What will you get?
- Internship Certificate from Techimax
- Training Certificate from SkillLauncher
- Hands on experience
- Extended Mentorship
Curriculum for this course
90 Lessons
30 Days
Live Classes Recordings
24 Lessons
- 22 May 2020
- 11 June 2020
- 10 June 2020
- 09 June 2020
- 08 June 2020
- 03 June 2020
- 30 May 2020
- 29 May 2020
- 28 May 2020
- 27 May 2020
- 26 May 2020
- 23 May 2020
- 06 May 2020 | Python Basics
- 21 May 2020
- 18 May 2020 & 19 May 2020 | Assignment
- 16 May 2020
- 15 May 2020
- 14 May 2020
- 13 May 2020
- 12 May 2020
- 11 May 2020
- 09 May 2020
- 08 May 2020
- 07 May 2020
Module-1
14 Lessons
- Introduction to Artificial Intelligence
- Types of Artificial Intelligence
- Subsets of Artificial Intelligence
- Heuristic Search Techniques
- A star algorithm
- What is Machine Learning
- Applications of Machine Learning
- Simple two class problem in Machine Learning
- Classification and Regression
- Clustering
- Supervised and Unsupervised Learning
- Reinforcement Learning
- Classifiers
- Clustering Algorithms
Module-2
10 Lessons
- What is Python and its applications
- Python variables, input and output
- Swapping two variables in Python
- For loops and if conditions
- Lists and Tuples
- Sets in Python
- Dictionaries in Python
- Object Oriented Programming
- Math library in Python
- Iterators, itertools and collections
Module-3
4 Lessons
- Introduction to NumPy
- Introduction to Pandas
- Visualization of Data
- Introduction to scikit-learn
Module-4
16 Lessons
- Artificial Neural Networks
- Implementation of Artificial Neural Networks
- Backpropagation in ANNs
- Activation Functions
- Loss Functions
- Batches and Mini-Batches
- Optimizers
- Regularization Techniques
- Initialization of weights and biases
- Machine Learning Frameworks
- Introduction to Google Colaboratory
- XOR Problem in NumPy
- Titanic Classification in Numpy
- Titanic Classification in TensorFlow
- Titanic Classification in PyTorch
- Titanic Classification in Keras
Module-5
13 Lessons
- What is Convolution
- Examples of Filters
- How do CNNs work
- Difference between CNNs and ANNs
- Transpose Convolutions
- Applications of CNNs
- Implementation of CNNs in TensorFlow
- Implemenation of CNNs in PyTorch
- Implementation of CNNs in Keras
- Popular CNN Architectures
- Optimizing CNNs
- Implementing Optimization techniques in Keraas
- Optimizing CNNs in PyTorch
Module-6
5 Lessons
- Introduction to RNNs
- Different Types of RNNs
- Brief Introduction to GRUs and LSTMs
- Preprocessing of sentences
- Implementation of a sequence model in Keras
Module-7
4 Lessons
- Introduction to Autoencoders
- Autoencoders in PyTorch
- Introduction to GANs
- GANs in PyTorch
Requirements
- No Pre-Requisites
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Description
Our Data Ninja course is a well-tailored program, that is compatible with every student's curriculum. Which will teach you the fundamentals of Python (Programming principles, Matrices, Data frames and Visualization), AI/ML and DL. Machine learning is the biggest asset of this program which will offer you a detailed insight of Data processing, Regression, Classification, Clustering, Dimensionality reduction and many more...
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