
d.) Axes Class
e.) Subplots
f.) Plots
Chapter V: - Seaborn
a.) Introduction
b.) Relplots
c.) Distplots
d.) Catplots
Unit-3: Statistics
Chapter I: - Statistics Foundation
a.) Introduction
b.) Population & Sampling
c.) Sampling Methods
d.) Types of Data
e.) Level of Measurement
f.) Types of Variables
Chapter II: - Data Cleansing
a.) Introduction
b.) Dirty Vs Clean Data
c.) Features of Clean Data
d.) Data Cleansing Process
e.) Data Validation
f.) Data Screening
g.) Outliers
Chapter III: - Central Tendency
a.) Introduction
b.) Mean
c.) Median
d.) Mode
Chapter IV: - Variability
a.) Introduction
b.) Range
c.) Interquartile Range
d.) Standard Deviation
e.) Variance
Chapter V: - Frequency Distribution
a.) Introduction
b.) Skewness
c.) Kurtosis
d.) Types of FD
e.) Grouped FD
f.) Ungrouped FD
g.) Relative FD
h.) Cumulative FD
i.) Relative Cumulative FD
Chapter VI: - Probability Distribution
a.) Introduction
b.) Types of PD
c.) Discrete PD
d.) Types of Discrete PD
e.) Continuous PD
f.) Types of Continuous PD
g.) Central Limit Theorem
Chapter VII: - Estimations
a.) Introduction
b.) Point Estimates
c.) Interval Estimates
d.) Confidence Intervals
e.) Confidence Level
f.) Margin of Error
g.) Standard Error
h.) Z-score Vs T-score
i.) Effect Size
Chapter VIII: - Statistical Tests
a.) Hypothesis Testing
b.) Parametric Tests
c.) Non-Parametric Tests
d.) Correlation
e.) Correlation Coefficient
f.) Causation
Unit-4: Machine Learning
Chapter I: - ML Introduction
a.) Introduction
b.) ML Lifecycle
c.) Types of ML
d.) Supervised Learning
e.) Unsupervised Learning
f.) Reinforced Learning
Chapter II: - Regression Analysis
a.) Introduction
b.) Linear Regression
c.) Multiple Regression
d.) Logistic Regression
e.) Polynomial Regression
f.) Non-Linear Regression
g.) Lasso Regression
h.) Ridge Regression
Chapter III: - ML Key Concepts
a.) Training Data
b.) Testing Data
c.) Validate Data
d.) Hyperparameter Tuning
e.) Variables
f.) Encoding
g.) Bias and Variance
Chapter IV: - ML Monitoring
a.) Precision
b.) Accuracy
c.) Recall
d.) F1 Score
e.) ROC and AUC
f.) Confusion Matrix
Chapter V: - ML Algorithms
a.) K-Means Clustering
b.) Naïve Bayes Classifier
c.) K Nearest Neighbour
d.) Decision Tree
e.) Random Forest
f.) Support Vector Machine
g.) Principal Component Analysis
h.) Apriori Algorithm
Unit-5: Deep Learning
Chapter I: - Deep Learning Foundation
a.) Introduction
b.) BNN
c.) Perceptron
d.) Propagation
e.) Cost Function
f.) Gradient Descent
Chapter II: - Artificial Neural Network
a.) Introduction
b.) Neurons
c.) Neuron Architecture
d.) Activation Functions
e.) Neural Networks
Chapter III: - Convolutional Neural Network
a.) Introduction
b.) CNN Architecture
c.) Input Layer
d.) Image Matrix
e.) Convolutional Layer
f.) Pooling
g.) Output Layer
Chapter IV: - Recurrent Neural Network
a.) Introduction
b.) Types of RNN
c.) Natural Language Toolkit (NLTK)
d.) Long Short-Term Memory (LSTM)
e.) Gated Recurrent Unit Networks (GRU)
Chapter V: - Generative Learning
a.) Introduction
b.) Autoencoders
c.) GANS
d.) Ensemble Learning
Chapter VI: - Deep Learning Framework
a.) Introduction
b.) TensorFlow
c.) Keras
d.) PyTorch
What does Deep Learning do?
Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions. You can use deep learning methods to automate tasks that typically require human intelligence, such as describing images or transcribing a sound file into text.
What are the domains where deep learning work?
Deep learning applications to bring us self-driving cars and virtual assistants like Alexa, Siri, and Google Assistant. But today, these creations are part of our everyday life. Deep Learning continues to fascinate us with its endless possibilities such as fraud detection and pixel restoration. Deep learning is an ever-growing industry, clearly and power ahead your career. Some of the common domains where deep learning may work include.
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- Self-Driving Cars
- Fraud Detection
- Virtual Assistance
- Visual Recognition
- Healthcare
- Colourisation of Images
- Automatic Machine Translation
- Automatic Handwriting Generation
- Pixel Restoration
- Photo Descriptions
- Election Predictions
Top Job Roles of Deep Learning
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- Data Scientist
- Data Engineer
- NLP Engineer
- AI Solution Architect
- Deep Learning Engineer
- Automation Engineer
Salary of a Deep Learning Engineer in India.
According to recent surveys and reports, Deep Learning Engineer salary in India ranges between ₹ 3.0 Lakhs to ₹ 24.0 Lakhs with an average annual salary of ₹ 8.0 Lakhs. The starting pay for deep learning engineers in India can range anywhere between ₹3–15 LPA. Individuals who fall on the higher end of the salary scale may have advanced qualifications, prior work experience, or may work for top players in the industry. The salary may vary depending on factors such as years of experience, skills, location, and the organization you work for. Some startups and large technology firms offer higher salaries to attract top talent for critical roles in AI technologies.
Is it worth to pursue a career in Deep Learning?
Where machine learning algorithms generally need human correction when they get something wrong, deep learning algorithms can improve their outcomes through repetition, without human intervention. A machine learning algorithm can learn from relatively small sets of data, but a deep learning algorithm requires big data sets that might include diverse and unstructured data. Think of deep learning as an evolution of machine learning. Deep learning is a machine learning technique that layer’s algorithms and computing units—or neurons—into what is called an artificial neural network. These deep neural networks take inspiration from the structure of the human brain. Data passes through this web of interconnected algorithms in a non-linear fashion, much like how our brains process information.