Day to Day

1. Week 1
    1. Numpy. ✅
    2. Pandas 1. ✅
    3. Pandas 2.✅

2. Week 2
    1. Numpy Extras. ✅
    2. Pandas Extras. ✅
    3. Introduction to the Visualization of Data. ✅

3. Week 3
    1. Python Libraries for Visualization: Matplotlib and Seaborn. ✅
    2. Introduction to the Linear Algebra. ✅
    3. Introduction to Deep Learning. ✅

4. Week 4
    1. Introduction to Keras and Artificial Neural Networks (ANNs). ✅
    2. Machine Learning Workflow. ✅
    3. Introduction to Multivariate Calculus for Machine Learning. ✅

5. Week 5
    1. Introduction to Computer vision and Visual Big Data with code in Python/Tensorflow/Keras. ✅
    2. Implementation of a Simple Keras Code of the implementation of Multi-Layer Perceptrons. ✅
    3. Implementation of Multi Layer Perceptron from scratch in Python without Keras. ✅

6. Week 6
    1. Theory and Implementation of Convolutional Neural Networks (CNNs) in Keras. ✅
    2. Theory and Implementation of Well Known CNNs for the Computer Vision (AlexNet, VGG16/19, Resnet50, InceptionV3, etc.). ✅
    3. Implementation of Advanced Deep Neural Networks in Keras (DNNs).

7. Week 7
    1. Introduction to Deep Learning for Natural Language Processing (NLP) and Textual Big Data. ✅
    2. Implementation of a Supervised Topic Modeling Classifier in Keras. ✅
    3. Introduction to Probability and Statistics for Machine Learning. ✅

8. Week 8
    1. Implementation of a Computer Vision Garbage Classifier in Keras. ✅
    2. Exploring Convolutions: Introduction to dilated, transposed and separable convolutions for segmentation, xception and efficient nets. ✅
    3. Data Augmentation with ImageDataGenerator in Keras and other some Regularizations with flow_from_directory. ✅

9. Week 9
    1. CNNs are not so black-box: See under the hood with the visualization of the internal Feature Maps within the Convolutional Layers. ✅
    2. Introductin to all the main Probability Mass and Density Functions: Bernoulli, Binomial, Gaussian, etc. ✅
    3. Word Embeddings in detail: Gensim Word2Vec, Training of a new Word Embeddings. ✅
    
10. Week 10 
    1. Theory and Implementation of Recurrent Neural Networks: Long Short Term Memories (LSTM), Gated Recurrent Units (GRU) and Bidirectional LSTM. ✅
    2. Theory and Practice of Time Series Forecasting with Deep Neural Networks. ✅
    2. Regression of Uk Land Registry Dataset with Keras and Deep Learning. ✅
    
11. Week 11
    1. Case Study: Deep Learning for the Classification and Segmentation of Satellite Imagery, Implementation with Python/Tensorflow/Keras. ✅
    2. Case Study: Deep Learning for Sentiment Analysis and Colab Implementation. ✅
    3. Introduction to Machine Learning and Description of a Complex Text Classifier in Python/Tensorflow/Keras. ✅
    
12. Week 12
    1. Installation of Anaconda and use of Conda: creation of virtual envs, use of pip and conda install. ✅
    2. Introduction to Traditional Machine Learning Algorithms: Linear Regression, Logistic Regression and Support Vector Machines. ✅
    3. introduction to Scikit-learn for the Implementation of Machine Learning Models with Scikit-Learn. ✅
    
13. Week 13 
    1. Theory of Decision Trees and Random Forest Models and Implementation with Python/Scikit-Learn. ✅
    2. CAM-GRAD: The Second Algorithm for Visualizing internal Mechanisms of ConvNets highlighting Activation Maps on the input Images. ✅
    3. Introduction to Data Preprocessing, Data Wrangling and Exploratory Data Analysis for Machine Learning. ✅
    
14. Week 14
    1. Filters Visualization: The Third Algorithm for Visualizing internal Kernels of Convolutional Neural Networks. ✅
    2. Extra useful Libraries for Machine Learning in Python: Flask, Pymongo, Pyplot, Django, BeautifulSoap, Dash and Mongo Atlas for Pymongo. ✅
    3. Data Preprocessing Implementation with Scikit-Learn. ✅
    
15. Week 15
    1. Theory of K-Nearest Neighbours (KNNs) and Implementation of KNNs with Scikit-Learn. ✅
    2. Study of Metrics with Scikit-Learn and Model Persistence with Scikit-Learn. ✅
    3. Theory of Semantic and Instance Segmentation, U-NET - Convolutional Autoencoder for Image Segmentation: Implementation of UNET in Keras for Aereal Drones.           
       Images Semantic Segmentation. ✅
       
 16. Week 16
    1. Unsupervised Machine Learning with K-Means and PCA and Unsupervised Deep Learning with Autoencoders: Theory and Implementation in Python, Scikit-Learn.
       and Tensorflow/Keras. ✅
    2. Theory of Gradient Boosting and XGBoost ML Algorithms and Scikit-Learn Implementation. ✅
    3. Case Study: Deep Learning for Vehicle License Plates detection. Implementation of Vehicles License Plates Detection Training and Inference backends in  
       Tensorflow/Keras. ✅
       
 17. Week 17
    1. Theory of Naive Bayes and Implementation in Scikit-Learn. ✅
    2. Introduction to Deep Reinforcement Learning and Markov Decision Process, Implementation in Python of Some Environments with OpenAI Gym. ✅
    3. Deep Q Learning Theory and Implementation of Deep Q Learning for Atari Games. ✅
    
 18. Week 18
    1. Decision Trees and Hyperparameters Tuning with GridSearch and RandomSearch. ✅
    2. Case Study: Network Intrusion Detection, Implementation of a Backend. ✅
    3. Introduction to PyTorch: Tensors, Datasets and Pretrained Models. ✅
    
 19. Week 19
    1. Case Study: Brain Tumor Classifier. ✅
    2. Introduction to Generative Adversarial Networks (GANs). ✅
    3. Case Study: Brain Tumor Segmentation. ✅
    
 20. Week 20
    1. Time-Series Prediction with Deep Learning: Theory and Implementation. ✅
    2. Generative Adversarial Netwokrs for Synthetic Data Generation. ✅
    3. Introduction to Pytorch: Softmax, NN Modules, Training MLP Models and Training ConvNets. ✅
    
 21. Week 21
    1. Time-Series Classification of Smart Devices Accelerometers Data ✅
    2. Generative models GAN for Synthetic Data Generation ✅
    3. Sequence-to-Sequence Models: Encoders-Decoders models for Neural Chatbots and Introduction to Transformers and Bert ✅